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Podcast with Daniel Volz, Co-founder and CEO of Kipu Quantum

Daniel Volz, co-founder and CEO of Kipu Quantum, a software company working to accelerate the delivery of quantum advantage is interviewed by Yuval Boger. Daniel and Yuval talk about what Daniel learned about quantum at McKinsey and BASF, their optimized approach to quantum algorithms, the expected timescale for quantum advantage and much more.


Yuval: Hello, Daniel. Thank you for joining me today.

Daniel: Hey, Yuval. Very glad to be with you today and thanks for having me.

Yuval: So who are you and what do you do?

Daniel: I’m Daniel. I’m a chemist by training. Recently turned CEO. It’s a quantum computing startup, and we actually want to bring early quantum advantage to industrial end users by being application-specific and hardware specific.

Yuval: In terms of background, I think that you were originally at McKinsey and then at Bosch doing some or a lot of quantum in both of these locations. What have you learned about the needs of users and quantum technology in your journey to Kipu Quantum?

Daniel: Yeah. So you’re correct. I worked with quantum in my past two jobs already at McKinsey and BASF actually, a large chemical company in Germany. What did I learn? So I think the first learning, and this was mainly drawn out at McKinsey already, where I looked at quantum in and across industry perspective, was mainly this massive economic potential. So back then, I’ve been looking at all kinds of different industrial users from very different industry verticals like finance, logistics, chemicals, of course, which is my home industry still, pharmaceuticals, manufacturing, et cetera. And essentially, no matter where we looked, you could really identify these disruptive use cases. And from that point on, it was very clear that there is this massive value potential that quantum computing, if it becomes mature enough, can actually bring to this industry verticals.

What I learned at BASF, specifically where I built up the quantum effort together with a great team of colleagues, is that the users are really incredibly diverse. So if you think about all the different kinds of things you could do with quantum, at BASF alone, we found about 100 or so different use cases. Users have extremely diverse backgrounds. So I think this is always something to keep in mind. Of course, mostly, they have a problem, they want it solved, but this can be people doing controlling, and so it’s just people with a bread-and-butter business background.

It can also be a theoretical chemist, and in some case, even quantum physicists who have just been hired recently by a lot of the advanced users. So whatever product comes online needs to cater to these needs. And maybe a third learning, and this was somewhat sobering at BASF, is that quantum advantage with all the credentialed approach is, I think, somewhat out still because there is still a lot of work to be done both on the hardware as well as the algorithm level to make this great potential a reality and to start actually doing something meaningful.

Yuval: I read the press release of your funding. Congratulations, by the way. And I think it mentions that you’re claiming that you could deliver quantum advantage sooner. On the one hand, I’m very happy to hear that, that quantum advantage could come sooner. And on the other hand, I’m very curious. How can you do that?

Daniel: So if you look at today’s hardware, we have a number of qubits. It depends on the system in the order of maybe 100 if we’re just considering gate-based quantum computing, give or take. Some platforms less than 100. If we’re adding annealing, we’re in the thousands more or less. All of the aforementioned approaches, if you’re targeting fully photonic quantum computing, you really need a number north of a million qubits to do something meaningful because you will want to have error correction, you will have to have massive overhead of physical qubits that are going to be busy forming logical qubits to essentially prevent these error issues. And the reason for that is that the current quantum computer sizes are small and have also a lot of noise.

But we realized, and this is mainly the work of one of my co-founders, Enrique Solano, who is a former professor with more than two decades of research in quantum computing, essentially. Essentially, you can shrink down these numbers and these requirements if you’re actually hardware specific. So what we’re sacrificing in order to deliver on that plane is the notion of having a hardware-agnostic algorithm. This was more or less the talk of the town when I started my own journey in quantum back in 2017, 2018. Everybody was claiming that hardware-agnostic algorithms are the thing to get, and if industry users would have that, they could pay for it today, and then they would just… We need to wait until the hardware catches up, and then they can essentially start solving problems.

