To set the context for why I’m going to be talking about the stuff that I’m talking about: I was a researcher for a really long time in the field of quantum optics.

The last seven of those years were spent here at Perimeter, where I was a PSI Fellow and also had a quantum optics research program. In 2020 I left and I joined IBM Quantum. I was there for about four years and I had quite a few different roles, but one of them was that I was leading a quantum software team. So I got to really see how people build software and how they collaborate. That was a really cool experience.

And then the last two years I’ve been building and working in Quantum Salon, which is my consulting business. There I work with quantum tech startups and quantum organizations to help them explain what they do and why it’s important, and also just in general helping the quantum ecosystem understand each other.

In building this business I’ve been using AI a lot to help accelerate different kinds of workflows, to become more efficient and also to increase my capabilities beyond what I can do without AI.

I kind of oscillate between my anti-AI phase and my pro-AI phase, and right now I’m in the pro-AI phase. So those three components, the research, the software, and the AI, all culminated together in the stuff that I want to talk about today.

Why “the third brain”

I’m calling it the third brain. Why is it the third brain? Well, it’s the natural succession to the second brain, which you may have heard about.

The first brain is like your brain. Everyone has a brain.

You may have heard about people talking about the second brain. The second brain is the externalization of the knowledge that’s in your head. You can just put it into a bunch of index cards, which was the Zettelkasten method, or people use tools like Obsidian to build out these kind of almost wiki-like things with lots of connections. Another term for it is a personal knowledge management system, and people have been using these things for decades. I kind of think of that as second brain 1.0, because it was more like a second memory.

But now with AI you can also externalize the processing. So I think of this as second brain 2.0, not just the memory but also the processing. And the thing that I’m highlighting here is that it’s a personal knowledge management system.

So what I’m calling the third brain is now a shared knowledge management system, which also has processing. Why do I not just call it second brain 3.0? Well, third brain sounds cooler. I think it’ll be more viral!

And also I kind of want to have connotations with this idea of a third space, which is this community, collective space where people come together. So that’s the context for the name.

What’s in this talk

In this talk I’m going to spend quite a bit of time on the motivations, so setting up what I think is the bottleneck in where AI is able to help research and where it hasn’t been able to help research, from what I’ve observed so far. Then I will propose what I think is the solution, which is this idea of the third brain, so setting up a shared body of knowledge and techniques, and in particular adopting workflows and having AI assist so that it doesn’t rot or go stale. I don’t know if any of you have ever been involved in building a wiki for the group. It starts off with a lot of energy, you’re making this wiki, but then it just goes stale because nobody really maintains it.

So I’m going to set up the bottleneck, then talk about the solution with what I think is the third brain, and then at the end I’ll introduce some tools. I’ll talk about AI agentic harnesses, GitHub and other tools, which I think are the things that you can use to implement this set of processes.

The bottleneck

I wanted to start with this meme that I like very much. Some of you may have seen it.

This is the public perception of science: oh, I wonder what happens if, challenge accepted, read science, do science, Nobel prize, great.

And then the counterpoint to that is, this is how science is in reality. You might not be able to see the details, but there’s lots of swearing, lots of going back and forth, like “WTF is going on”, “oh my god someone’s done this already”, and it’s kind of crazy and chaotic.

The reason I want to start with that is I really want to acknowledge that I think this is a good reflection of how science happens. And also we all have our very individual ways of doing things, especially I think in theoretical physics, where we’re less about the scientific method and more this creative process of coming up with ideas. It’s rigorous, but still very individual. I kind of want to keep that in mind, because for this talk I’m going to simplify how I think science happens just to be able to communicate some ideas. But I want you to keep in mind that that’s simplified, and in reality I acknowledge that it’s a very individual, chaotic process.

Your first research project

So let’s start with the simplified version. I want you to think back to the first time you did a research project. Probably your adviser gave you a research project to work on. You guys had a meeting, then you went off and worked on it. Probably you met with them every week while continuing to do the work by yourself. Maybe there was a postdoc involved that you were meeting with more regularly, getting some help. Maybe you had to ask another professor something. You roped in another student. But it was this process of everybody working together, meeting occasionally. And probably in your head you had this vague idea that yeah, my supervisor came up with this idea at some stage, and then the work started when they gave me the idea and I started to work on this project.

When you start having your own ideas

Then when you progressed in your career and you had your own ideas which you wanted to give to students, you realized, oh actually, there’s the idea, but then a heck of a lot of work, and almost the hardest work, has to go into turning that idea into something that you can give to a student, which is a well-defined project that they can work on.

From idea to project

So how would this process work? For starters, I think you don’t just have one idea, right? You have a ton of ideas that you have in your head, that you’ve collected through doing research, talking to people and so on, and those ideas kind of sit in the back of your head. There’s this incubation period, so maybe the idea comes back to you every now and then, you think about it, you do some more progress, maybe it just sits in your subconscious and does its magic back there. But stuff is kind of happening, these ideas are mixing around.

And then some external pressure comes along. Maybe it’s, oh my god, the winter school is coming and I have to present a project. Or a student is starting in the group, I have to give them a project, I have to give them some work to do. Or a collaborator is visiting and you’re going to have only a week to come up with something good that you can work on when they go home. Or you’re at a conference having dinner with someone and you only have a little bit of time to work together. So this pressure comes along, somehow magically pulls out one of those ideas and says, this is the idea we have to figure out.

And then there’s this intense period of hashing out this idea. Maybe you spend the whole weekend figuring out what is this research project, and you’ve got all this stuff in your head and it’s messy. Or maybe you and your collaborator lock yourselves up in an office for three days and just hash everything out on the blackboard. But it’s this very intense thing, which is very different to the later collaborative stage. And hopefully at the end you have a well-formulated project and you can give it to your student.

This is not to scale. I think the incubation period can be weeks, months, years, but this intense period of formulating the idea is kind of days.

Where AI has helped, and where it hasn’t

What I’ve kind of seen from talking to researchers is that I think AI has accelerated some aspects of this. It’s helped generate more ideas, like we’re having this interaction with ChatGPT and getting all these different ideas. And what are you going to do with these ideas? And then I think it’s also accelerated this part at the end. I think it’s helped us execute much faster on a project that you’re already working on.

But I think it’s created bottlenecks in this middle section. I think there are a few of them.

One of them is the incubation bottleneck. I think when you have a lot more of these ideas, it’s really hard to just keep them all in your head and have them do things in the background. This is not scientifically proven or anything, it’s just what I at least have experienced myself, and heard of other people talking about, that you’ve got all this stuff happening there and you don’t really know how to process it.

I think there’s a filtering bottleneck too, because these external pressures haven’t really changed. They’ve kind of stayed the same, but now you’ve got more ideas to choose from. How do you pick out which is the one that you should keep working on?

And then I think there’s this intensity bottleneck. If you have ten times more ideas that you have to turn into well-formulated projects, it’s really hard to have ten times more of these intense weekends, or to have these intense periods where a collaborator and you are hashing things out. You kind of don’t have the energy to do all of those things.

Weakly coupled and strongly coupled collaboration

Another way of thinking about it, which is a physics analogy, is that I think of the start and the end as being kind of weakly coupled collaboration. You’re mostly doing stuff as an individual and talking to people occasionally. Because of this, you’re able to work as an individual with the AI to make things faster. But then here in the middle it’s this strongly coupled collaboration.

So I think what we want is to be able to use AI to accelerate all aspects of that research. We want not just more ideas, but then faster processing of ideas into projects that you can then give to someone to execute on.

It turns out that this very strongly coupled collaborative experience is something that software developers have been dealing with for decades. When they’re working on a codebase, that’s a highly messy collaborative thing, and they’ve figured out best practices for how to deal with this kind of collaboration. So that’s kind of to foreshadow what I want to talk about later. I want to pull a lot of these best practices from collaborative software development into the research process.

So what do we want? We want a shared and distributed incubation period, shared and distributed filtering, and shared and distributed refining of the idea into a project. And it would be great if we can use AI to support all of that. That’s what I’m coining here as the third brain, being able to do all of those things in a shared and distributed way.

The third brain

Okay, so that was the motivation. Let’s talk about this third brain.

First we need a shared praxis

Some of you may know Michael Nielsen. Michael Nielsen is most famous for his textbook Nielsen and Chuang, the quantum computing bible. He later went on to work in different fields. He had this book Reinventing Discovery from 2011, which I quite like. In fact he was here at Perimeter for a couple of years, or maybe a shorter period of time, sometime just before he put out this book.

I want to quote stomething that I really liked in the book, this idea of a shared praxis.

He says that a fundamental requirement that must be met if we’re to amplify collective intelligence is that participants must share a body of knowledge and techniques. It’s that body of knowledge and techniques that they use to collaborate. When this shared body exists, we call it a shared praxis, after the word praxis, meaning the practical application of knowledge. And whether a shared praxis is available determines whether collective intelligence can be scaled up, or whether it cannot be.

In this book he talks about the fact that scientists already have this shared praxis. Once you are in science for a while you collectively share knowledge and share techniques. But I think that kind of knowledge is distributed mostly amongst people’s heads. It’s not like any of this is really encoded anywhere, for the most part. So the way I think of it is that externalizing this shared praxis and making it concrete, that’s what I see as this third brain.

Two levels, two halves

We want to break it down into shared knowledge management, and shared workflows and processes. And I want to do it on two levels. I want to do it at the domain level, so that’s the physics, the quantum foundations, math, whatever it is that we’re actually interested in doing science about. And then the meta level, which is more about how we do things. So knowledge about how we do it, and processes about how we implement all of this stuff. I’ll dig into all of these things in detail throughout the talk.

Knowledge management at the domain level

I want to start with the shared knowledge management at the domain level, and, okay, there’s like ten slides of folder setups. Which I think is actually critical and interesting, but if you’re bored you can tune out for a second.

I put the “serving suggestion” thing up the top there because I’m not dictating that this is how you should set up your knowledge management system. I think as a group, when you do this, you need to think about what is the thing that is going to work for you as a group. But I think it helps just seeing a concrete version of how somebody else would do it.

The way that I would do something like this is to split it up into sources of knowledge, syntheses of the stuff that’s in the sources, and then the pipeline, which is the items that we store that help us turn an idea into a project proposal.

The sources are the things that are kind of past tense. They’re fixed. Once a source is in the knowledge management system, you’re not going to be changing it or editing it, and it’s essentially a record of what was said or known at a given time. Then the syntheses are things that we’re actually working with, so they’re dynamic. They change, and they’re what we understand these sources to mean. And then the pipeline is what are we going to be doing in the future, what are we working towards, and that’s also a live and dynamic thing.

The reason that I split it up this way is because if you’re using AI on all of these files, you really have to have this separation to avoid AI eating itself. When you get AI to synthesize something, it can create a document which, if you read it, you don’t find anything wrong with it, because you kind of know which things are 100% true and which things are fuzzy, and you don’t flag it as being wrong. But then if AI starts using that and eating it, it gets really overzealous and implements these things as “this is an absolute must” when you just mentioned something in passing. So I really want to separate between things that are inputs into the AI, these sources, and then these things that are outputs of AI and outputs of your work as well.

Source files

If I was to set up a directory that had these things, I would have my three folders. I’ll talk about the inbox in a sec, but you know, pipeline, sources, syntheses. Then in sources I would have meetings, papers, podcasts, talks, whatever, everything from your group. And this will grow as you create more sources.

Then you’re going to want to define some naming conventions, which sounds really boring, but is very important. I would have a folder for every kind of meeting.

And then inside you have all of the artifacts from that meeting. So maybe some photos from the whiteboard, slides. And because AI is really good at taking text files, in particular markdown files, I would even go as far as transcribing these boards and notes into markdown files and keeping them in that folder as well.

Synthesis files

Then syntheses. I think of these as files that are generated by AI. You can ask it to go through all these papers and find every reference to contextuality and give me a narrative of how ideas about contextuality evolved over time, or something. So this could be where you store your wiki. A wiki would just be a collection of markdown files that connect to each other.

Maybe you make research maps. This was a fun one. Rob, I took all your arXiv abstracts and put them into AI and said, make me a research map of Rob’s research program. And it had this mind map. There was a lot more stuff that I didn’t include, but it was a fun way to visualize how your ideas have changed over time.

And narratives. Like I mentioned before, maybe you can get it to go through papers and give you a history of QBism or something. I have this lightbulb to remind me to say, you can get really creative with this. There are a lot of fun things you could do that you wouldn’t do if you were doing it manually, because it just would be too labor intensive. But if you ask your AI agent to just go through all the sources and create these things, I think you can have new ways of understanding the body of literature, or the meeting that you had. Maybe make a connection between what was discussed in this meeting and this paper from however long ago.

Pipeline files

So that’s the synthesis files, and then I’ll talk more about the pipeline, but essentially the pipeline would be a template, which I’ll talk about later, and then instantiations of that template for various ideas that you may have had. Like, oh, it’d be great to have a project on that.

