AZoQuantum had the pleasure of speaking with Sabrina Maniscalco, CEO and co-founder of Algorithmiq, which is set to change the field of life sciences and drug discovery with its quantum software.
What first drew you to science as your academic pathway, and to quantum science in particular?
I'm Sabrina Maniscalco, a Sicilian living in Finland, and I'm a professor of quantum information, computing, and logic at the University of Helsinki. I’m also the CEO and co-founder of Algorithmiq.
I've been working in quantum science and technology for over 20 years. When I did my Ph.D. in quantum science and technology, it was really very far from anything that could have a commercial value or that could impact our society in terms of economics or even from a more general innovation perspective. And so I saw how the field changed and progressed and about two years ago I thought, together with a perfect team, that the conditions were right for Algorithmiq. I'm very excited and happy about it and I think this was the right thing to do.
Why quantum science and technology? Well, I fell in love with quantum physics when I was in secondary school, and it just so happened that the professor with whom I did my Master's thesis was working in the field of quantum optics. This is one of the main fields underlying quantum information science and technology, and it's one of the platforms nowadays that is used for quantum computers.
Could you describe your specific research and development focuses within quantum engineering?
My expertise was really all the very subtle interactions that quantum systems have with everything around them. There's a very technical term called open quantum systems, and there's a theory that describes them. It's really about why quantum systems are so fragile and delicate, which is the reason why we have errors in today’s quantum computers. It's one of the main enemies of existing devices because we want to make them as perfect as possible in order to really operate at a large scale.
You have held many impressive academic positions, including Professor of Quantum Information and Logic at the University of Helsinki, Vice Director of the Finnish Centre of Excellence for Quantum Technologies, as well as serving on the scientific advisory boards of institutes like the Quantum Technology initiative at CERN. How has the field of quantum engineering developed over the course of your research and teaching career?
I think that what is interesting is that it has become more and more interdisciplinary and it has become more and more in need of all sorts of specialistic expertise. Before, I think really quantum information science and technology was born from theory. And at the same time, there was a development on the experimental side that was groundbreaking, so we were able to isolate and manipulate individual particles at the level of individual quanta. This is amazing because they are so small, to be able to isolate them and manipulate them required enormously large laboratories and a lot of very advanced techniques. This happened at the same time as the theory, and the number of particles that could be manipulated individually grew until a point at which we could really design algorithms for performing computations.
The whole field slowly somehow moved towards a new focus, which is ‘what are the possible applications?’ So now we have even some prototypes - small devices that are available on the cloud.
We know quantum computers are available on the cloud, and we can use them, but they're very imperfect. So it's very difficult to do something now that is better than any classical computer or any classical algorithm. Therefore, what we are trying to do now is, on the one hand, improve the algorithms, and on the other hand, identify what are the best applications for quantum computers of today and for the future. This quest for the best applications immediately became very multidisciplinary, because of course the best applications are in chemistry, but they are also in materials, logistics, finance, etc.
Use cases are spread across different fields where there is the need to have very high computational power. There are many fields that are limited by existing computational abilities, and already in the past there have been cross-disciplinary exchanges with computer scientists, mathematicians, physicists, engineers, and all sorts of figures. So this is very exciting because it's one of the fields where you really see the intersection between all these different disciplines and also the need to find some sort of common language, which in quantum physics is not always very simple to explain. It's not so straightforward to find the proper way to communicate across different disciplines.
What more needs to be done to support the growth of this sector, and how will academia continue to feed into this?
I think that there are several important points to highlight. On the one hand, I think it is very important to clarify what is hype and what is not hype. In the field, there has been a lot of debate between claims that are considered too strong and too unrealistic, and counterclaims that are also saying we are very far away and nothing has been proven. So in a way, I think it is very important that one finds a way of communicating what is realistic but also remains specific, because the problem is that very often we generalize and we make claims pro or against which confuses people.
So we have to somehow be precise and make very clear assumptions about the conditions for which, for example, quantum computers can be deployed now or can be deployed in 10 years time. What can be done now? In which fields? And so on and so forth, without generalizing.
All the different types of quantum computers have different timelines, different properties, and different problems that need to be solved in order to become full-scale and achieve better than the classical computer, etc. So this is one point - the hype.
