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.

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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.

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

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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.

Subscribe now

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.

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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.

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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.

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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?

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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.

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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.