Conventions files

And then I think you need an additional folder, which is conventions. This is because every time you bring in your AI collaborator, it’s permanently a new group member. It doesn’t have a memory of all of the stuff that you’ve been talking about. So you need to give it a bunch of context every time you create a session with the AI. You want to include anything you would tell a new student before they could be useful. And it’ll be read by the AI agent every time it gets into this third brain.

On the domain side it’ll be things like, what are the notations that you use in your research group, what’s a glossary of things. Different words might have different meanings in the literature, so you want to agree as a group how you define this particular thing when you use it. What is your idea of a rigorous argument. All of these things would be encoded, and these are to be read by the AI but also read by new people who are maybe joining the project.

And then on the meta side, things like file conventions in your folders, how do you name things, how do you give attribution, and so on. And then there’s this file AGENTS.md. This is kind of a standard file now across Claude and Codex, which is the thing that AI knows to read when it first gets into a directory. I started putting it here because conceptually it’s a meta convention, but really the standard is to just have it at the root of your directory, so it knows where to find it. So you kind of want to set up your files like this.

AGENTS.md is a markdown file. This is just a short version of it, they can get really, really long, but it’s essentially to just tell the AI everything it needs to know about this folder, about this repository or whatever it ends up being. So it defines things like, this is what’s in the folder, this is where things are, what are some rules. And then it also points to other files that are useful for when it’s doing different things, but maybe you don’t want it to load all of those things every time you instantiate a new session. So it’s essentially just instructions for the AI every time it joins the project.

[here there was some Q&A about agents; see video]

It seems like a lot of overhead

Okay, so that was the knowledge management side. On the domain level, what do we know: sources, syntheses, pipeline, conventions. And then on the meta level, what are the processes, so this is how are we going to work.

One thing I want to say: this seems like a lot of overhead. But wanting to use AI with all this, it’s necessary to just make sure that you have a very nice clean third brain, or second brain if you’re doing it just by yourself. But also, I tried doing stuff like this a couple of years ago, before AI, in a different context, and it was just too much to do all the documentation myself. So I also think that having the AI, while it makes it necessary, it also makes it cheap, because it can now run all of this stuff without you having to do it manually.

And I think the bonus of doing all of this is that it also makes onboarding new group members easier. If you already have all of this set up, then when a new person joins they can just go in and get the AI to explain what the group has been working on for some time and what are the things that they need to know. And it also makes your individual work with AI more effective, because you’ll pick up a lot of these best practices.

Workflows and processes

I’m going to jump to the meta level first, because I think it’s a little bit easier to describe.

Meta level: keeping the third brain from rotting

These are just some examples of workflows you can set up that your AI will do to keep this directory clean.

For example, the triage. Maybe you drop a paper into the inbox and then AI adds metadata, finds out where the links are, gives it a conventional file name, files it under the appropriate source, and then it says, human check, did I do it right? So that’s something you don’t have to think about, where do I file this thing. It’s always just drop it in the inbox, it’ll do it on its own.

Then a distillation thing. For example, if a new source has been filed and you have all of these different synthesis files already existing, like maybe your wiki or you have some kind of map, and it files an extra paper, you can just get it to go through all of the syntheses and propose changes. Like, oh, I think this paper should slot into the wiki here, or you should add a node in your mind map here. And then at the end the human accepts it or rejects it.

You can have a reconciliation thing where periodically, maybe once a month, it just goes through the whole directory and highlights, are there any stale things, are there things that are contradicting that need to be resolved.

Or maybe you’ve had a meeting. Maybe we’ve had this meeting here and we’ve been recording it with Granola or something, and we have a transcript of the meeting. You can just dump that transcript into the inbox and it goes through and extracts, did you make any decisions as a group, were there any questions raised, are there any actions, what pipeline items need to be updated. So you can just get it to extract all of these things that make sense for you as a group.

All of these workflows are going to be stored in these SKILLS.md files. These are the natural language scripts for AI agents. And you’ll come up with more of your own workflows that make sense for you.

Domain level: how ideas become projects

Okay, so that was the workflows on the meta level, how to keep everything clean. But now I wanted to talk about the workflows for the domain. So for taking the ideas that you have, all of these ideas that you’ve generated, and turning them into projects.

I think ideas are just going to start off in your own head, and I would say the first step of this pipeline that I’m proposing is that it should be just a really low-effort brain dump. You don’t want to go through this intense weekend of teasing everything out, you just want to get it out of your head and put it somewhere. Essentially it should be just one paragraph that you can write in 30 minutes or something, that is going to give enough information for the rest of the people in the group to make progress on it. So I would create a file from the template in that pipeline folder, and it just has the one paragraph. That’s fast to get it off your brain and it’s sitting there.

Then, as a group, we all have access to this template.

It has a whole bunch of things that usually you feel need to be answered before you can work on a project as a group. This is going to be something that the group fills out over time, and it’s the big part of this process. It might take weeks or months. And since it’s not kept in someone’s head, since you’re not juggling all this context, it doesn’t need to be this intense session. It can be something that is done over weeks, over months, by different people that are communicating in a way that is distributed, and that keeps all of the context. And then the incubation period is kind of woven into the fact that you’re taking some time to work this through.

The template above is the raw markdown, which is what the AI wants to read, but markdown is nice because you can open it in a markdown viewer and it makes it human readable.

So it could be something like, okay, this is where you dump your paragraph, then a simple statement of what you’re trying to solve, why bother, current approaches and why we expect them to fail, if we had a solution what would it look like, how would we know we’d solved it, all sorts of things. You’ll come up with what are the things you generally need to know about a project before deciding that you should work on it. Maybe a whole bunch of sources. So this is the thing that you collaborate on collectively and have discussions on.

And I just want to highlight, markdown is pretty much the standard file format for readability by humans and AI. So that’s why you see everything usually in markdown files.

From workup to proposal to project

So this is the big part. You fill out this template that has all of the information that you need, and then you can have the AI generate some proposal that then gets sent off to the winter school or whatever. So this is something you sign off on, do we like what the AI proposed? And then I think once this thing is a well-defined project that people can start working on, I would move it out of this third brain folder and create a new folder that is now the project, which can bring in external collaborators. Maybe a whole bunch of PSI students, and you don’t want them having access to your entire third brain, but just to that project.

So that’s the pipeline that I’m imagining, and all of these workflows would be described in the README file stored in this pipeline.

Okay, so I think we’ve touched on all of the conceptual stuff: the knowledge management, what we know, how we work, how ideas become projects, and the AI processes to keep the whole thing from going stale.

Tools

I just wanted to now touch on tools and how this would all be implemented in practice.

Models vs. harnesses

The first question is probably going to be, which AI should I use. I think most people have heard that there are all of these different models, like Claude Opus 5 from Anthropic, GPT-5.6 from OpenAI, that’s the one that’s supposed to be really good at research. I hear Claude, I don’t know, someone told me that Claude is more creative or something. And there are various other models from other companies. In some sense that’s kind of a personal decision. You can use many models, for whatever you find that the model is better at for different tasks.

I think the more important decision to make is which harness you should use. Harnesses are, okay, the model is essentially just the thing that has all of the weights, the thing that’s being trained, but you never just directly talk to the model. You’re always talking to it through some kind of software infrastructure that’s been built around the model for you to be able to engage with it.

Roughly speaking, you can split it up into three different kinds. There’s chat, general purpose agentic, and custom purpose agentic.

Chat is the one that you’re usually exposed to first, like ChatGPT or Claude on your browser. It’s usually an app or something on the browser. It just sees the files that you upload and ask it questions about, and it gives you output, maybe it gives you a file or an answer, but it’s something that stays in the browser until you copy it out. So that’s the basic chat.

Then the general purpose agentic ones. They are the ones that run on your machine, and they run in the folder that you point them to. So you’d be pointing that at your third brain, and it reads and edits things in this folder and it does things. If you tell it, send this email, or process this file, it’ll do it. Examples of this are Claude Code, which is the one that I mostly use, and Codex, which is the ChatGPT one that I also started using recently.

And then there are these custom purpose agentic ones. Maybe you guys have heard of Transformer Lab. Transformer Lab is a local startup. They have this AI scientist called Primus. The idea is that this thing can do one-shot research. So it’s an agentic model but it’s designed for one fixed job. In this case, creating a research paper. You give it a problem and it spits out a research paper after like 40 hours of running tons of agents doing crazy things. Another popular one was Lovable, which is the one that people were using to design websites. It was kind of popular maybe a year ago, until Claude just became really good at making websites. But that’s another kind of harness.

So for what we’re proposing here, I think what you want to use is a general purpose agentic one. You can choose any of those ones depending on what you’re comfortable with. If you have a software background you’re probably really comfortable with Claude Code, but you can also use something like Claude Cowork which does similar things.

Where to store the third brain

So now the question is where are you going to be storing this third brain. You want this on your machine, because you don’t want it in an app, because you want the agents to be able to access it. And pretty much you have two options. You can have it on a shared folder like Dropbox or Google Drive or whatever, or on GitHub, a combination of Git and GitHub.

I think you absolutely need to be using Git and GitHub. Git is a piece of software that does all of the version control on your local machine, and then GitHub is like the cloud version of that, that allows you to share it across different groups. Usually you’ll be doing stuff on your machine with Git and then syncing it to GitHub, and then other people can access those syncs. So that’s how you share it.

Why do I think you should use GitHub? It really separates these aspects of saving something as you’re working on it, sharing it with people, and then integrating it into the collective third brain. And it does that in a way that supports discussion, which I think is really important as you’re taking things from the idea to the project pipeline stage, because you really want to be able to have discussions about it. I think that helps you refine the ideas in a shared but distributed way, and no one’s changes are going to override somebody else’s changes without it being a conscious act. If you have Dropbox and you make a change, it might write over something. If your agent is in there doing crazy things, it might mess everything up.

So what do I mean by this? Pretty much, if you save something on Google Drive it’s just going to integrate it. Whereas on Git you have something called a Git commit, which is what you do when you want to save something that you’ve just made. Then you have it in a branch, and then you push that branch to GitHub. At that point everybody can access it and review it, but it hasn’t yet been integrated into the agreed-upon third brain. Then you have this whole process of commenting on something called a PR, a pull request. That’s where all of the discussion happens, and you make changes to it and everything until everyone agrees, and then you merge the PR into the main branch, which is how you integrate it. Now this is the source of knowledge that we all agree on.

At first, when you first start learning how to do that, it just seems really stupid and a mess, and you screw everything up and you think you’re going to make big mistakes. But once you get the conceptual idea of what’s going on, then you can just get your AI agents to do all this for you as well. I essentially haven’t made any mistakes since I’ve started using AI, whereas before it was always such a drama. So I think it’s a very nice process.

It also has additional machinery for moving ideas through stages, like creating something called an issue, which lets you have discussions. You put something there before, it’s kind of like a proposal. It has these templates, kanban boards and stuff. So it has lots of nice infrastructure.

I think this one is really important, something that people in academia are always worried about: how do we assign attribution? GitHub records every contribution that anyone makes throughout the thing. So maybe by the time a paper is ready, you can then go to your AI and be like, hey, look through the Git history and give me a report on what everyone’s contributed, and then it’ll give you a list over the last six months of what everyone’s done and who should be first author and whatever. And I think in some future where maybe papers aren’t a unit of work anymore, maybe GitHub repos are going to be the things that are considered the work that you share with people, all of that attribution is going to be there all the way from the beginning to the end. So I think that’s a really important aspect.

And also, because it’s something that software developers use, and AI agents first became really important for software developers, they already know how to use it. So everything you want to know, you just ask, tell me this, tell me that, and it’s really good. Also, if all of you are running your own agents, having this Git and GitHub setup keeps them all out of each other’s way. You don’t want both to be working on Dropbox and Rob’s agent writes over something that Lucien is doing or whatever. So I think there are all these reasons why Git and GitHub are really, really good for doing this.

But I think you may still want to have a shared Google Drive, because Git doesn’t really work well with large files. So maybe if you have a video that’s a one hour talk or something, it just wouldn’t be great to keep that on GitHub. I would have an additional thing that just stores all of your large files, non-text files. And those don’t need to be organized in any particular way, because your third brain will just link back to everything.

So that’s the setup. You may want to store other things on a drive, but I don’t think you ever want your agents to be working inside the drive.

Using GitHub

So what would I recommend? I think as a research group you should create something called a GitHub organization. Right now probably a lot of you have your own GitHub profile. You can create repos there, but it’s a bit of a burden for one person to own everything that’s going on in a group. Maybe the supervisor can own it, but maybe the supervisor doesn’t want the burden of managing it. So having an organization I think is a good way to do it. You would create the Quantum Foundations GitHub organization.

This thing allows shared accounts where teams can collaborate across many projects at once, and it has sophisticated security and administrative features, so you can give access to different repos to different people and so on. So I would have one main repo that’s the third brain repo, and this would be accessible by all of the core members of the group. And then I would have individual repos for projects, once something has gone through the pipeline and it’s now a project, we’re bringing in collaborators, we’re working on it. So then each one of those would have its own repo.