Image Credit: Bartlomiej K. Wroblewski/Shutterstokc.com
The second point is that I think it's very important to realize that, and I say this as a CEO of a software company, the more imperfect the quantum computer or in general, the device, the more complex and optimized at all levels the software must be.
I always cite the moon landing as an example, because the Apollo Guidance System, which is the computer that was used to control Apollo to bring the men to the moon and back, was really very, very limited. It had four kilobytes of RAM and 32 kilobytes of hard disk. Four kilobytes of RAM wouldn't be able to store even the quantum state of five qubits. It's really, really poor. But the code that was used was incredibly complex. So that's why software needs to be very, very optimized at each and every level. And for this, focus is key. I believe that the best way to achieve a quantum advantage is to focus on one specific field. In our case, it's life science and quantum chemistry, and in the longer run, drug development and discovery. So that's the second point - focus and software optimization must be supported and they must go hand in hand with the improvement of the hardware.
The third thing is the workforce. We lack the highly specialized workforce that is needed for this field, so we need to educate more quantum scientists and quantum engineers. This is something that has to be done at the level of universities, but not only universities. Indeed, this is something that we are also doing, providing several degrees with a platform qplaylearn.com that we have created precisely to provide this multilevel and multilayered education.
What are some of the goals of Algorithmiq as a company?
Algorithmiq, as I mentioned, is a quantum software company with a specific focus on life sciences. What we are doing at the moment is creating a platform for quantum chemistry simulations, but also more generally for simulating protein-ligand binding, which is one of the processes that is key for drug development and discovery.
Of course, there exist other software companies doing similar, but we do have one main difference with respect to the others, which is the way we have developed the interface between quantum and classical post-processing. Because we focus on near term quantum computers, we are working on proving a quantum advantage for chemistry within the next year or two years. In the next three years, we will prove practical and industrially relevant quantum advantages for drug discovery and development.
Image Credit: Gorodenkoff/Shutterstock.com
To expand on the key enabler that I mentioned before that differentiates us from the other startups, this is the use of so-called informationally complete data or informationally complete measurement. Basically, this is the interface between the quantum computer and the classical side of post-processing. The classical is a high-performance computer that is used together with the quantum computer to run hybrid algorithms and quantum / classical algorithms.
This readout process has a unique property that in some way allows us, with the same data, to estimate many properties of a molecule that we want to simulate at once. This is different from other algorithms with other measurement and readout strategies for which we would need to repeat the experiment every time we want to calculate a specific property. We are able to estimate many properties at once, and this automatically allows us to really shorten the run time of the algorithms, both quantum and classical.
This edge increases as we increase the complexity of the problem, because everything which has to do with the quantum simulations of chemistry scales as the number of qubits increases.
At the same time, this method allows us to clean the data that we obtain from the noise. This measurement strategy allows us to clean all the errors that exist in the near-term computers and extract the pure quantum resource that is inside the algorithms.
Quantum technologies are only relatively recently becoming commercially viable. How do you think Algorithmiq will bring us closer to a commercial quantum future?
For us, the main way of commercializing is through partnerships with pharma companies and also with biotech in order to identify the use cases that can be solved using these hybrid algorithms - Algorithmiq's proprietary techniques.
Then there will be the commercialization of Aurora, our platform. We will open it up to other users who want to calculate the properties of molecules and binding affinities or binding energies, the reactivity of properties that are useful in the framework for drug development and discovery, etc.
And later on, there will be royalties coming from the patents of new drugs. So this eventually will be a pipeline and in the future, we plan to become really a full stack quantum biotech company, with in-house everything that is needed to solve unsolved problems in medicine.
What we will produce are large datasets of new molecular compounds which exhibit low toxicities and high potencies and, therefore, are successful in clinical trials.
Why have you chosen to focus on the sector of life sciences?
We believe that the first applications on near-term quantum computers will be in quantum chemistry simulation and material simulation. In general, electronic structure problems. So this being one of the first applications, it made sense for us to restrict to this. And then the second reason is that we think that it is a very impactful field where there is also a lot of money around. So it definitely makes sense because the pharma industries are stuck at the moment.
