Sabrina Maniscalco, co-founder and CEO of Algorithmiq, a company that is bringing quantum to life and to life sciences is interviewed by Yuval Boger. Sabrina and Yuval talk about Algorithmiq’s approach to achieving quantum advantage for life science applications using NISQ-era gate-based machines, her prediction for when quantum advantage will be achieved, the journey from Sicilia to Finland and much more

Transcript

Yuval Boger: Hello, Sabrina. And thanks for joining me today.

Sabrina Maniscalco: Hi. Hello. Very nice to be here.

Yuval: Glad to have you. So who are you and what do you do?

Sabrina: So, Sabrina Maniscalco. I’m a Sicilian who lives in Finland. I’m a professor of Quantum Information, Computing, and Logic at the University of Helsinki. But most of my time, I’m CEO and co-founder of Algorithmiq. So now, after many years working in research in academia, I now decided to join really the side of the startup. And so therefore, we co-founded Algorithmiq and we are bringing quantum to life.

Yuval: What does that mean? What are you working on, and what are the applications of your technology?

Sabrina: We are working on the development of quantum algorithms with the specific focus of life sciences. So we are talking about quantum algorithms for near-term quantum computers. So not yet for fault tolerant. Of course, we are going in the direction of fault tolerance, but we want to use the computers we have now in the way in which they are growing with the noise, which is one of the main obstacles causing errors. And specifically, we want to develop algorithms and software to tackle problems in the life sciences.

So drug development and discovery is one of the main lines. But of course, in order to reach these milestone, there are other intermediate steps that need to be achieved. For example, the demonstration that quantum computers with our algorithms can perform quantum chemistry simulations that cannot be performed by any classical machine. So obviously, this is the first goal that will be the stepping stone towards industrial applications in DDD (drug development and discovery) and in life sciences in general.

Yuval: What kind of computers do you use? So do you use gate-based quantum computers? Do you use annealers or maybe it is just quantum-inspired algorithms that you’re using? Could you tell me a little bit about the technology, please?

Sabrina: Yes. We are using algorithms for a gate-based model quantum computers. And what is a little bit our differentiator, what makes us different with respect to other startups working in a similar field, is the development of what we call Informationally Complete Readout Strategy.

So this is maybe quite abstract and detail lexicon, but basically, what we mean is that we have developed a way to read out quantum computers that makes really algorithms for very simulations scalable. And this is the reason why we believe that we can achieve demonstration of useful quantum advantage in the very short term, actually. And it’s one of our goals for the next year.

Yuval: And what kind of problems can you solve? And are these problems something that people cannot solve today classically? Or is it more, “We’re proving the concept now, and in a couple of years, we’ll be able to solve problems that could not be solved classically?”

Sabrina: That’s a very good point. Because we see that many algorithms have been already developed for near-term quantum computers. That work is proof of principle. So we can demonstrate experiments on the real machine. That potentially these algorithms might work as we will be able to scale up the devices and also protect them from the noise. But of course, these are just proof of principle experiment.

But what we are working at the moment and what we have discovered very recently, I actually announced this in one of the major conferences in Vienna. What we have discovered is, actually, if we use the strategy that we have developed, then this is something for which we do not need to wait several years. So what we are tackling are not specific experiments on molecules that are proof of principle, but they have already a complexity such that they are very difficult to tackle classically.

And so this is our current major R&D effort. And of course, at the same time, we are collaborating with the Harvard Medical School and with the Lawrence Livermore National Laboratory in order to identify use cases more related to the drug development and discovery part. For which in the longer term, so in a couple of years, we will be able to apply the same strategy.

If you want, it’s really like a combination of tools that are optimized to all degrees for all pieces in order to work on these very limited computers. I always make an analogy, that is, one of the Apollo guidance system. The Apollo guidance system was a device that, if one looks at the specs, really, it had four kilobytes of RAM. And four kilobytes of RAM is nowadays not even sufficient to store a five-qubit density matrix in double precision.