This is an example of a repo that I set up based on the stuff that I discussed in the talk, and it’s the one that we’re going to use for the hands-on part of the workshop.

Honorable mentions

Before I wrap up I just wanted to give a few honorable mentions to other kinds of tools I found helpful.

I really like to use VS Code, like 90% of the time. If you look at my computer it’s got that up. It’s not because I’m writing, I mean, okay, so initially this, ID stands for integrated development environment. So this is something that software developers used, but I think it’s just great for anything you want to use with your AI agents. Here you have the list of the files, these open up, you can see everything, so it’s your whole directory. You can have a terminal open. So here is an actual terminal that you can run bash commands in. In another terminal you can open Claude Code or Codex or something. And so then you can chat with your AI agent, or whatever, your harness, here.

Another way of using VS Code is I think you can have a Claude plugin, and it’s somewhere over there, but I generally don’t bother with that. I just use it in a terminal. I think that’s a really convenient way of doing it. So here, for example, you have the source code, and then it renders the markdown in this nice human readable way. It’s also really good for LaTeX. You can have your LaTeX source file here and then your PDF that’s generated there, and it just automatically updates things. So you can get Claude to change something in the code and it automatically sees it in there, if you have the right plugin. I just think it’s a really convenient way of working with many different files like this. And by default it has access to everything here, but you can grant it permissions to access other parts of your computer if you want as well. So that’s one.

Another one is Obsidian. This is pretty much how people used to organize their second brains. You would have folders with markdown files and so on. And it does really nice cross-linking between your files, like if you were building a wiki, and you can generate these graphs of how different markdown files connect. Again you can have a terminal to talk to your Claude Code. So that’s another one that people use for their personal knowledge management systems. And the nice thing that it does is that there you can edit the markdown directly and then it renders it in this nice human readable way, rather than in VS Code where you have to edit the source code itself.

But the thing that sucks about Obsidian is that you need to store everything in a vault, which isn’t ideal for just accessing random markdown files on your computer. So I really like something called Typora, which just lets you open local markdown files in a really nice readable way, which you can edit. But the problem with that is if you’re opening local files, you can’t sync them between your computer and your iPad. So another one that I really like is Bear, which I use if I know I’m going to be working on something on my computer and on my iPad. But there you have to store the thing in the app rather than locally on your computer.

So yeah, they’re just tools that I found pretty useful working with markdown files, which are critical to working with AI agents.

Key takeaways

Okay, so that’s pretty much the talk. I just want to give a few key takeaways.

The first takeaway is that I think the AI bottleneck in research is in this middle section, which is this strongly coupled workflow stage.

I think to accelerate that part you’re going to want to create this third brain, which is this shared body of knowledge and techniques that have been really externalized explicitly in some kind of system. And you want to use the AI to make sure that it doesn’t rot or go stale.

And I think the key tool here is Git and GitHub, which separates this save, share and integrate part in a way that helps you process the ideas to projects.

I guess, as you’re watching this, you’re probably like, oh my god, this is a huge amount of overhead compared to how you usually work. And it’s true. If you take a single idea, if you were to go through all of this process rather than the organic way that you do it, it’s a crazy amount of overhead. But I think when you’re in a situation where you have too many ideas that you can process in the way that you’re naturally used to working, that’s when something like this is going to become helpful. So I think it can make things collectively work more efficiently if you execute it well, even if it seems less efficient for an individual project.

Shoutouts

Just a couple of shoutouts. A lot of these ideas came from a project I worked on three or four years ago called DRiP, doing research in public. This was before AI existed, so I had all of the workflows but it kind of didn’t work because it was too labor intensive. But it was something that I thought about a while ago, and Christophe Pere also helped me tease out some of these ideas, so I wanted to give a shout out to him.

And also to Builders Club, which is in downtown Kitchener. It’s a co-working space that I hang out at, and everyone there is at the bleeding edge of AI tools, so I just get to see all of the cool stuff that people are doing. A lot of what I’ve learned about how to use AI I picked up from people there.

So yeah, that’s pretty much it for the presentation part. Thanks for being here.

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It was a real treat to share the stage with other local quantum folks: Bohdan Kulchytskyy from Nord Quantique, Alexander Neirinckx from Irréversible, Abdullah Khalid from 1QBit, Meenu Kumari, PhD from the National Research Council Canada, Benjamin MacLellan from Open Quantum Design / Ki3 Photonics, Mercedes Gilroy from Coin Edge, Yushao Chen from Xanadu, Chang Liu from WaveRyde, and Stephanie Bohaichuk from Quantum Valley Ideas Lab.

Thanks very much to the PI Career Trajectories team for organizing the event and to the PI AV team for putting together the recording!

Quantum Salon Insights is an independent reader-supported research project focused on making sense of the quantum tech ecosystem.

Here we explore the ideas, patterns, narratives, and structural misalignments that show up across quantum tech and adjacent deep-tech fields.

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One of the most interesting, but challenging, aspects of quantum technology is its diversity. Not only in the kinds of technologies being developed—computing, communication, sensing—but in the companies themselves, which differ in development speed, maturity, and business models.

I’m constantly learning how differently these companies operate, though my work with clients, through conversations with other folks in the industry, and from reading about startups and tech more broadly. While there hasn’t been much written about running quantum tech startups specifically, there’s a large body of material on running regular tech startups.

But to understand which lessons are likely to apply to which quantum tech startups, it’s useful to categorize these startups along several axes. That’s what I want to do in this article.

I’m specifically going to focus on startups that are building a product, rather than services or consulting companies. I’m using “startup” here in a specific way, which I’ve written about in more detail here.

The four axes

When it comes to categorizing quantum tech startups, I’ve found four dimensions to be particularly useful. For the sake of discussion, I’ll describe them as binary (pre vs post, slow vs fast) but in practice they form a spectrum.

The four axes are:

• Pre-PMF vs post-PMF (PMF = product–market fit)

• Technology push vs market pull

• Slow development cycles vs fast development cycles

• High-expertise products vs low-expertise products

These are definitely not independent dimensions, but for the sake of simplicity, I’ll treat them as if they were.

A company’s position on an axis can change organically over time, or it can be the result of a deliberate decision.

Pre-PMF vs post-PMF

This first axis is product-market fit (PMF). PMF is an important dimension for any startup, not just those in quantum tech.

If you google the term, you’ll get a wide variety of definitions. Here I define it as follows.

Product-market fit: when a product consistently matches a real, felt need in a defined user segment, such that demand grows without heroic effort.

For a pre-PMF company, sales and communication are usually founder-led. Everything is still evolving so it’s difficult to externalize it enough to be able to get outside help. Messaging tends to vary a lot, not only depending on who the founders are talking to, but also over time, as the founders adapt what lands and what doesn’t. In this stage, the company is still figuring out who actually cares and why.

Post-PMF, sales become more repeatable, in the sense that people other than the founders can do it. Communication becomes teachable, with a shared narrative, rather than something that lives only in the founders’ heads. Messaging stabilizes around a proven use case, and the focus shifts from exploring the market to scaling the segment that already cares.

Technology push vs market pull

The next axis tell us whether a company starts from a market need or from a new technical capability.

In a market-pull startup, the process usually begins with a clearly defined problem. The startup exists to solve that problem, and early interest comes from people who might actually buy the product.

In a technology-push startup, the process usually begins with a new capability, often rooted in a scientific or engineering advance. In the early stages, it’s not clear what the use cases are, so there are no obvious buyers. The people who are most interested in what the company is doing are other researchers and technical peers.

There exists a large body of startup literature (books, podcasts, blogs, founder lore, etc) but most of it is written with market-pull in mind.

Not in the sense that it assumes that a startup is market-pull, but in the sense that it assumes that a startup has the ability to become market-pull and therefore it should. You often here things like:

There’s nothing worse than being a solution in search of a problem.

This is great advice for startups based on mature technologies, like software, who can build what buyers want in a relatively short amount of time.

It’s more difficult to do for deep tech startups, especially those with long-development cycles.

That brings us to the next axis.

Slow development cycles vs fast development cycles

Some products allow for short build–test–iterate loops. You can ship partial versions early, learn from real usage, and make decisions based on data.

Others require long periods of R&D or engineering before anything usable exists. Partial versions can be hard to test with users, and decisions rely more on forecasts and judgment calls.

A lot of quantum hardware typically has slow development cycles. Quantum software, on the other hand, can have fast development cycles.

Again, the large body of startup literature implicitly assumes fast development cycles. But if your development cycle is slow, not everything from the startup canon will be relevant. For example Agile methods tend to make sense when you can iterate quickly, while more sequential approaches like Waterfall might be better when development takes longer.1

High- vs low-expertise products

The final axis is about who your product is actually for.

Some products require deep technical expertise to use. Their value is obvious mainly to specialists, and communication has to match that.

Others require relatively little background. Their value is more immediately visible, and communication can stay higher level.

Specialized quantum products being sold to quantum companies tend to fall into the high-expertise category. Other products, like quantum-themed games sit much closer to the low-expertise end.

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Putting the axes together

If you’re a nerd, like me, you might have had this thought: 4^2=16. That’s sixteen combinations!

You might even go as far as considering what each of these combinations means.

It turns out that not all combination are equally interesting, but some of them represent some interesting scenarios.

Let’s take a look at these.

Scenario 1A: Conventional broad-market startup

In this scenario, the company is market pull, has a fast development cycle, and is building a low-expertise product. It may be pre- or post-PMF, but it effectively looks like a regular tech startup from Silicon Valley.

How a post- or pre-PMF startup operates is going to vary quite drastically. Fortunately, there is a tonne of advice out there for both phases, and a tonne of people who have built such startups for you to talk to, and that advice will apply if the startup is in Scenario 1A.

Scenario 1B: Conventional expert-user startup

This scenario is almost the same as above, except you have a high-expertise product. So for example, you’re selling software that will be used by technical folks inside a company, or researchers at a university.

The main challenge in this scenario is communication.

Because the product requires deep domain knowledge, the messaging to technical customers has to be quite specialized. At the same time, the product needs to be explained in much plainer terms to less technical stakeholders, like investors. Doing both well usually means having someone with real domain expertise who can also translate that work without flattening it.

This is the kind of communication work we do at Quantum Salon.

Scenario 2: Conventional tech push startup

In this scenario, the company is pre-PMF, technology-push, but has a fast development cycle. Even though the starting point is a new capability rather than a clearly defined market need, the company can iterate quickly and learn from feedback.

Because of that, these companies often end up moving toward a market-pull approach.

You see this a lot in the quantum tech ecosystem, especially with algorithm-focused startups. They might begin with a specific quantum algorithm aimed at a particular domain, like drug design or optimization. But after talking to customers, they figure out that the original use case isn’t very compelling. At the same time, through these conversations, they also discover problems that customers do care about. The startup then shifts its focus to those problems, often trying to solve them with different techniques like classical or AI-based algorithms rather than strictly quantum ones.2

That move is sometimes mocked, particularly in academic circles. But in practice, it can be the right business decision. It increases the chances that the company survives, and it doesn’t necessarily mean abandoning the original research. Some companies continue working on the original quantum stuff in the background, for later, while building something that people want to use now.

Scenario 3: Deep tech startup

In this scenario, the company is pre-PMF, technology-push, and operating with a slow development cycle. This is where a lot of deep tech startups find themselves.

It’s a really tough spot to be in. They have a cool technology, but it’s going to take a really long time to develop it. It’s not clear what the product is. They might not even know what the market is, or if there even is one. But they founders have started a company and need to figure out how to fund it during development.

How do they deal with this? I’ve seen it play out in two main ways.

The first way is to move toward market pull. That usually means the company can’t stay in a slow development cycle. It has to find ways to shorten the cycle so that they can iterate faster, which may mean scrapping/putting on hold most/all of the technology they started with. Once they do that, they can think about the market, adapt to it, and do the usual startup things.

You see this in quantum tech when companies start out building hardware, but also have a software, cloud, or services component. At some point, they decide to drop the hardware effort and focus entirely on the software or cloud product. As in the above scenario, sometimes they can continue working on the original quantum stuff in the background, but it’s more difficult when it’s a capital intensive technology like hardware.

The second option is to not do that, and stay technology-push with a slow development cycle. That’s the scenario that most quantum hardware companies, that choose to continue to focus on hardware, are in.

If you take this route, you’re deep in the Valley of Death, and life is hard.

Some regular startup lessons might not apply

(This section is my own, non-standard, view)

I think for companies that choose to remain in Scenario 3, some parts of the startup cannon stop being helpful, especially the idea that a company should focus so heavily on customers/end-users and use cases. If it’s going to take five to ten years to develop a product, then asking customers what they want now isn’t going to be very productive. Of course, the company still needs to believe that a useful product exists at the end of the path, and work towards it, but it’s like steering a big ship towards that outcome rather than building it incrementally based on what customers say today.