The Algorithmiq team. Image Credit: Algorithmiq
It is well known that spending in R&D for drug development and discovery has been increasing steadily over the years, but the number of new molecular entities and new drugs that are discovered remains the same. Generally, pharma is failing. Just one statistic says it all: about 90% of drugs existing today fail to cure 50% of the population. They are really failing and there are many reasons for this. One is the oversimplification of cell biology that we solve with the network medicine approach, but the other one is that we need to be able to predict how small molecules bind to proteins, and very often you need a full quantum mechanical approach to do this. Quantum computers are naturally born to provide this description.
Will Algorithmiq be the first company to apply quantum algorithms to life sciences?
Yes, that's correct, specifically in the framework of a very holistic approach that we call quantum network medicine. We combine not only the quantum chemistry simulations but also what we call this multi-scale approach or data-driven method, which starts from the analysis of a variety of data in, for example, omics, that allows us to identify the complex cell biology and identify in a more holistic manner how molecules interact not just with one protein but with several.
There's a nascent field that is network medicine, and we are collaborating with the founding fathers - Professor Joseph Loscalzo and Harvard Medical School. We provide the quantum side of network medicine, so we can combine this data-driven approach and have indeed launched the quantum network medicine vision.
Will you continue to balance academia alongside your work at Algorithmiq?
At the moment, I'm not teaching, but what I'm doing is representing Finland in the quantum science and technology international community. For example, I was invited to the White House where I discussed our national roadmap for quantum science and technology. Next week I go to the European parliament. So it's more like a representation role for the quantum science and technology community, trying to somehow identify the priorities and the needs of our community in Finland and of course in Europe and internationally.
Looking over your career so far, are there any moments that stand out to you as having been particularly significant or enjoyable?
Well, certainly the moment in which I co-founded Algorithmiq alongside Guillermo García-Pérez, Matteo Rossi, Boris Sokolov, and Jussi Westergren. I think that I remember clearly my first professorship thinking ‘okay, now I achieved something I really dreamed of doing'. But also, when my first Ph.D. student finished their degree, this was of course something very important. And going back, I think that there are a couple of research results that were, for me, particularly important as a researcher.
Generally, I would say that at the very beginning it was really the excitement of discovering something new and then realizing how this would be used, first in theory, then in experiments. And now, even in real applications.
Where do you see Algorithmiq being in five years' time?
In five years' time, we will be the first quantum biotech company, and we will have proven for the first time the quantum advantage in chemistry, as well as practical quantum advances in the field of drug development and discovery.
In order to do this, you really have to have the best possible team. So one of the things I'm most proud of is the team at Algorithmiq, the way in which it has been formed, and the fact that it is so united and there is really an absence of people competing to show off.
Everyone is really working well together and most of all, we are having a lot of fun. I think that we are contributing in a profound way to a field that is really promising to change our lives in so many ways.
More from AZoQuantum: Creating Quantum Network Solutions with Aliro
About Sabrina Maniscalco
Sabrina Maniscalco is the Professor of Quantum Information and Logic at the University of Helsinki, and an Adjunct Professor at Aalto University, Finland. She is the Vice Director of the Finnish Centre of Excellence for Quantum Technologies and serves on the scientific advisory board of several international institutions, such as the Institute for Quantum Optics and Quantum Information (Austria) and the Quantum Technology initiative at CERN. She is CEO and co-founder of Algorithmiq Ltd, a startup focussing on quantum algorithms and software.
Sabrina obtained her Ph.D. at the University of Palermo (Italy) in 2004. She has held academic research positions around the world, in Sofia (Bulgaria), Durban (South Africa), Turku (Finland) and Edinburgh (UK). She returned to Finland in 2014 to lead the Theoretical Physics Laboratory in Turku. She then moved to the University of Helsinki in November 2020.
Sabrina represented Finland’s Quantum National strategy at the White House in May 2022 and is a national figure in quantum for Finland. Sabrina has coordinated several international and interdisciplinary projects and is recognized as one of the leading experts in Quantum Technologies
Disclaimer: The views expressed here are those of the interviewee and do not necessarily represent the views of AZoM.com Limited (T/A) AZoNetwork, the owner and operator of this website. This disclaimer forms part of the Terms and Conditions of use of this website.