We think that this is not going to work because even if the number of qubits is doubling every year, it’s still going to take a while until we are north of one million qubits. So that’s not something for the near term. And instead, our technology is essentially geared towards the different algorithms that are very specific towards a certain problem and adjusted towards a specific hardware setup. I guess a certain hardware machine, actually, to be precise. And with that, we can more or less cut away some of the overhead. We don’t require error correction, and we can essentially start solving meaningful problems much, much sooner than the fault-tolerant quantum computing paradigm.

Yuval: And this specific hardware that you mentioned, is it still a gate-based computer, or is it an analog computer or is it a quantum-inspired algorithm? What kind of hardware are you anticipating running this on?

Daniel: So we are partnering up with the hardware manufacturers working on gate-based quantum computing. One of the two technical columns we’re standing on is literal analog quantum computing. So it’s essentially a mix between analog and digital quantum computing. We are using native gate operations. We are using analog building blocks. That, of course, requires some more tinkering in our algorithms, but we think that it’s actually worth it because doing so really cuts away a lot of overhead. And right now, hardware is great, and there’s super cool improvement happening every year essentially, but we are still limited by the number of gates we can run. Saving there is I think, really worth it because it can bring quantum advantage earlier to the present day.

Yuval: You mentioned chemistry, and I think VQE is one of the popular algorithms in chemistry. So if you take VQE and you make it, as you mentioned, hardware specific to a particular machine, how much of a saving can you get? Is it 10% fewer gates? Is it ten times fewer gates? Can you give me an order of magnitude there?

Daniel: It kind of depends on the problem. It can be several orders of magnitude, depending. I think what’s also relevant to understand is beyond the digital analog element, we have also something else, which is called digitized-counterdiabatic quantum computing. So that’s more or less a digital-only compression tool, which will take an algorithm like VQE, for instance, and indicate and cut away the number of required gate operations as well as lower the amount of classical pre-processing overhead to essentially get the problem done. So it’s really orders of magnitude, and if you translate this into how much sooner for industry users, it’s essentially many years. Of course, depending on the problem size and the pace at which the hardware development is going to progress.

Yuval: Now that reduction in the number of gates or the resources in general, is that done automatically? I’m thinking about the recent Nature article where AlphaTensor found a better way to multiply matrices. Is that the sort of work that you’re doing, or is it more of manual adjustment to the algorithm and going in to say, “Well, if I have this hardware and this algorithm, then I can save a little bit here and then a lot more there,” and so on?

Daniel: I think it’s more towards we’re doing this in an automatic way. Of course, if you would just work long enough with a given hardware setup, you could find all kinds of way, and people are doing this. This is also an embedded effort. Unless we achieve quantum advantage as an industry, we need to squeeze out every optimization potential we can. But it’s not about tinkering and squeezing out incremental and advantages by cutting corners. So by optimizing something by hand, it’s more or less finding systematic ways of doing so. And, of course, this requires some sophisticated machine learning technology and a lot of proprietary techniques, which ostensibly help us to both our universal compression, but also the kind of mapping towards analog building blocks and native gates in a more automized way.

Yuval: You mentioned at BASF you realized that there are about 100 different use cases that you found. But earlier in our discussion, when we were talking about chemistry, is your focus chemistry algorithms for now, or is it broader to other types of applications?

Daniel: So our technology per columns are applicable to essentially every industry problem. So we did a lot of benchmarking work trying to find out where the limitations of the technology are, and of course, there are some problems here and there that don’t lend themselves particularly well to our approach. But I would that the vast majority of fundamental problems can be solved and simplified and compressed using our technologies. Of course, as a chemist, this industry vertical is particularly dear to my heart. So we think that this is certainly something where we would like to start, but we are not limited to that.

We also did some recent work on portfolio optimization, for instance, for the financial industry. And I think for us, now the question is… And that, again, depends on the use cases since we are all about making stuff very specific. We want to look at different problems working with different companies, from chemicals, and pharma, but also finance logistics manufacturing, who is essentially identify where the compression potential of our technology applied on real problems is the biggest. And this, of course, will then tell us which of these different use cases is going to be the first to help us achieve quantum advantage. And that’s more or less what we are going through right now.