Things also get awkward from a communication perspective when tech-push companies, that are still far from having a product3 (let alone product–market fit) present themselves as if they were market-pull. They end up squeezing what they’re doing into the language of products, users, and applications that don’t yet exist. At best, it’s confusing. At worst, it raises questions about clarity, competence, or whether the company respects all of it’s stakeholders. All of these undermine trust.

I understand why there is pressure to focus on customers prematurely, because that’s the way everyone is used to thinking in business. But I believe that long-term outcomes would be better for Scenario 3 companies if they focused their communication on the stakeholders that actually matter for survival during the development phase: investors, corporate partners, academic collaborators, policymakers, and future technical hires.

The task is to explain why the technology matters and to make a credible case that a market could exist, without pretending that concrete use cases or users are already there. That requires understanding what each of these groups cares about, what would make them want to engage, and how to communicate with them in a way that stays internally consistent.

That kind of communication depends on a deep understanding of the different actors in the quantum technology ecosystem, and how they relate to one another. This is also the kind of communication we do at Quantum Salon.

Choosing which lessons to apply

I find this classification really useful in understanding why different companies do what they’re doing, and to reason about what I think they should be doing.

If you’re working with (or for) startups in the quantum space, it might also help you decide which lessons are worth paying attention to, and which ones assume conditions that don’t (yet) hold for that company. At the very least, it should help you pause and run a sanity check to see if they do.

I only covered several scenarios (combinations of the four axes) that are most interesting to me. I wonder if there are others that you find interesting.

Quantum Salon Insights is an independent reader-supported research project focused on making sense of the quantum tech ecosystem.

Here we explore the ideas, patterns, narratives, and structural misalignments that show up across quantum tech and adjacent deep-tech fields.

To receive new posts and support this work, consider becoming a free or paid subscriber.

I really like the analogy of algorithms as recipes. I think it’s one of the few effective analogies out there, in the sense that most people have a good feel for the reference point, and the mapping between the algorithms and recipes is reasonably tight.

We can extend the analogy to quantum computing. Other people have made similar connections in the past (here, here, here, and here).

In this post, I take a different approach.

Extending the algorithm-as-recipe analogy

If an algorithm is like a recipe, then we can think of the inputs to the algorithm as ingredients in the recipe. An algorithm also has operations the same way that a recipe has techniques (e.g. grate this, mix these two things together, bake it all for 30 mins). And an algorithm is usually executed on a computer, the same way that a recipe is usually executed in a kitchen.

Running an algorithm produces an output, much like following a recipe produces a dish. But a dish on its own isn’t necessarily a meal. A meal is what you get when that dish is combined with other dishes, served for a particular purpose, and eaten (maybe a stretch by this point, but stay with me). In the same way, an application is what happens when the output of an algorithm is used as part of a broader workflow to do something meaningful.

We can summarize the mapping as follows:

• recipe ↔ algorithm

• ingredients ↔ inputs

• techniques ↔ operations

• kitchen ↔ computer

• dish ↔ output

• meal ↔ application

A standard kitchen, like the one you cook in, obeys the laws of classical physics.1

But now imagine that your kitchen is a quantum kitchen.

The quantum kitchen

A quantum kitchen obeys a completely new set of physical laws: the laws of quantum physics.

In this quantum kitchen, you have a bunch of exotic ingredients that you’ve never seen in a regular kitchen. And you have a bunch of exotic techniques that you’ve never had access to in a regular kitchen.

But it’s up to you to come up with quantum recipes.

And since it will be very expensive to build this quantum kitchen, we want those recipes to either prepare certain meals more quickly than the same meals prepared in a regular kitchen, or you want those recipes to make meals that are better in different ways, for example, more tasty, or healthier, or prepared with less expensive ingredients, or in larger batches.

Barbie and The Sims

Unfortunately, we don’t have a fully-functional quantum kitchen yet.

What we do have is the analogue of a quantum Barbie kitchen.2 And we also have the analogue of a—potentially larger—quantum kitchen inside a game like The Sims.3

What’s neat is that, despite this, we’ve already been able to create quantum recipes that can be executed in existing Barbie kitchens, that are able to prepare certain kinds of dishes more quickly than you could in a regular full-sized kitchen4.

The problem is that no one wants to eat those specific dishes56.

Meals that people want to eat

It turns out that we don’t actually have very many proven quantum recipes for meals that people want to eat (not yet anyway)7.

But we know that one of these recipes8 makes it possible to prepare a certain dish9 much, much more quickly than you could in a regular kitchen with a regular recipe. This dish is used in a meal (a poisonous meal?)10 that is so important, that being able to make it more quickly, is almost enough of a motivation on its own to build one of these quantum kitchens. In fact, almost every major government has a strong interest in having one.

Can we create new recipes?

If we only have a few proven quantum recipes for meals that people want to eat, can we come up with others?

We have reasons to believe that we can, but only for some specific kinds of dishes, so we still need regular kitchens to continue to make many of the dishes we already know how to make, as quickly as possible.

A more exciting possibility is that we might be able to make dishes that we’ve never made before, or even imagined being made in a regular kitchen.

In both cases, however, we need to create more quantum recipes.

How do you create new recipes?

I often wonder about this when I’m eating something. How did someone think to combine these ingredients in this particular way? Occasionally, some brilliant chef might have had some special insight, but the typical answer is trial and error.

We’ve been lucky that a few people have been able to come up with a handful of quantum recipes in the abstract, and we’ve been lucky that we know some special tricks for confirming that these recipes will be faster than regular recipes, without having to actually prepare the meal.

But to really come up with new exotic recipes, we need to get into the kitchen and play around, to get our hands dirty, to accidentally combine things together that don’t make sense at first, to let something sit on the counter for too long, and have it surprise us.

We can only do that once a fully functional quantum kitchen gets built and we can cook in it.

The exciting part is getting to cook

The space of possible quantum recipes is structured in a way that we don’t fully understand yet. We can reason about it abstractly. We can prove a few things on paper. We can test small examples in the Barbie kitchen or simulate them in the Sims kitchen. But we don’t have the kind of hands-on experience, in a real quantum kitchen, that lets us develop intuition.

If you talk to chefs11 in the field, this is often what motivates them. Not the promise of specific meals, but the chance to explore a new kind of kitchen, unlike any they’ve ever cooked in before. To see what happens when you actually start combining these exotic ingredients in unusual ways.

What we know and have built so far has allowed a small number of genuinely new recipes to be discovered, and it’s given us hints about what might lie beyond them.

The hope is not that a fully functional quantum kitchen will suddenly reveal a menu of obvious new meals, but that spending time inside this kitchen will gradually expand our sense of what’s even cookable. That’s what’s exciting to me.

Bon appetit!

Quantum Salon Insights is an independent reader-supported research project focused on making sense of the quantum tech ecosystem.

Here we explore the ideas, patterns, narratives, and structural misalignments that show up across quantum tech and adjacent deep-tech fields.

To receive new posts and support this work, consider becoming a free or paid subscriber.

I was catching up on the quantum news over the break and at some stage noticed “this is mostly all press releases and research papers”.

I found that curious because it was in contrast with the more general news I read, which reports on stuff that was observed to have happened in the world, not announced in a press releases.

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So then I tried to remember if we’d had any of that kind of reporting in quantum tech and could only come up with a couple of examples of stories from the last year.

I posted about it on LinkedIn to see if anyone had any more stories to add. No one added any, but Hilary Kaye helped me refine the distinction I was trying to make (which went beyond press-releases). She phrased it as:

. . .the difference between news that comes from a company (regardless of how it reached the journalist) and news that happens on its own, i.e., either an enterprising journalist pursues a story on their own or something that is intrinsically newsworthy and generates coverage without a company suggesting it.

I’ll refer to these two kinds of news as source-led vs. emergent news:

• source-led news: coverage whose initial push comes from a company or organization, e.g., via press releases, blog posts, or direct pitching to journalists

• emergent news: coverage initiated through independent journalistic judgement, either by an enterprising journalist pursuing a story on their own or by events that are intrinsically newsworthy and generate attention

There’s nothing intrinsically wrong with source-led news. It’s an important way to get news to the media (although, things can get iffy when that kind of push leads to too much hype). But emergent news is important too. And the presence or lack of it should in itself tell us something.

Now, we do have quite a few quantum-focused publications in the ecosystem: Quantum Pirates, Deep Policy, The Quantum Foundry, Quantum’s Business, The Quantum Stack, The Quantum Dragon, Sutor Group, The Quantum Leap. But these mostly focus on thought leadership, analysis, expert commentary, etc. Also very important, but not what I’m getting at here.

In professional news outlets, on the other hand, we’ve seen an increase in general quantum computing coverage over the last couple of years, but these are often context-setting pieces like this one (aka: quantum computing exists and this is why you should care about it), rather than event-driven news.

So is there really no emergent, event-driven news coverage in quantum tech?

To answer that question, I decided to do a bit of digging, and found these 22 pieces.

22 emergent, event-driven news stories in quantum tech from 2025

1. Quantum computing stocks take a hit as Nvidia CEO predicts long road ahead, Reuters (Jan 8, 2025)

2. Outbound Investment Screening Rule Goes into Effect (includes quantum tech), Holland & Knight (Jan 10, 2025)

3. A quantum computing milestone is immediately challenged by a supercomputer, ScienceNews (Mar 12, 2025)

4. Quantum leap: scientists skeptical of Microsoft’s invention of a new state of matter, The Week (Mar 24, 2025)

5. D-Wave CEO Responds To Criticisms About Quantum Supremacy Claim, The Quantum Insider (Mar 14, 2025)

6. Class Action Filed Against Quantum Computing Inc. (QUBT), PR Newswire (Mar 18, 2025)

7. Nvidia CEO says quantum computing is reaching an ‘inflection point’, CNBC (Jun 11, 2025)

8. U.S. Lawmakers Urge Action On Cybersecurity In Face Of Quantum Threat, The Quantum Insider (Jun 26, 2025)

9. Quantum alternative to GPS navigation will be tested on US military spaceplane, The Conversation (Aug 14, 2025)

10. Controversial Quantum-Computing Paper Gets a Hefty Correction, Scientific American (Aug 21, 2025)

11. SEC Documents Confirm Larger Quantinuum Funding Round, The Quantum Insider (Aug 26, 2025)

12. Experts pour cold water on HSBC quantum computing ‘breakthrough’, GT Global Trading (Sep 30, 2025)

13. Physics Nobel awarded to three scientists for work on quantum computing, BBC (Oct 7, 2025)

14. BlackRock to U-turn on quantum computing ETFs, ETF Stream (Oct 15, 2025)

15. IonQ, Rigetti Computing, D-Wave Quantum, and Quantum Computing, Inc. Have Served Up an $875 Million Warning to Wall Street, The Motley Fool (Oct 17, 2025)

16. Google’s Quantum Echoes Algorithm Sparks Controversy Over Claims, Quantum Zeitgeist (Oct 22, 2025)

17. Trump Administration in Talks to Take Equity Stakes in Quantum-Computing Firms, Wall Street Journal (Oct 23, 2025)

18. Quantum computing firms jump after report of US government stake talks, Reuters (Oct 23, 2025)

19. Quantum stocks stayed higher after Trump administration’s denial of equity talks. Here’s why, CNBC (Oct 23, 2025)

20. IBM joins the outage club after quantum cloud service unexpectedly went down, sdxcentral (Oct 31, 2025)

21. The Pentagon Can’t Trust GPS Anymore. Is Quantum Physics the Answer?, Wall Street Journal (Nov 19, 2025)

22. XPRIZE Quantum Applications Names Seven Finalists in $5 Million Global Competition, Quantum Computing Report (Dec 10, 2025)

23. Study Finds Security Flaw in World’s First Quantum Satellite, The Quantum Insider (Dec 15, 2025)

(I think I did a pretty thorough search, but I probably missed some. If you know of any other stories that fit my criteria, let me know and I’ll add them in.)

Is there any reason to favour emergent news over source-led news? I don’t think so. But one thing I’m finding is that the volume of source-led news out there is overwhelming. There’s gotta be like 1000 press releases per year now (does someone want to check?1). It’s hard to keep up with it all. So I like having different filters that reduce the volume in different ways that help me make sense of things. Picking out emergent news turns out to be one of those filters.

So what can we say about this list?

The first thing I found interesting was that this list is very short. Just 22 articles compared with hundreds of source-led pieces.

The second thing I found interesting was that four of these articles were written by the same person: Matt Swayne from the Quantum Insider. So, nice job, Matt!

What about content? Two were about prizes (feel-good). Three were about stuff breaking (morbid curiosity). Five were about stocks (money). Five were related to high-profile personalities (celebrity). Six were about controversy and skepticism surrounding big results announced by the big players (drama).

Almost all of these stories would be interesting to quantum outsiders, unlike most of the source-led stories. Does that mean that emergent news is for quantum outsiders and press releases are for quantum insiders?

For, now, maybe. But what would emergent news reporting, that was interesting only for insiders, look like? And why don’t we have any?