AZoQuantum had the pleasure of speaking with Sabrina Maniscalco, CEO and co-founder of Algorithmiq, which is set to change the field of life sciences and drug discovery with its quantum software.
What first drew you to science as your academic pathway, and to quantum science in particular?
I'm Sabrina Maniscalco, a Sicilian living in Finland, and I'm a professor of quantum information, computing, and logic at the University of Helsinki. I’m also the CEO and co-founder of Algorithmiq.
I've been working in quantum science and technology for over 20 years. When I did my Ph.D. in quantum science and technology, it was really very far from anything that could have a commercial value or that could impact our society in terms of economics or even from a more general innovation perspective. And so I saw how the field changed and progressed and about two years ago I thought, together with a perfect team, that the conditions were right for Algorithmiq. I'm very excited and happy about it and I think this was the right thing to do.
Why quantum science and technology? Well, I fell in love with quantum physics when I was in secondary school, and it just so happened that the professor with whom I did my Master's thesis was working in the field of quantum optics. This is one of the main fields underlying quantum information science and technology, and it's one of the platforms nowadays that is used for quantum computers.
Could you describe your specific research and development focuses within quantum engineering?
My expertise was really all the very subtle interactions that quantum systems have with everything around them. There's a very technical term called open quantum systems, and there's a theory that describes them. It's really about why quantum systems are so fragile and delicate, which is the reason why we have errors in today’s quantum computers. It's one of the main enemies of existing devices because we want to make them as perfect as possible in order to really operate at a large scale.
You have held many impressive academic positions, including Professor of Quantum Information and Logic at the University of Helsinki, Vice Director of the Finnish Centre of Excellence for Quantum Technologies, as well as serving on the scientific advisory boards of institutes like the Quantum Technology initiative at CERN. How has the field of quantum engineering developed over the course of your research and teaching career?
I think that what is interesting is that it has become more and more interdisciplinary and it has become more and more in need of all sorts of specialistic expertise. Before, I think really quantum information science and technology was born from theory. And at the same time, there was a development on the experimental side that was groundbreaking, so we were able to isolate and manipulate individual particles at the level of individual quanta. This is amazing because they are so small, to be able to isolate them and manipulate them required enormously large laboratories and a lot of very advanced techniques. This happened at the same time as the theory, and the number of particles that could be manipulated individually grew until a point at which we could really design algorithms for performing computations.
The whole field slowly somehow moved towards a new focus, which is ‘what are the possible applications?’ So now we have even some prototypes - small devices that are available on the cloud.
We know quantum computers are available on the cloud, and we can use them, but they're very imperfect. So it's very difficult to do something now that is better than any classical computer or any classical algorithm. Therefore, what we are trying to do now is, on the one hand, improve the algorithms, and on the other hand, identify what are the best applications for quantum computers of today and for the future. This quest for the best applications immediately became very multidisciplinary, because of course the best applications are in chemistry, but they are also in materials, logistics, finance, etc.
Use cases are spread across different fields where there is the need to have very high computational power. There are many fields that are limited by existing computational abilities, and already in the past there have been cross-disciplinary exchanges with computer scientists, mathematicians, physicists, engineers, and all sorts of figures. So this is very exciting because it's one of the fields where you really see the intersection between all these different disciplines and also the need to find some sort of common language, which in quantum physics is not always very simple to explain. It's not so straightforward to find the proper way to communicate across different disciplines.
What more needs to be done to support the growth of this sector, and how will academia continue to feed into this?
I think that there are several important points to highlight. On the one hand, I think it is very important to clarify what is hype and what is not hype. In the field, there has been a lot of debate between claims that are considered too strong and too unrealistic, and counterclaims that are also saying we are very far away and nothing has been proven. So in a way, I think it is very important that one finds a way of communicating what is realistic but also remains specific, because the problem is that very often we generalize and we make claims pro or against which confuses people.
So we have to somehow be precise and make very clear assumptions about the conditions for which, for example, quantum computers can be deployed now or can be deployed in 10 years time. What can be done now? In which fields? And so on and so forth, without generalizing.