What I do that is somehow related to chemistry is reasonably high in my perspective, and I would like to see this because, at the very beginning of my career, I didn’t mention that I also worked in a start-up using classical computing to do material and modeling. I really enjoyed the benefits, but I also ran into some severe issues because we had accuracy limitations, and we were trying to find cool emitting materials. So I know that these issues exist, and I would like to see it being chemicals for the first, but it could also be finance or machine learning or something entirely different.

Yuval: In one of the interviews that I read that you gave, you mentioned the GPU, the graphic processing unit playbook. What did you mean by that?

Daniel: I mean, I think the nice thing about GPUs is that it is also a spirit where you are very, very efficient in a way. So I think GPUs is about solving a problem, which is also complex, like graphics rendering essentially, using small chip architectures. And, of course, the rationale for GPUs is that smaller chips are more inexpensive than larger ones, so you can make much, much more cost-efficient proposals if you’re doing this kind of mix-and-match approach and tailoring of the architecture as well as algorithms towards the problem. I think in the quantum space, where we are of course, still limited by the number of pip sizes in a way, it’s not so much about saving cost. It’s about lowering that threshold for solving real problems to the point where it more or less intersects with the hardware’s abilities to deliver. So this is something which I really like.

Yuval: One last question on the technology. If someone were to try to explain what you do, would it be correct to say that you’re doing an optimizing compiler or a transpire, or would you say that your technology is a completely different type of product?

Daniel: I think it’s a dual step. It certainly has this compression element, so-called digitized-counterdiabatic quantum computing, that we are doing, but the compilation towards digital analog building blocks is certainly also there. So it’s more or less both of these things, I would say.

Yuval: You mentioned compressing the time to quantum advantage, so the obvious question is, well, how soon? How soon can a customer expect to get some quantum advantage when working with you?

Daniel: I think the time for that again depends on the hardware development, of course. But if I’m looking at different roadmaps, I think there are three front-running technologies, superconducting, quantum computers, ion traps, as well as now recently, even multiple atoms or cold atoms, which have been emerging. If at least a section of those roadmaps manifests themselves, then I think we can target quantum advantage for the first isolated problems. Not broad all quantum advantage, but first isolated problems in the order of three to five years. So that’s our current assumption. If the hardware development roadmaps hold, and if our compression also holds the same way it has been performing on the smaller problem size and of course, the smaller quantum hardware that we’ve been testing it on.

Yuval: And because this is hardware specific, have you decided on what technology or vendor you’re going to focus on, or are you at the moment working on multiple versions at the same time?

Daniel: We are working on multiple versions. Of course, there will be one player who will have a hardware platform that is good enough first. There always has to be a winner in the race, but we think that there will also be a mix and match between hardware types and problems at the end of the day because some paradigms, like ion traps offered very cool all to all connectivity, and there are certain mathematical problems that then lent themselves very well towards that specific capability. There are other problems where all to all connectivity is maybe more of a nice to have and not used to the fullest extent.

So we think that there will be platforms that will be good for using our technology for some kind of problems, and others for other kinds of problems. So I don’t believe that it will be like one winner takes it all market. From the hardware perspective, it’s going to be a number of hardware players in the next few years claiming quantum advantage with our help, essentially hopefully, and problem-specific always.

Yuval: If you would, tell me a little bit about the company. How large is the company, and how would customers work with you? Is it the product that they buy and then use or is it more of a project based at this time?

Daniel: So, as you alluded to, I didn’t tell this yet, but we recently closed a three million euro pre-seed round obviously to save the currency in times of high inflation, and especially a very weak euro. But we now have full coffers, and essentially, with that already, we have put together a team of 10 FTE, essentially. Of course, a lot of physicists in the team, some full-stack software developers, some application domain experts, some operations professionals with a lot of track record essentially built the organization. We want to grow, of course, some further than that, and we’re already working with first customers.