The nature of the ecosystem right now is that it’s mostly in everyone’s interest to be nice to each other. A rising tide lifts all boats, after all! And it’s still a very small and interconnected ecosystem. But maybe those dynamics will change over time and different kinds of behaviours will be incentivized, for better or for worse. And then maybe one day we’ll get BlueSky flame wars between quantum CEOs! 😅

Ok, but seriously, this isn’t about wanting spicier quantum news. In fact, I think most of us want less spice in our news these days (looks around). But ecosystems evolve, and I think this could be an interesting lens to try to see the quantum ecosystem through. I’m curious how the 2026 list will look.

What about you?

Quantum Salon Insights is an independent reader-supported research project focused on making sense of the quantum tech ecosystem.

Here we explore the ideas, patterns, narratives, and structural misalignments that show up across quantum tech and adjacent deep-tech fields.

To receive new posts and support this work, consider becoming a free or paid subscriber.

Greetings from Barcelona. I’ll be attending the Quantum Education Summit tomorrow. I thought this would be a good time for me to write down my thoughts on how we should (and shouldn’t) talk about quantum technology with general audiences.

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As an aside, when I moved from academia to industry, I found it funny that people used the term quantum like it’s a standalone thing. Because technically, it’s supposed to be a modifier: quantum physics, quantum computing, quantum sensing.

Inevitably, I adopted this lingo too, but I think it’s related to my pet peeve about how people talk about quantum tech, where they conflate different “quantums” in an unproductive way. Specifically when they conflate quantum foundations, quantum physics/engineering, and quantum information.

In this post, I’ll describe how I see the distinction between these fields, and make my case for why it’s helpful to maintain that distinction when discussing quantum tech with general audiences.

But first we should start with quantum theory.

Quantum Theory

Quantum theory is a set of equations and rules that tell you how a system behaves. It’s a mathematical framework. On its own, it doesn’t tell you what the system is—an atom, a photon, a qubit, a molecule, a cat, consciousness—it just gives you the machinery for calculating how that thing evolves, interacts, or gets measured.

That mathematical framework branches into different disciplines depending on what systems or questions you apply it to.

Quantum Theory applied to Philosophy = Quantum Foundations

Quantum Foundations is the field of research where people ask questions like: what does the wavefunction mean? It’s where people talk about Schrödinger’s cat, Wigner’s friend, many worlds, spooky action at a distance, and other things that seem like paradoxes. This is the stuff that captures the imagination, both for scientists and non-scientists alike. Understandably, it’s the stuff people want to talk about.

Quantum Foundations is a very important academic discipline—one that I would like to work in if I ever go back to research again—but you don’t need it to understand quantum tech. In the physics community, there’s a famous expression for putting aside philosophical questions in favour of more practical applications. That expression is “shut up and calculate”.

Quantum Theory applied to Physics / Engineering = Quantum Physics / Quantum Engineering

“Shut up and calculate” is the physics and engineering branch. Here, people use the same equations from quantum theory to understand the physical world and to design and build new technologies like quantum sensors, atomic clocks, scanning tunneling microscopes, and of course quantum computers. The questions are practical: can we model this system, can we fabricate this thing, can we make it stable?

The work done here is also very important, but I don’t think it’s particularly exotic. I don’t mean that in a disparaging way (this branch is where I spent most of my academic career, and I think it has immense value). And of course, when you dig deep enough, things like new quantum materials are exotic, but in the grand scheme of things, I don’t think they’re more exotic than the most interesting processes in other parts of physics, or chemistry or biology. They’re not “capture the imagination” exotic like the idea of a cat being alive and dead at the same time or parallel universes.

I would say the main distinguishing feature of this branch is that it’s really hard. The math is really hard. The simulations are really hard. The building the stuff and making it work is really hard.

Quantum Theory applied to Computer Science = Quantum Information

The third relevant branch is computer-science. In this branch, parts of quantum theory are interpreted as carriers of information. Here we have quantum computing (i.e. how to use a quantum computer, rather than how to build one), and other quantum information protocols such as quantum communication, quantum cryptography, quantum random number generation.

The thing that’s special about this branch is that there is real potential for a paradigm shift, with the development and application of new quantum algorithms and other quantum information protocols. You could argue that the paradigm shift already happened with the development of algorithms like Shor’s and the idea of quantum simulation, but these are academic results. In the grand scheme of things, in terms of impact on the general public, a paradigm shift remains to be felt (or even adequately imagined).

So it’s a bit of a Catch-22. Quantum computing could be HUGE. Or it could be a nothing-burger. It’s really hard to come up with new algorithms and applications in the abstract. Only a few people around the world are able to do that kind of work. The hope is that if a large-scale fault-tolerant quantum computer is built, and people start playing around with it, they will discover amazing things for it to do1, things that have the potential to change the world. But we don’t know if they will. And in order to build these devices, it requires billions of dollars of investment and more time than people like to admit. So the challenge is how to make the case for that investment.

Incidentally, here’s a relevant quote I saved a while back:

Mr. Altepeter [program manager for DARPA’s Quantum Benchmarking Initiative] said that among the 10 smartest physicists he knows, “half are convinced this will be the most important technology of the 21st century and will revolutionize everything we do. The other five are convinced you’ll never build a quantum computer no matter what you do” – or that if one is built, “it would never be more useful than your laptop for solving a real problem.”

The communication challenge

I think the potential of quantum computing has absolutely captured the imagination of academics who understand how quantum algorithms and quantum simulation differ fundamentally from their classical counterparts. But it’s really hard to communicate that potential to people who don’t have a PhD-level understanding of quantum theory or quantum information theory.

So you get this bizarre mix of communication blunders:

• Academics trying to explain how the algorithms work, and ultimately losing people in the details.

• The use of imprecise or unsatisfying metaphors for how the algorithms work, which then get twisted when repeated by others, to the chagrin of academics.

• Exotic concepts from quantum foundations being used to motivate why quantum technology is exciting, even though they aren’t really relevant as motivation.

• This idea that we should stop talking about the technology and focus on the specific benefits of quantum computers for users, which is premature, and so it comes out as hype.2

The big picture

I know I’ve been terribly imprecise on some of the details here, and experts reading this are grimacing. But my point is that certain details aren’t important to someone coming from the outside who wants to understand what all the fuss is about quantum technology.

What’s important is the big picture, and understanding which parts of it are or are not relevant, so they can decide where to dig deeper.

Quantum foundations is awesome. If you’re interested in it, absolutely learn about it. But it’s not the reason why quantum technology is important for society.

Quantum physics/quantum engineering and quantum information are much more closely connected to each other than they are to quantum foundations, but I think they’re still distinct in the way in which they’re exciting and the way in which they have potential for impact.

Generally speaking, I don’t think the application of quantum physics/quantum engineering to technology is more important than the application of classical physics/classical engineering to technology. But it isn’t less important either. It’s complimentary and we should absolutely invest in it (a lot).

But the potential that quantum information has (quantum computing and other protocols) is different, and I think we need to find a better way of communicating to general audiences why academics are so excited about it, without leaning on conceptual paradoxes3 or resorting to hype. I’ll take a stab at this in an upcoming post (and if you know of some examples where people have done this well, I’d love to see them!).

I think if we don’t do this, we risk disillusionment. If people get excited about quantum technologies for the wrong reasons, and then discover that those reasons don’t stand up to scrutiny, then it will be (justifiably) difficult to convince them of the real value of these technologies.

Quantum Salon Insights is an independent reader-supported research project focused on making sense of the quantum tech ecosystem.

Here we explore the ideas, patterns, narratives, and structural misalignments that show up across quantum tech and adjacent deep-tech fields.

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I’m preparing a talk on communication challenges for quantum tech startups for the Quantum Education Summit in Barcelona next month, and I’ve been thinking a bit about the difference between communication and education.

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The question about communication vs. education comes up a bit actually. At Quantum Salon, we do deep tech communication, which sounds a bit like science communication, which overlaps with education. And so it’s easy to confuse what we do with what other companies—that focus on education and training within the quantum ecosystem—do.

So today, I wanted to spell out the difference, at least how I see it.

Communication vs. Education

Both communication and education involve a sender and a receiver of information: the communicator and the audience.

But education is for the audience. It’s about helping someone understand something new, build a skill, get better at it. While communication is for the communicator. It’s about helping a person (or a company) align people, change how they see things1, or get them to take action.

Sometimes both happen at the same time, which is why I think of them as two sides of the same coin. But when you’re trying to understand what a communication or education company does, you have to ask who they are serving: the communicator or the audience?

What does Quantum Salon do?

We work on the communicator’s side, helping deep tech startups get their message across so they can achieve their goals. Usually these goals are things like: get funding, hire new talent, keep investors happy, keep current employees happy, develop academic partnerships, get customers, etc.

Each kind of communication—marketing, public relations, management, technical, political, and so on—has its own area of expertise. Marketing speaks to customers, government relations speaks to policymakers, internal comms speaks to employees.

But for quantum tech startups, and deep tech startups more broadly, the challenge is the wide variety of stakeholders. Each has their own language, incentives, priorities, and biases. Our focus is on helping startups develop a communication plan so their efforts stay aligned rather than scattered.

Quantum Salon Insights is an independent reader-supported research project focused on making sense of the quantum tech ecosystem.

Here we explore the ideas, patterns, narratives, and structural misalignments that show up across quantum tech and adjacent deep-tech fields.

To receive new posts and support this work, consider becoming a free or paid subscriber.

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Earlier this month, it was Waterloo Tech Week, and I got to attend some really cool events. At one of them, I was chatting with someone from a venture studio, who brought up that one of the challenges with recruitment was that most people weren’t sure about the difference between incubators, accelerators, and venture studios. I thought it would be a good topic to touch on as we try to wrap our heads around the quantum tech ecosystem.

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As with everything, there is nuance and blurry edges in practice, but I think they key differences are that:

• Incubators are primarily spaces (and usually for very early stage startups)

• Accelerators are primarily structured programs (and usually for companies that are already formed)

• Venture Studios are companies that build startups from scratch

Let’s dig in a little more.

Incubators

Incubators tend to be spaces that are tied to a particular company or university. As the name suggests, incubators are for startups in the very early stages. The founders might have just an idea that is still forming, and the company might not even be established yet.

Incubators provide office or lab space, they give startups access to shared tools and facilities, and they let you hang out with other startups.

Incubators provide mentorship, networking opportunities, and resources, but typically they don’t invest money in startups.

Examples in the quantum tech space (with deviations from the description above noted in brackets) include:

• DistriQ in Sherbrooke, Canada (with other ecosystem support beyond incubation)

• Quantino in Quebec, Canada

• NQCC/QuBIC (Quantum Business Incubation Centre) in Oxfordshire, UK (also gives small grants and vouchers)

Accelerators

Accelerators are limited-term programs that usually last several months. They are typically for startups that already have a founding team and a core product. As the name suggests, the point is to accelerate the company to the next stage of growth.

Startups go through the program with a cohort of other startups, receive structured mentorship, hit various milestones along the way, and participate in activities like demo days.

Accelerators often invest capital in the company in exchange for equity.

Examples in the quantum tech space (with deviations from the description above noted in brackets) include:

• Duality Accelerator in Chicago, USA (doesn’t take equity)

• QAI Ventures Accelerator Program in Arlesheim, Switzerland, with a subsidiary in Calgary, Canada in collab with Quantum City

• ACET Quantum Startups Program in Sherbrooke, Canada (doesn’t take equity)

• CDL Quantum Steam in Toronto, Canada (doesn’t take equity, doesn’t give funding, accepts pre-incorporation teams)

Venture Studios

Venture studios are companies that create and launch startups. You can join a venture studio as a potential founder without a startup idea in mind, and the studio helps you build the company from ideation all the way to product and market.

The venture studio might pair you with another founder (e.g. bringing together one technical and one non-technical founder), and the studio itself becomes a co-founder too (the “third” founder model).

They profit primarily from equity in successful companies, similar to VCs, but with deeper involvement and due diligence.

Examples in the quantum tech space (with deviations from the description above noted in brackets) include:

• QV Studio in Sherbrooke, Canada (with accelerator-like [structured program] and incubator-like [lab space] elements)

• Roadrunner Venture Studios in Albuquerque, USA (with accelerator-like [accept formed companies] and incubator-like [lab space] elements)

• QAI Ventures Venture Building Program in Arlesheim, Switzerland

What should you do?

Some decisions you might want to make are between an incubator, accelerator, or venture studio. That will depend largely on the stage of your company and your reasons for wanting to be a startup founder. You might want to decide between one that specializes in quantum tech or deep tech more broadly, or even one that’s more general. If you need lab space and facilities, then probably something more specialized might make sense. But I think these aren’t decisions you can make in the abstract, for at least two reasons.

The first is that these programs vary the same way that schools and universities do. Some will be genuinely helpful and guide you in building a solid company while others will be more about “startup theatre”. Even though I’ve chatted with many people who run and have participated in these programs, I’m not yet as deeply familiar with each of them to comment on which is which. If there are some programs that look interesting to you, find out who has gone through them. Usually the companies are listed on their websites, and you can find the founders of those companies through LinkedIn. Get in touch with them and ask if they can share their experience with you. I think the important decision is between specific programs not categories of programs.