All the different types of quantum computers have different timelines, different properties, and different problems that need to be solved in order to become full-scale and achieve better than the classical computer, etc. So this is one point - the hype.
Image Credit: Bartlomiej K. Wroblewski/Shutterstokc.com
The second point is that I think it's very important to realize that, and I say this as a CEO of a software company, the more imperfect the quantum computer or in general, the device, the more complex and optimized at all levels the software must be.
I always cite the moon landing as an example, because the Apollo Guidance System, which is the computer that was used to control Apollo to bring the men to the moon and back, was really very, very limited. It had four kilobytes of RAM and 32 kilobytes of hard disk. Four kilobytes of RAM wouldn't be able to store even the quantum state of five qubits. It's really, really poor. But the code that was used was incredibly complex. So that's why software needs to be very, very optimized at each and every level. And for this, focus is key. I believe that the best way to achieve a quantum advantage is to focus on one specific field. In our case, it's life science and quantum chemistry, and in the longer run, drug development and discovery. So that's the second point - focus and software optimization must be supported and they must go hand in hand with the improvement of the hardware.
The third thing is the workforce. We lack the highly specialized workforce that is needed for this field, so we need to educate more quantum scientists and quantum engineers. This is something that has to be done at the level of universities, but not only universities. Indeed, this is something that we are also doing, providing several degrees with a platform qplaylearn.com that we have created precisely to provide this multilevel and multilayered education.
What are some of the goals of Algorithmiq as a company?
Algorithmiq, as I mentioned, is a quantum software company with a specific focus on life sciences. What we are doing at the moment is creating a platform for quantum chemistry simulations, but also more generally for simulating protein-ligand binding, which is one of the processes that is key for drug development and discovery.
Of course, there exist other software companies doing similar, but we do have one main difference with respect to the others, which is the way we have developed the interface between quantum and classical post-processing. Because we focus on near term quantum computers, we are working on proving a quantum advantage for chemistry within the next year or two years. In the next three years, we will prove practical and industrially relevant quantum advantages for drug discovery and development.
Image Credit: Gorodenkoff/Shutterstock.com
To expand on the key enabler that I mentioned before that differentiates us from the other startups, this is the use of so-called informationally complete data or informationally complete measurement. Basically, this is the interface between the quantum computer and the classical side of post-processing. The classical is a high-performance computer that is used together with the quantum computer to run hybrid algorithms and quantum / classical algorithms.
This readout process has a unique property that in some way allows us, with the same data, to estimate many properties of a molecule that we want to simulate at once. This is different from other algorithms with other measurement and readout strategies for which we would need to repeat the experiment every time we want to calculate a specific property. We are able to estimate many properties at once, and this automatically allows us to really shorten the run time of the algorithms, both quantum and classical.
This edge increases as we increase the complexity of the problem, because everything which has to do with the quantum simulations of chemistry scales as the number of qubits increases.
At the same time, this method allows us to clean the data that we obtain from the noise. This measurement strategy allows us to clean all the errors that exist in the near-term computers and extract the pure quantum resource that is inside the algorithms.
Quantum technologies are only relatively recently becoming commercially viable. How do you think Algorithmiq will bring us closer to a commercial quantum future?
For us, the main way of commercializing is through partnerships with pharma companies and also with biotech in order to identify the use cases that can be solved using these hybrid algorithms - Algorithmiq's proprietary techniques.
Then there will be the commercialization of Aurora, our platform. We will open it up to other users who want to calculate the properties of molecules and binding affinities or binding energies, the reactivity of properties that are useful in the framework for drug development and discovery, etc.
And later on, there will be royalties coming from the patents of new drugs. So this eventually will be a pipeline and in the future, we plan to become really a full stack quantum biotech company, with in-house everything that is needed to solve unsolved problems in medicine.
What we will produce are large datasets of new molecular compounds which exhibit low toxicities and high potencies and, therefore, are successful in clinical trials.
Why have you chosen to focus on the sector of life sciences?
We believe that the first applications on near-term quantum computers will be in quantum chemistry simulation and material simulation. In general, electronic structure problems. So this being one of the first applications, it made sense for us to restrict to this. And then the second reason is that we think that it is a very impactful field where there is also a lot of money around. So it definitely makes sense because the pharma industries are stuck at the moment.