As you can imagine, the bar is already set. Quantum advantage, even with our technology, is about three to five years out. So we don’t claim that we can solve meaningful problems as of today, so it’s more or less project related business model at this point. We think that very soon, as soon as it becomes more apparent which specific hardware platform and which specific problem type it’s going to be, we are going to quickly turn our full staff developer that is already involved into turning this into a scalable product with a kind of enterprise software business model. But for now, it’s project-based.

Our favorite customers, maybe to add that, is, I think, the companies who already work with quantum computing. So we are less interested in convincing novices, companies who have never worked in quantum before, to just test it. I think that’s already a very viable ecosystem with a lot of companies who do this particularly well. I mean, I did this myself in earlier days at McKinsey, but that’s not the type of customers we are approaching. We more or less want to work with the front runners that have been emerging, which have already quantum teams on the ground, which have the capability to know what their use cases are where we don’t need to convince anybody that this is valuable and they have a pre-understanding of what use cases. Because this helps us, of course, to have also clear counterparts in these organizations who can tell us, “Let’s focus on this use case. For this, you would appreciate an application hardware-specific solution,” and then essentially, we’re counting on this.

Yuval: And speaking of customers in various stages of their quantum journey, have you seen customers that are disappointed that maybe believed too much of the hype and tried something, it didn’t work for them, and now they said, “Oh. Now we’re going to put it aside for a few years”? Or are most customers still in the “haven’t tried quantum” or “working on quantum right now” stages?

Daniel: So it’s a little bit in between. So I can attest that there are a lot of customers who have been disappointed by overpromising from the ecosystem. I mean, of course, the expectation horse may be a little bit driven by over hype. Some expect that there would be not dabbling, but I don’t know, a five or 10-fold improvement or further leap-frogging in the number of qubits that it would have. That didn’t happen. It’s still a healthy pace, but of course, no miracles happened in the past three, four years. Some customers were led to believe that a miracle would occur, which didn’t.

I didn’t see so far that a lot of customers gave up on quantum, mainly because there’s still this massive opportunity. The carrot is still big and juicy, in a way. So that didn’t happen from my understanding, but of course, people are now more weary. I think this is also good, because it helps to avoid hype if customers don’t jump into the answer for everybody claiming miracles in a quarter or two. Customers are more critical, which is good. Some are disappointed with the past progress, but I’ve not seen anybody give up on quantum at this point in time. Not quite yet.

Yuval: And as we get close to the end of our conversation today, you are based in Berlin or two cities in Germany? Where is the company based?

Daniel: Yeah. So the official headquarters is in Karlsruhe, Germany, which is about an hour south of Frankfurt. We have a second hub in Berlin. Of course, the tech team is extremely international at this point, and, of course, for that, we wanted to pick the most international city, which is inarguably Berlin in Germany. So this is where the tech team is located, and the commercial activity is mainly driven out of Karlsruhe, which in my humble opinion, has much better weather and climate compared to Berlin, which is terrible during winter times.

Yuval: How can people get in touch with you to learn more about the work that you’re doing?

Daniel: I guess an email is the best way to reach me. First name dot last name at So feel free to get in touch if you’re interested to learn more about Kipu or the kind of things we can do. Again, we are not fully loaded yet regarding customers, and we are still hiring. So if you feel that you are either a quantum scientist or a software developer, or a business professional with a background and you want to make quantum advantage a reality by being application and hardware specific, please be in touch. I think the same goes out for all mature quantum users who are feeling that disappointment because promises have not been made and who still want to give quantum another thought. So you are also more than welcome to reach out.

Yuval: Daniel, thank you so much for joining me today.

Daniel: Thanks, Yuval. It was a pleasure.

Yuval Boger is an executive working at the intersection of quantum technology and business. Known as the “Superposition Guy” as well as the original “Qubit Guy,” he can be reached on LinkedIn or at this email.

Published on Quantum Computing Report - November 14, 2022


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