The second is your reason for wanting to join one in the first place. If your reason is that you have some vague idea that you want to commercialize your technology or start a company, but you don’t know what you don’t know, then it might not be the best idea to join any of them just yet. I would invest in getting to know people in the startup community first and learning more about what you don’t know. Once you know what you don’t know, you can make a better decision about whether one of these programs will help you fill those gaps, and whether what you give up is worth it (it very well might be, but you should know what you’re getting into).

The valley of death in deep tech is real, and accelerators, incubators, and venture studios can help quantum tech startups traverse this valley. They provide startups with resources and infrastructure that they wouldn’t otherwise have access to. I know many quantum founders whose companies would not be where they are today without the support from one these programs.

But as you’re doing your homework, also track down companies that have taken different routes. Get to know the entrepreneurial community around you, both where you live and in the industry you care about. Building a peer network of entrepreneurs will give you a better sense of what paths make sense for you, whether it’s a formal program or something less structured (👋 Builders Club!).

What about you?

If you’ve participated in one of these programs or are involved in running one, I’d love to hear about your experience. Let me know how it went in the comments, or get in touch with me if you want to chat about it off-line.

Venture capital (VC) is an integral part of the quantum tech ecosystem, so I thought it would be fun to do a bit of a dive into the mechanics of VC. I used to think of VCs as this mysterious group of people, who existed to give you money, if only you managed to impress them somehow. My guess is that other academics, who haven’t actively looked into what VCs do, might feel a similar way.

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As scientists, we're actually already used to mysterious groups of people who give us money. But I think the difference is that if you're an academic, surrounded by other academics, you can get quite far just by absorbing, through osmosis, best practices for grant writing.

The VC world is different to the world of government grants, and if you're a scientist considering entrepreneurship, you won't have years to spend in a like-minded community to internalize how to do things. You’ll need to get out there into the world, talk to people, and learn from them. A basic grasp of the mechanics of VC will give you a framework to make sense of what you’ll learn in those conversations (it might also make interpreting the quantum technology news easier!).

This basic framework is what we'll be looking at here.

What is a VC fund?

At the most basic level, a VC fund is a pool of money raised from outside investors. These outside investors might be pension funds, university endowments, state-owned investments funds, high-net-worth individuals, etc. Their goal is to get high returns, better than they could get in public markets like stocks and bonds.

In return for those high returns, the investors are happy to accept higher risk and to have their money locked away for a long period of time (several years, even over a decade). The investors who invest in a VC fund are usually called Limited Partners, or LPs for short.

The fund is usually managed by another group of people. These fund managers decide which companies to invest in, they sit on the boards of those companies, and eventually guide when and how to get money out of those companies and back into the fund, and ultimately, to the investors. These managers are usually called General Partners (GPs) or managing directors.

(Most) VC funds don't last for ever

When a group of GPs raises money for a fund, they make a deal with the LPs that the money will only be tied up for a set period of time.

Most venture capital funds have a 10 year term (9% are < 10yr, 72% are 10yr, and 19% are >10yr. While the term sets a fund’s intended lifespan, funds can have the ability to extend their term. The median fund liquidation time is now slightly over 14 years. According to recent data from Silicon Valley Bank, top-performing US VC funds now often take between 16 to 20 years to fully return capital to investors. One of the longest funds I've come across is Bill Gates' Breakthrough Energy Ventures, which has a 20 year term.

The fact that investors want to cash out within e.g. 10 years, makes VCs prefer companies with timelines shorter than that. That’s obviously a problem for deep-tech ventures with longer R&D cycles.

Incidentally, when I found these numbers, I was surprised that they didn’t really line up with what I typically hear, which is that software funds tend to have 5-7 year terms and deep tech funds tend to have 10-15 year terms. This is something I plan to dig into further for a future article.

There is also something called an evergreen fund, e.g. the Mars IAF, which doesn’t have a fixed term. I don’t know how prevalent these are, but I’ll also dig into it more in a future article.

VC fund life cycles

For the sake of illustration, let’s take a look at a typical lifecycle for a 10 year fund. These stages may overlap significantly, but can roughly be broken down into:

Fundraising (~Year 0–1)

GPs raise commitments from LPs. In this phase, GPs present an investment thesis (e.g., “AI-first enterprise SaaS,” or “early stage quantum technologies”), which is how funds differentiate themselves and signal their expertise to attract LPs who want exposure to specific sectors or different phases of a company's development. LPs commit some total amount of money, which they will transfer to the fund in chunks over the next few years.

Investment period (~Year 1–5)

GPs identify and invest in startups. As they pick companies to invest in, they're aiming for a set number, across different stages, with diversification by sector or risk profile. This investment period can last anywhere from 3 to 5 years, but often starts as soon as the GPs have raised enough committed capital to begin operations.

Portfolio management (~Year 3–10)

GPs start managing portfolio companies immediately after investing in them. They support companies through board seats, introductions, and follow-on investments. They also make internal decisions about which companies to double down on, and which companies to devote less to, in terms of resources.

Harvest/exit (~Year 5–10+)

GPs focus on “exiting” remaining portfolio companies to return capital to LPs. Returns are realized through acquisitions, IPOs, or secondary sales. Some funds can get 1-2 year extensions if they have promising companies that need more time to mature before exit, extending the harvest period.

For a more detailed breakdown of the various stages, check out this resource.

What's an exit?

When the time comes, GPs need to turn the investments in a certain company back into cash or liquid shares and distribute them to the LPs. That moment is called an exit.

Exits can take several forms:

In an acquisition, another company buys the startup outright, either for strategic technology, market position, or talent. This is the most common type of exit. In quantum tech, this route was taken by QuantumWise → Synopsys (2017), QxBranch → Rigetti (2019), Zurich Instruments → Rohde & Schwarz (2021), Quantum Benchmark → Keysight (2021), Entangled Networks → IonQ (2023), Oxford Ionics → IonQ (2025), and Lightsynq → IonQ (2025).

In an IPO (Initial Public Offering), a company lists directly on a stock exchange through the traditional IPO process. This involves extensive regulatory review and financial disclosure. For VCs, it provides liquidity because shares can be sold into public markets. One example in quantum is QuantumCTek who went public on the Shanghai Stock Exchange STAR Market in 2020.

SPACs and reverse takeovers are alternative routes to going public without a traditional IPO. In a SPAC (Special Purpose Acquisition Company) a private company merges with a publicly listed shell created to raise cash for such a deal. This was the route taken by IonQ (2021), Rigetti (2022), D-Wave (2022), and Zapata (2024). Horizon Quantum and Infleqtion also recently announced their SPAC plans. In a reverse takeover, a private company merges with an already listed firm that is not a shell.

Instead of waiting for an acquisition or IPO, a VC may sell its shares privately to another investor, such as a later-stage fund. This is known as a secondary sale. These transactions can provide partial liquidity before a company reaches a full exit. Interestingly, these kinds of sales are becoming more common, being up by 41% from the first half of 2024, with the introduction of new structures like Special Purpose Vehicles (SPVs).

Is every ending an exit?

Unfortunately, no. Companies can also come to an end in ways that don’t give investors their money back. These can also take several forms:

A shutdown is when a company stops operating altogether and closes down. In this case, investors don’t receive any payout and their equity is no longer worth anything. An example is Nordic Quantum Computing Group, which wound down in 2024, although they were not being managed by a VC firm at the time.

You can also have mergers without payout, where two companies combine for strategic reasons, but no money changes hands. Rather than cashing out, investors’ shares roll over into the new entity. This happened when Cambridge Quantum merged with Honeywell Quantum Solutions to form Quantinuum in 2021.

A company can also go quiet without officially shutting down, sometimes referred to as going dormant. Operations may stall, staff may leave, and new funding may dry up, but the legal entity lingers. Investors technically still hold equity, but there’s no realistic way to get their money back.

As a side note, you may have heard about Zapata shutting down. This happened after their SPAC IPO, so it wouldn’t have affected the VCs. Incidentally, it seems that Zapata is on their way to a restructuring, so it might not be the end of the story for them.

How to know if a fund is going well?

There are several metrics that LPs and GPs use to track performance. Two of the most basic are the multiple on invested capital (MOIC) and the internal rate of return (IRR), but there are several others as well.

The MOIC is a simple ratio of how many times the fund’s invested money has been multiplied. For example, a 3x MOIC means the fund has returned three dollars for every dollar invested.

The IRR is an annualized measure of the return which gives a better sense of the funds performance than the MOIC. Obviously a 3x return in five years is better (higher IRR) than the same 3x stretched over fifteen.

For deep tech, long R&D cycles make it harder to deliver the quick returns that drive a high IRR. This is another thing that specialized deep tech firms would be more understanding of.

Who you’ll meet at a VC firm

General Partners aren’t the only people you’ll encounter when dealing with a VC. Firms have layered teams, which might look different depending on the firm, but usually look something like the following.

At the top are the GPs, who raise money and make final investment decisions. They typically reach that level by founding their own fund, being a successful entrepreneur first, or climbing the ranks internally.

Below them are principals or VPs, who oversee daily deal flow and diligence and usually have several years of venture or operating experience.

Associates and analysts sit at the bottom rung, ranging from recent graduates to MBAs. They handle due diligence (research, market maps, memos) and sourcing (finding startups through networks, programs, or scouting online).

For a deeper dive, check out this resource.

Don’t forget, VCs are people too!

This should be kind-of obvious, but it’s worth mentioning in a “framework” article like this one. Sometimes it’s too easy to think of a group of people that you’re unfamiliar with as being a bit homogenous. But, like in many industries, people end up in venture capital from many different backgrounds: finance, economics, business, academia, etc.

Some have been in VC for a long time, while others are early in their careers and still figuring it out. Some genuinely love what they do, while others are burnt out and having a bad time. Like the rest of us, they have different life experiences and biases, and personal lives that influence their mood on any given day.

This means you can’t rely on an article like this to tell you what VCs are like and how to make an impression on them. You have to build your own picture over time by talking to lots of people, including the VCs and investors themselves as well as other founders who have pitched to them.

I really like Derek Sivers’ echolocation metaphor for what I mean here. You need to have conversations, sending out questions and ideas, and learn from the echoes that come back. Hopefully the framework in this article will help you make better sense of those echoes.

Communication for deep tech startups is tricky. The obvious challenge is the complexity of the technology. Quantum computers are harder to explain than food delivery apps, right? And so the standard advice is to abstract away the technical details and focus on customer problems. But in deep tech, that’s easier said than done. And even when it’s possible, it’s often not enough.

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The complexity in deep tech doesn’t just make explanations harder. It brings in a wider range of stakeholders, each with their own language, incentives, priorities, and biases. It also stretches development timelines, creating a long gap between promising research and viable products. This gap sometimes goes by the memorable name the valley of death. In the valley of death, competition for resources is more fierce and you have to stretch further to reach what each group of stakeholders cares about, creating its own communication challenges.

To understand how the valley of death comes about, let’s look at what makes deep tech startups different from regular tech startups. But before we do that, we should know what makes startups different from small businesses.

So let’s start there.

The difference between a startup and a small business

Most people I talk to seem to think that any new company is a startup. But it's useful to make a distinction between companies that grow organically and those that grow inorganically. There was a great discussion of this in the first episode of the The Startup Podcast where they gave the following definitions:

• A small business is designed to grow organically while a startup is designed to grow inorganically.

• A small business is designed to earn revenue and spend it while a startup is designed to raise capital and burn it.

Another way to think about it is that for a small business, there is a fairly linear relationship between capital investment and profit, while for a startup, the relationship is highly nonlinear. If things go well, the nonlinearity contains an inflection point that yields a hockey stick curve.

This distinction is important because it affects how you should run your business. If you're a small business and you operate like a startup (or vice versa), things might not work out as planned.

Incidentally, Quantum Salon is a small business, not a startup.

Two ways to burn capital: regular tech vs. deep tech

So what does it mean to burn capital? Is it just a fancy way to say "lose money"? Not at all. Burning capital is a conscious decision to spend money on growing your business inorganically until it reaches the point where it can generate profit.

Here are two scenarios in which this makes sense.

1. The first is when your product is built using software and relies on a huge number of users to generate profit. With software, the more people you serve, the cheaper it is to serve each person and you're able to create outsized returns at scale. But while you grow your user base, you need to offer your product at a loss. So you have to burn capital until you reach scale...that is, until you have enough users to generate profit.

2. The second is when your product relies on technology that is extremely complex, and takes a long time and a lot of money to develop and commercialize, but will be very valuable once it exists. You might not need scale effects to generate profit, but you do need a product. So you have to burn capital while you develop and commercialize the underlying technology.

The first approach is how most regular (aka traditional, aka Silicon-Valley style) startups work. The second approach is how deep tech startups work.

So why does this distinction matter?