And it’s the computer that was able to send a man to the moon and back. And this is because the code that Margaret Hamilton developed was so optimized in all aspects to allow a very limited device to perform and to achieve something that is one of the major successes and milestones in human history.

Yuval: Is there a particular type of drug that you are working on, for instance, focused on cancer or focused on something else, or is it more of a generic effort at the moment?

Sabrina: Yes. That’s a very good point. So what we thought is that the very first effort has to be concentrated on the demonstration that we can actually run algorithms on existing devices that are good for quantum chemistry simulation. So this means basically calculating binding affinities, binding energies, and therefore demonstrating what is the core to apply then to the drug development and discovery use case.

So we do have some examples of use cases, but most of all, this will come only in the moment in which we have established the partnerships with the pharma companies, and with them, identify, which is the best strategy. So most of the effects which are very difficult at the moment to analyze and for which we think that quantum computers will have an advantage are the effect of solvent.

Very often, when one considers drugs, and in particular, small molecules binding in the binding pockets of proteins, one neglects a number of realistic scenarios. For example, the fact that water is generally the usual solvent, where these small molecules are immersed in. And therefore it very often takes a very important role in the binding. And these are types of calculations that we are at the moment analyzing and we believe that these are examples for which we can have the full power of quantum computers exploited to the best.

Yuval: How long has the company been active?

Sabrina: The company is two years old. We have been working in stealth mode. We have created a team of experts, which includes not only quantum computer scientists, but also a quantum chemist, a computer scientist, and complex quantum theory experts, and, in general, mathematicians.

Because as you know, this is a very interdisciplinary field and therefore, it is necessary to have top-level expertise from several disciplines in order to tackle what is still the holy grail of the field, that is really to prove useful quantum advantage.

And this is what we are extremely excited about because that is precisely what we believe we can do differently. We have understood really what is the ingredient that allows to change the perspective and unlock the power of quantum computers.

Yuval: How do you feel about pharma companies trying to do the same thing? Because pharma companies certainly have the scientists, they have the budgets, they know what targets they’re looking for. So you could make the case that they just need a few quantum scientists to get started. How do you compare a startup that’s focused like Algorithmiq versus a quantum activity within a large pharma company?

Sabrina: Well, this is a very good point. In general, what is important at the moment is really not to underestimate that the missing ingredient for achieving quantum advantage for, in general, electronic structure problem is really very fundamental. It’s because it’s not, let’s say, a technical… Of course, there is the aspect of technical improvement of the machines that clearly all algorithmic companies are working hand in hand with the development of the hardware companies.

But it is a very fundamental problem. I want to explain it because I think it’s important to understand what is the issue. The all quantum information theory and quantum computation from the algorithms and theory side, was developed to handle systems which were either ideal, so in absence of noise, the complete absence of errors. Or alternatively, based on the assumption that we could measure the full state of this complex molecule, the full quantum state of the molecule.

Now, unfortunately, this is impossible. We know that this is an exponential problem. We will never be able to access the full state of a complex molecule. And therefore, any measurement strategy that tries to do so immediately faces a very strong roadblock. And what is needed is to understand how to, in a way, extract the relevant information from a redundant object. So the full state of our complex quantum system is very redundant, contained way too much information.

But within this information, even in presence of noise that is hidden and the quantum power, the power of the resource that you need to extract. So this is a very fundamental problem. And in order to tackle this problem, you need a team with a very strong academic and research background in quantum information theory. It is not easy to create this team.

In my opinion, the best way of creating such a team is to start from academia because of the networking and the connection, and not the other way around. Of course, then, this will change as the progress and ecosystem evolves. And as these computers become commercially useful. But at the stage in which we are, this is the key, let’s say, block. And I think that this is what we have been targeting.

Yuval: You mentioned that you’re running on gate-based computers, and they’re various approaches to gate-based computers. Some have more qubits, some have better connectivity, and some have different noise characteristics. Do you compare and try to find the best computer for a particular program? Or are you just running on a simulator or just on one particular vendor that you like?