The Algorithmiq team. Image Credit: Algorithmiq
It is well known that spending in R&D for drug development and discovery has been increasing steadily over the years, but the number of new molecular entities and new drugs that are discovered remains the same. Generally, pharma is failing. Just one statistic says it all: about 90% of drugs existing today fail to cure 50% of the population. They are really failing and there are many reasons for this. One is the oversimplification of cell biology that we solve with the network medicine approach, but the other one is that we need to be able to predict how small molecules bind to proteins, and very often you need a full quantum mechanical approach to do this. Quantum computers are naturally born to provide this description.
Will Algorithmiq be the first company to apply quantum algorithms to life sciences?
Yes, that's correct, specifically in the framework of a very holistic approach that we call quantum network medicine. We combine not only the quantum chemistry simulations but also what we call this multi-scale approach or data-driven method, which starts from the analysis of a variety of data in, for example, omics, that allows us to identify the complex cell biology and identify in a more holistic manner how molecules interact not just with one protein but with several.
There's a nascent field that is network medicine, and we are collaborating with the founding fathers - Professor Joseph Loscalzo and Harvard Medical School. We provide the quantum side of network medicine, so we can combine this data-driven approach and have indeed launched the quantum network medicine vision.
Will you continue to balance academia alongside your work at Algorithmiq?
At the moment, I'm not teaching, but what I'm doing is representing Finland in the quantum science and technology international community. For example, I was invited to the White House where I discussed our national roadmap for quantum science and technology. Next week I go to the European parliament. So it's more like a representation role for the quantum science and technology community, trying to somehow identify the priorities and the needs of our community in Finland and of course in Europe and internationally.
Looking over your career so far, are there any moments that stand out to you as having been particularly significant or enjoyable?
Well, certainly the moment in which I co-founded Algorithmiq alongside Guillermo García-Pérez, Matteo Rossi, Boris Sokolov, and Jussi Westergren. I think that I remember clearly my first professorship thinking ‘okay, now I achieved something I really dreamed of doing'. But also, when my first Ph.D. student finished their degree, this was of course something very important. And going back, I think that there are a couple of research results that were, for me, particularly important as a researcher.
Generally, I would say that at the very beginning it was really the excitement of discovering something new and then realizing how this would be used, first in theory, then in experiments. And now, even in real applications.
Where do you see Algorithmiq being in five years' time?
In five years' time, we will be the first quantum biotech company, and we will have proven for the first time the quantum advantage in chemistry, as well as practical quantum advances in the field of drug development and discovery.
In order to do this, you really have to have the best possible team. So one of the things I'm most proud of is the team at Algorithmiq, the way in which it has been formed, and the fact that it is so united and there is really an absence of people competing to show off.
Everyone is really working well together and most of all, we are having a lot of fun. I think that we are contributing in a profound way to a field that is really promising to change our lives in so many ways.
More from AZoQuantum: Creating Quantum Network Solutions with Aliro
About Sabrina Maniscalco
Sabrina Maniscalco is the Professor of Quantum Information and Logic at the University of Helsinki, and an Adjunct Professor at Aalto University, Finland. She is the Vice Director of the Finnish Centre of Excellence for Quantum Technologies and serves on the scientific advisory board of several international institutions, such as the Institute for Quantum Optics and Quantum Information (Austria) and the Quantum Technology initiative at CERN. She is CEO and co-founder of Algorithmiq Ltd, a startup focussing on quantum algorithms and software.
Sabrina obtained her Ph.D. at the University of Palermo (Italy) in 2004. She has held academic research positions around the world, in Sofia (Bulgaria), Durban (South Africa), Turku (Finland) and Edinburgh (UK). She returned to Finland in 2014 to lead the Theoretical Physics Laboratory in Turku. She then moved to the University of Helsinki in November 2020.
Sabrina represented Finland’s Quantum National strategy at the White House in May 2022 and is a national figure in quantum for Finland. Sabrina has coordinated several international and interdisciplinary projects and is recognized as one of the leading experts in Quantum Technologies