In the regular startup world, the recommended approach is to build your company around a problem that needs to be solved, not around a product or a technology. There are sound business reasons for taking this approach, and this approach is completely feasible when solutions use well-developed technology like software. Once you've identified a problem to solve, you have a plethora of software tools and experts to develop the solution in a reasonable amount of time.

This isn't the case for the kinds of technologies being developed by deep tech startups. The underlying technology is usually developed during one or multiple people's PhD degree/s, possibly based on years of prior work in the PI's lab, then developed even further outside of the lab on its way towards commercialization. It's usually not practical to start from a problem and then ask someone to spend 20 years developing a deep-tech solution to it. Instead, deep tech startups start with the technology, then commercialize it (i.e. develop the product and move it to market). The terminology for this is that deep-tech startups are technology first.

Technology first, however, doesn't mean technology only. To success, every deep tech startup must identify real problems their technology can solve and develop viable business models around those solutions. The difference is in the process: they start with the technology, then find problems it can address, rather than starting with a problem and developing a technology to solve it. These are drastically different approaches to product development, and you should know which camp you're in so you're taking the right approach.

The valley of death

The technology first scenario presents a unique challenge for deep tech founders. They often take their technology out of the university setting well before they even have a product, let alone product-market-fit (PMF). Most investors naturally don't want to invest in startups where they don't see a clear path towards a timely return on their investment. The gap between university resources and commercial resources is sometimes referred to as the deep tech valley of death.

While deep tech startups should be working towards a product and product-market-fit from the beginning, the reality is that it can take years to get there, and in the meantime, those companies need funding and support.

In the deep tech world, the investors who are willing to fill this gap as sometimes referred to as patient capital or patient investors. These are investors who understand the longer development timelines and are willing to wait for returns. This might include government grants, strategic corporate investors, or specialized deep tech VCs who have experience with these extended development cycles.

But unfortunately, there isn't nearly enough of this support out there, as indicated in the image above, which presents deep tech startups with some unique challenges.

Why this makes communication tricky

While a regular startup can focus almost entirely on communicating with customers and investors, a deep tech startup also has to consider scientists, hardware engineers, academic partners, corporate partners, governments, and the public all at once. Each group comes with its own language, incentives, priorities, and biases. What excites one group can bore or even alienate another.

If you’re far from product-market fit, the stretch to reach investors who are used to funding post-PMF companies can pull you away from your scientists. Meanwhile, the scientists that are an integral part of your R&D can be difficult to align with company objectives. And the patient investors, who are most aligned with your timeline and resource needs are few and far between, which makes reaching them a different kind of communication challenge.

Figuring out what users want is harder when the product is still years from being developed. Even with an existing product, talking to potential customers can require navigating a great deal of technical complexity, especially for B2B or B2B2C companies whose customers are also working in deep tech industries.

Bringing in external help with communication, marketing, or go-to-market adds the extra step of getting them up to speed on the technology if they don’t already specialize in your field. And the technical people in the company who could do that onboarding often find it difficult to translate their knowledge for non-experts.

Beyond all of this, the road is long, and startups also need to work on reducing the size of the valley of death to create future opportunities. That means educating post-PMF-minded stakeholders on the value of investing in pre-PMF technologies. To complicate it further, they need to know when it makes sense to educate stakeholders on what they should value, and when it’s better to meet them where they are and appeal to what they already value. Getting this wrong leaves you talking at cross purposes and undermines your goals.

What to do about it

So how do you deal with this complexity? You need to start by taking stock of where you are in relation to the valley of death. What is your plan for getting through it? Do you need to pivot, focus on reaching patient investors, or try something else? What are your short-term and long-term goals? Which stakeholders matter most for those goals, what do they care about, and what might alienate them? Decide whether this is the moment to educate them on what you think is important, or do you need to meet them where they are for now?

Don’t just focus on the technology, but think about the big picture. Define a vision that’s compelling but doesn’t slip too far into overhype. Tailor your language and distribution to each audience, but keep the core message consistent. And most importantly, make sure everyone inside your company understands and buys into that strategy, because without alignment internally, you risk losing the trust and commitment of your own team.

Authors note: part of this article was adapted from the author’s 2022 article entitled Quantum computing startups are not like regular startups.

Quantum computing isn't the only technology being developed that takes advantage of quantum mechanics. You've probably come across some others as well, like quantum sensing, quantum cryptography, and the quantum internet.

It's useful to have some map of how all of these technologies fit together. I've tried to make one in the past, but I was never entirely satisfied with it. I've seen others do it as well, and I think there are some great infographics out there (see e.g. here), but somehow they don't achieve what I'm trying to get at.

A common approach, which I also initially adopted, was to break things into quantum sensing/imaging, quantum communication, quantum computing, and post-quantum crypto.

My problem with this is that I'm not sure what insight is supposed to be gained from this categorization. It presupposes some kind of lens, but that lens is not explicit. And various things within each category are there with caveats.

I want to try a different approach, which may be more rudimentary, but might be more flexible. The approach is to categorize the technologies along different dimensions (or axes). These axes might take on a binary classification or a spectrum. For each axis, one would also need to identify some correlated factors, to help with gaining new insights and decision making.

The ultimate goal is to have a set of mental models, a decision-making framework of sorts, that is appropriate for your lens (investor, government official, job-seeker, etc.) and for your goals.

A tall order indeed! But let's see how we go.

For simplicity, we can start with two axes. The two I find most natural to start with are: does the technology use quantum physics, and does it need quantum information?

A note on quantum information vs. quantum physics

Quantum information is fundamentally different from classical information. If you’re reading this, you’ve probably heard about classical bits vs. quantum bits (qubits). That discussion is about how the information is represented. There are also differences between how classical and quantum information can be processed, and also how we can read out the result of that information processing.

The representation, processing, and readout of quantum information is fundamentally tied to the way quantum physics works, but it’s possible to represent, process, and read out quantum information using classical physical systems (for example when a student completes a problem set from their quantum information course using pen and paper...as we did in the old days!).

The limitation is that if you’re using classical physical systems (even classical supercomputers) to model quantum information, you run into scaling issues and quickly reach the limits to how complex a quantum information system you can model.

Such limitations aside, we can think of quantum physics and quantum information as separate axes, which gives us four categories to explore.

A note to the experts

If you’re a practitioner and, as you continue reading, you think I’m glossing over some critical nuances which put your technology into a different category below, I really welcome this feedback.

For example, I’m probably glossing over the fact that these days, quantum error correction is being applied to areas outside of traditional quantum information domains (e.g. quantum sensing). This might need to be taken into account.

But I also don’t think the framework will be very useful if everything ends up in the same box. What I’m going for here is something along the lines of that famous quote:

“All models are wrong, some are useful.”

So please give me your feedback, but please frame your feedback in the broader context of making this a more useful framework.

With all those caveats, let’s go!

Quantum physics: yes & Quantum information: yes

Here we have technologies that use quantum physical systems to represent, process, and read out (measure) quantum information. From a practical perspective, we also care about sending that quantum information from one physical location to another, so we need to encode that information in quantum systems that are well-suited for transmission (usually light).

Universal quantum computers are the most well-known example. There are many physical systems currently being developed commercially. These include superconducting circuits, ion traps, neutral atoms, light (photons and quantum modes), and spins.

Quantum networking refers to connecting different quantum computers together. To do that, you need to encode quantum information in a physical system that’s easy to move around (usually a photon or a quantum mode of the electromagnetic field). Over short distances, like in a quantum data centre, this is usually known as quantum interconnects. Over long distances, this is usually known as the quantum internet.

Quantum cryptography uses quantum information protocols to enable secure communication. This includes quantum key distribution (QKD), which allows two parties to generate a shared secret key that's secure against any eavesdropper. Another application in this space is quantum random number generation, which uses quantum physics to generate true randomness for cryptographic systems.

Quantum physics: yes & Quantum information: no

Here we have technologies that use quantum physical systems to perform various tasks better than would be possible otherwise, but that don't require quantum information concepts to be developed. Some readers might object to my categorization here, because practitioners often do use quantum information concepts when working on these technologies (because quantum information is a great unifying language for translating between different quantum physics scenarios), but the point I am making is that they don't need to.

Quantum annealing is a form of computation that is more specialized than universal quantum computation. This is one of those categories where people often use quantum information concepts (D-Wave has qubits, right?). But you don't actually need quantum information concept to make quantum annealers work. What you're really doing is mapping classical optimization problems to quantum physical systems and then letting them evolve, according to the laws of quantum physics, to their lowest energy state.

Quantum simulation gets a little confusing because different communities use different terminology. What I'm talking about here is using one quantum physical system to model another quantum physical system. For example, you might use a trapped ion system to simulate the behaviour of electrons in a high-temperature superconductor. Physicists call this quantum simulation (for the quantum-industry usage of "quantum simulation", refer to quantum emulation in the next section). Again, people often use quantum information concepts to make the mapping between the different systems, but they don't have to. Quantum physics concepts are enough.

Quantum metrology/sensing is another confusing one in terms of terminology. While the terms metrology and sensing are well-defined and distinct, as far as I can tell, folks in the quantum community use the terms quantum metrology and quantum sensing interchangeably for the most part. This category can be broken down into several subcategories. Quantum timing for precise timekeeping, using systems like Cesium fountain clocks. Inertial or field sensors for sensitive measurements of magnetic and electric fields, gravity, rotation and acceleration. Examples include NV-diamond magnetometers and atom-interferometer gravimeters. Quantum imaging for better resolution, sensitivity, or reduced noise, using properties of quantum light like squeezing and entanglement. Quantum radar and LiDAR for remote sensing (quantum radar uses microwaves while quantum LiDAR uses light).

Quantum physics: no & Quantum information: yes

Here we have technologies that deal with quantum information but don't rely on quantum physics.

Quantum emulation (aka quantum simulation in industry) is where you use classical systems to reproduce the computation results of a quantum computer. These classical systems can be regular desktop computers as well as more advanced supercomputers and GPUs (or, as mentioned above, they could just be pen and paper!). Ignoring the technicality that everything in the universe is ultimately quantum in nature, these systems don't use any special properties of quantum physics to perform computation of the quantum information.

Quantum-inspired algorithms are classical algorithms that were developed by people thinking about quantum computing problems, but they run on regular computers. They take ideas from quantum algorithms and adapt them for classical systems. I wasn't sure about whether to put this here or in the next category, but decided to have it here, because by definition, these classical algorithms could not have been developed without quantum information concepts.

Quantum physics: no & Quantum information: no

Here we have all the classical technologies that exist in the quantum ecosystem but don't themselves use quantum physics or process quantum information.

Post-quantum cryptography is the use of classical cryptographic algorithms designed to be secure against attacks from quantum computers.

Classical support technologies includes dilution fridges that cool quantum systems, control electronics that manage quantum devices, orchestration software that coordinates quantum experiments, and the specialized manufacturing tools needed to create superconducting circuits, trap ions, or grow crystals with specific defects for quantum sensing, etc.

Putting it all together, we get the following chart:

How is this useful?

Say that you're an investor and you care about risk. You could make the case that technologies that use quantum physics are riskier to develop than those based on classical physics. You could also make the case that technologies that use quantum information are riskier than those that don't.

Furthermore, the nature of that risk could be different for each axis. You could make the case that for technologies that use quantum physics, progress will be more smooth because the challenges are mostly engineering. On the other hand, for technologies that use quantum information, progress could require some step-function jumps, for example, for new quantum algorithms to be developed. Then again, the discovery of a new quantum algorithm or quantum error correction code could be an inflection point in terms of progress in the field.

Now say you're someone wanting to get a job in the quantum tech industry. Maybe you're coming from academia. Knowing that there are quantum technologies that don't require quantum information expertise is useful. I think many quantum physics PhDs think that quantum computing is their only option, and they feel blocked by the fact that they're not experts in quantum information. This framing helps candidates focused their efforts on subsets of the industry.

Beyond these two axes

There are other axes that are interesting for categorizing quantum technologies. We could for example explore hardware vs. software, analog vs. digital, component vs. system. We can then identify interesting correlated factors.

But we can also go beyond technology. While I don't think we're at the stage in quantum tech where we can abstract away the underlying technology, technology is not the only important thing when assessing a company. So we could also explore axes like business models (B2B vs. B2C) or development time lines (near-term vs. long-term).

Like any categorizations, these will have limitations, but combining multiple axes gives you a richer picture than a single framework can provide. I think what's important is understanding what you're trying to achieve and picking the right lens for your particular context.

A fun immediate thing to do is to take the existing two axes presented here, and start putting companies on the diagram. You can start to do that yourself if you like. If you discover anything interesting, let me know. I will likely do it in an upcoming post too.

What do you think?

Does the categorization above need tweaking? Can you think of any other interesting dimensions to consider? Let me know in the comments!

Quantum, the industry, is an interesting beast. Just like the scientific theory from which it evolved, the industry is full of perplexing questions and apparent paradoxes.