Sabrina: Our algorithms are hardware-agnostic, but of course, it does not mean that they cannot be optimized and indeed that they are optimized based on the different connectivity of the different devices. So we don’t do just simulations, but of course, we, first of all, demonstrate on real devices, whether they are top downs or whether they are superconducting qubits.

Until now, we have not been using photonics as systems, but mostly superconducting qubits and trapped ions. And we demonstrate the proof of principle of our algorithms. And at the moment, we are collaborating with a major hardware provider in order to implement our measurement strategies and demonstrate quantum advantage for chemistry.

Yuval: One would think that this approach could go beyond just pharma, right? You could maybe apply it to material design, or to solar energy, or other things. Do you agree? And, have you made efforts to work in adjacent fields?

Sabrina: Yes, definitely. I agree. At the same time, we have chosen quantum chemistry because we believe that this is, on the one hand, it’s something that has a very strong impact in the life sciences and also, at the same time, is a class of problems for which we have expertise, and for which we believe that we have a clear benchmark. So we have in-house quantum chemists, which are developing a state-of-the-art classical chemistry techniques. With whom obviously we work on the comparison.

Because quantum advantage means always comparing with the best in class classical algorithms. So yes. However, we have chosen on purpose to focus, because different, in a way, use cases, different fields, materials versus solar panels versus chemistry for drug development and discovery, they have something in common. But the details are such that focus is needed in order to tackle all the optimization problems that are naturally arising.

In a second phase, subsequently, we plan to, of course, grow in the space of everything which has to do with the electronic structure properties, but this is not the initial focus. So there can be a future in which we will consider also materials, and therefore designing of new materials and all the applications in that framework. But this is not happening now. And this is not, let’s say, happening also in the short timeframe, because we believe that focus is needed. And because we want to become a quantum biotech in the future.

Yuval: The Million-dollar question is always about the timing for quantum advantage. When do you think, based on the status of your company, your development, hardware, and so on, when do you think a pharma company could say, “Wow, this is something that we could not have done classically, and quantum is really the only way to achieve these results?” Is it this year? Is it next year, in 2023? When do you think it’ll come?

Sabrina: We believe, and we have reason to be very optimistic about applications for the pharma companies, they’re useful, to be developed within 2025. So it’s a very short timeframe, but it is based on the recent results and on the successful development of the strategy that our proprietary strategy that has been developed in the last couple of years.

Yuval: And you said 2025, right? Excellent. I’m curious about your personal journey. As you mentioned, you are originally from Italy. How did you end up in running a company in Finland?

Sabrina: It’s never a planned. Life is so full of a lot of unexpected turns and events. To be honest, I never planned many years in advance what I would be doing. So after finishing my Ph.D. in Italy, it just happened that I started to collaborate with really scientists working on open quantum system. My expertise is really noise and errors on quantum devices. So I’ve been studying these many for many years.

And I just got offered a postdoc position, first in Bulgaria, then in South Africa. Then from South Africa, it was very funny because it was from South Africa to Finland. It is really from the South Pole to the North Pole. Then eventually I got my first professorship in Edinburgh in Scotland. And I moved back as the chair of theoretical physics to Finland in 2014. And then I remained in Finland as a professor. I had my own group and so on.

Why Finland? There is no other why than it just happened. It was part of my life, it just happened as many things happened. And I’m extremely happy, however, to live in Finland. It’s a fantastic country. I found it one of the most civilized countries in the world. It’s beautiful to live here and I plan to stay. In general, it’s quite the opposite as Sicily is. I would say that there is nothing more opposite than Sicily in Europe than Finland. So there are from many points of view, almost all.

Yuval: Thank you for sharing that. Sabrina, how can people get in touch with you to learn more about your work?

Sabrina: Yes. Well, certainly with one email address is info@algorithmiq.fi. So this is one of the ways. And then, of course, via LinkedIn. So that is the second way. Yes.

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

Sabrina: Thank you. It was a pleasure to talk.

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.

November 6, 2022