Why do some people think that quantum tech is underfunded while others are surprised it gets as much funding as it does? Why are quantum companies having trouble hiring, but people who want to get into the industry can’t get jobs? Why do some people say that quantum computers won’t be useful for at least a decade while others say we’re already in the era of quantum advantage?

These apparent paradoxes arise because quantum, the industry, brings together people from all walks of life: academia, software development, design, business, government, and more. It’s a fast-growing industry, but it’s still relatively small and tight-knit. This diversity and closeness leads to remarkable collaboration but it can also lead to tension, as people interpret the world through their own lenses, and then those interpretations clash.

Resolving such paradoxes, therefore, lies in understanding all the players in the ecosystem. Understanding the skills they bring, the challenges they face, the incentive structures in which they operate, and how all of this shapes their world view.

But before we can do that, we need to know who these players are, what role they play, and how they interact. This is the purpose of this post. We’ll also make some progress towards resolving the apparent funding paradox.

A note on tech ecosystems

I usually see people discuss the quantum tech ecosystem in terms of the different kinds of quantum technologies being developed (computing, sensors, cryptography, etc.). This is a really interesting topic and we discuss it in other posts, but here I want to focus on the entities that come together to develop the technologies, rather than the technologies themselves.

I was introduced to this way of thinking in a post on tech ecosystems by Joseph Darko. It’s a great article, with a focus on what makes tech ecosystems healthy. I’ve modified his framework to suit our ecosystem, but I highly recommend reading his original article as well.

The quantum tech ecosystem

Let’s begin at the source: universities and research institutes. This is where most of the foundational quantum technology starts. It begins as research in a lab. Usually it’s a collaboration between professors, postdocs and students. As the technology matures, the team might get the idea to commercialize it.

The commercialization process usually takes the form of a startup. Often, there will be relatively tight coupling between the startup and the university, with exchange of resources between the two, as shown in the diagram below.

Intellectual property flows from the universities to the startups, as do people. Universities sometimes also provide lab space during the early stages of company formation. But there’s also a flow of resources from startups back to universities. Startups might sponsor research, provide internships for students, or offer real-world problems for academic researchers to work on.

If startups get the chance to grow up, they become established companies. Some established companies conduct their own research & development in quantum tech. But established companies also play a crucial role in the ecosystem that's often underestimated. They provide secure employment for technical people graduating from universities. Without secure and reliable jobs for people after graduation, these graduates might move to other towns or other countries. Established companies are a crucial element in creating a vibrant local tech ecosystem.

Established companies can provide resources and support to startups. Other times they might decide to buy them. Maybe they want the technology that's in the startups, or maybe they want the people. It's actually really difficult to get good people that work well together on a particular area of research, so sometimes established companies see a well-functioning team and decide they would really like to have that team in their company.

Of course, none of this would be possible without money. So investors are a really important part of the ecosystem. You can have VCs (venture capitalists), private equity firms, or angel investors. These give startups money, which startups need to operate, but in turn the startups provide the investors with opportunities to make more money. This is a symbiotic relationship.

Investors also interact with established companies. They might share information, people might flow between them, and they might support each other in various kinds of partnerships.

Another way that investors support startups is through accelerators and innovation hubs. Accelerators and innovation hubs help startups with funding, infrastructure, mentorship, and access to relevant networks. By supporting accelerators and innovation hubs, investors can find out about the most interesting companies to invest in.

Government also plays an important role. They set the agenda for where the ecosystem should focus, and they do that by deciding where they want to provide funding. They might fund startups in certain ways, they fund universities, they fund certain research programs, and provide tax credits for companies. There is also a flow of people. People who work at universities or other parts of the ecosystem can also go on to become program managers or program directors in government agencies.

And finally, there are community groups. Community groups organize hackathons, meetups, and so on. They add dynamism and provide opportunities for young people to get involved and connected to other parts of the ecosystem.

The quantum tech ecosystem is an interconnected and interdependent network of diverse entities coming together to spur innovation in quantum technologies. As with ecological ecosystems, each entity is needed for the quantum tech ecosystem to thrive.

Side bar: If you prefer video, you can watch me discuss the quantum tech ecosystem here, through the lens of careers in quantum.

Resolving the funding paradox

With this framework in place, let’s take a look at the question of funding, and why some people think it's too much and others think it's not enough.

Funding in academia is a scarce resource and the competition for that limited funding is fierce. The amount of funding that many academics see is quite small compared to the money that gets passed around in industry and the startup world.

Academics are also a lot closer to the science and technology. They have a really good sense of what it takes to develop the technology, and they have a really good sense of the state of the art. When they read claims in non-academic outlets, about what quantum tech is supposed to achieve, they interpret it through their technical lens, and see it as hype.

Now let’s take a look at established companies. When established companies invest in R&D, it isn’t usually about immediate financial returns. It's about positioning themselves for potential future advantages and signalling innovation to customers, partners, investors, and potential future hires. Being seen at the forefront of emerging technologies can be valuable beyond immediate returns.

What about investors? Most investors invest in many different kinds of technologies, not just quantum. VCs typically bet that most of their investments will fail, but a few will succeed so dramatically that they more than make up for the other failures. By design, their portfolios contain companies with different levels of risk and different expectations of reward (ideally reward and risk are correlated, but not always). Quantum companies might be high risk, but they sit alongside other companies whose risk is lower.

So from an academic’s perspective, quantum is getting so much more money than similar-but-not-quantum areas of research, and the technological challenges are enormous. They therefore think that putting this much money into a technology that is far from mature is incredibly risky. On the other hand, from the perspective of enterprise companies and investors who are investing in multiple innovative technologies as a whole, the amount of money being put into quantum is relatively small (say when compared with AI), and it’s part of a larger diversified risk portfolio anyway.

What about government? Governments are interested in more than just the technology. They are interested in creating vibrant technology ecosystems in order to grow the economy, create jobs, and make them competitive against other countries. So one of the ways they prioritize funding is in favour of technologies where there’s a good chance that all of the pieces can come together into a vibrant ecosystem.

Quantum has a certain je ne sais quoi, which makes people want to get involved in it. This generates momentum, making it an attractive ecosystem for governments to invest in. Even if some specific technologies don't work out as planned, the ecosystem will still have value because it can be leveraged in other ways. Incidentally, this is why you see so much news about new partnerships being formed. Governments like to see partnerships as evidence that the ecosystem is functioning well.

Different governments likely differ on whether they think quantum is under- or over-funded, but whatever side they land on will depend on more than just the viability of specific pieces of technology.

Looking forward

As it always turns out, there are no quantum paradoxes. You just need the right framework to see the different perspectives. This is true in quantum theory and it’s true in the quantum ecosystem.

For the question of over- vs. under-funding, it depends on which lens you’re viewing the situation through. But it also raises another question of whether it’s possible to make a global statement about the right level of funding, a statement that takes the whole ecosystem into account. That’s an interesting question. One I don’t have the answer to, but might get a chance to explore someday.

In future posts, I plan to cover the other apparent paradoxes mentioned in the introduction, as well as any others that come up. I also plan to dive deeper into how different players in the ecosystem see their roles, and how this influences how they act. In the mean time, I hope this framework will help you answer some of your own questions.

Finally, this framework is a work-in-progress, so if you think there are some pieces missing, or you’d like to suggest some other modifications, I would love to hear from you.

If you're new to the world of quantum tech, you might be feeling a little bit overwhelmed. Not just with the complexity of the technology, but with the amount (and variety) of news covering developments in the ecosystem.

Breakthrough announcements seem to be dropping constantly. Funding rounds are in the headlines. New partnerships are getting announced left, right, and center.

And if you're like most people entering this field (or even if you’ve been in the field for a while!) you're probably wondering:

What does it all mean? How do I know what actually matters?

It turns out, like with most tech news, news about quantum technologies serves many purposes beyond just informing you about technical progress. Understanding who's communicating what to whom (and why) will help you cut through the noise and focus on what's genuinely significant for your interests and goals.

The quantum news ecosystem isn't just about the science

Quantum technologies operate at the intersection of cutting-edge research, venture capital, national security, and intense international competition. This creates a unique information environment where the same development can be presented very differently depending on the source and audience.

Consider an example: a company announces improved quantum sensor sensitivity. This single development generates multiple stories. The academic paper focuses on the experimental methodology. The company press release emphasizes the path to commercial advantage. The government report highlights national competitiveness implications. The investment newsletter talks about market timing.

None of these perspectives are wrong, but they're serving different audiences with different needs.

Your job as a reader is to understand which lens you're looking through and what the speaker is trying to accomplish.

A framework for understanding quantum news

Here's a practical way to categorize the quantum technology news you'll encounter.

Technical research announcements typically come from universities, national labs, or company R&D teams. These are usually about fundamental advances like better quantum error correction, new types of quantum devices, or novel quantum algorithms. The timeline to practical impact is often measured in years or decades, but these developments form the foundation for everything else.

Product and platform news comes from companies ready to sell you something today. This includes new quantum cloud platforms, software development kits, quantum sensors, or quantum communication systems. The timeline is immediate, but the practical capabilities may be limited to specific use cases or early adopters.

Business and market updates cover funding rounds, partnerships, and strategic announcements. These stories are often more about positioning and market dynamics than technical capabilities. A major partnership announcement might signal that a quantum technology is becoming more mainstream, even if the underlying technology hasn't fundamentally changed.

Workforce and education news has become increasingly prominent as the industry faces talent shortages. This includes job market reports, educational program launches, professional certification announcements, and skills gap studies. With these shortages, workforce development is now a strategic priority across the industry.

Policy and international strategy includes government quantum initiatives, export controls, and competitive positioning between nations. These stories often involve massive funding announcements, national security implications, and cross-border collaborations or restrictions. The timeline is typically long-term but the geopolitical stakes are high.

Infrastructure and ecosystem development covers the building of quantum innovation hubs, supply chain initiatives, and the physical infrastructure needed to support the quantum economy. This includes everything from new fab facilities to quantum cloud access programs that democratize the technology.

Reading between the lines

Every quantum technology announcement has multiple audiences, and understanding this helps you extract the right information for your needs.

When a startup announces a funding round, they're not just telling you they raised money. They're signaling to potential customers that they're stable and growing. They're telling competitors that they're a serious player. They're reassuring employees that jobs are secure. And they're laying groundwork for future funding rounds.

When a tech giant announces a new quantum service, they're not just launching a product. They're establishing themselves as the platform other companies will build on. They're attracting developers to their ecosystem. And they're signaling to the market that quantum technologies are moving from research to early commercial applications.

When a government announces a national quantum initiative, they're not just funding research. They're signaling to allies and competitors that this technology is strategically important. They're encouraging domestic industry development. And they're often responding to similar initiatives from other countries.

Practical tips for quantum news consumption

Follow the timeline clues. Research breakthroughs typically have 5-15 year timelines to practical impact. Product announcements are about capabilities available today (though often limited). Business news is about market positioning for the next 1-3 years. Workforce and education initiatives can take 3-10 years to show results.

Pay attention to the source. Academic papers undergo peer review and tend to be more conservative in their claims. Company press releases are marketing materials. Government reports often emphasize strategic implications over technical details.

Look for independent validation. The most significant developments usually generate coverage from multiple independent sources. If only one organization is reporting a "breakthrough," approach with healthy skepticism.

Understand the competitive landscape. Quantum technologies involve intense competition between countries, companies, and technological approaches. This competitive dynamic influences how news gets framed and what gets emphasized.

Focus on trends, not individual announcements. One company claiming a new record might be an outlier. Multiple companies achieving similar results suggests genuine progress.

What actually matters for different goals

If you're considering quantum technologies for business applications, focus on product announcements from established players, partnership news between quantum companies and enterprises in your industry, and use case demonstrations that solve real problems.

If you're interested in investment opportunities, pay attention to funding patterns, market size estimates, and news about talent movement between companies. Also watch for government initiatives that signal long-term market support.

If you're pursuing academic or research interests, prioritize peer-reviewed publications, conference presentations, and collaborations between different research groups. The most important developments often appear in technical journals before they hit the business press.

If you're thinking about career opportunities, watch for hiring announcements, new lab openings, educational partnerships, and certification program launches. The quantum talent market is incredibly active, with shortages creating opportunities across many quantum technology sectors. Pay attention to workforce development initiatives and skills gap reports to understand where demand is heading.

Making sense of it all

Quantum technology news can feel overwhelming because it's not just about science. It's about business, geopolitics, and the future of technology itself. The key to navigating this landscape is understanding that different sources serve different purposes and different audiences.

Don't expect to understand everything immediately. Even experts in the field have to constantly learn about new developments outside their specific area of expertise.

What matters is developing the ability to identify what's relevant for your interests and goals.

The quantum technology industry is still in its early stages, which means there's genuine uncertainty about which approaches will succeed and which applications will prove most valuable. This uncertainty creates both opportunity and complexity in the news landscape.

By understanding who's communicating what to whom (and why), you'll be better equipped to separate signal from noise and focus on the developments that truly matter for your journey in quantum tech.