# insideQuantum

insideQuantum tells the human stories behind cutting-edge developments in quantum technology, with the aim of highlighting the diverse range of people behind the amazing discoveries powering the quantum revolution. Each episode features a different guest, chosen from a wide variety of backgrounds, jobs and career stages, including guests from both academia and industry. Over the course of a 30-40 minute chat we'll hear all about their story, and how they got to where they are now. What got them interested in quantum physics? Where did they start, what has their journey so far been like, what advice do they have for others interested in getting into the field, and what do they think the future holds for quantum technologies?

## insideQuantum

# Episode 4: Prof. Román Orús

How can quantum computers be used to solve real-world problems in the financial world? Take a listen to Episode 4 of insideQuantum to find out!

This week we’re featuring Prof. Román Orús, an Ikerbasque Research Professor at the Donostia International Physics Center in San Sebastián, Spain and Chief Scientific Officer of Multiverse Computing. Prof. Orús obtained his PhD from the University of Barcelona, followed by postdocs at the University of Queensland and the Max Planck Institute for Quantum Optics (Garching), followed by several visiting professorships and a Junior Professor position at the Johannes-Gutenberg Universität Mainz.

For more information and a full audio transcript, see our website insidequantum.org.

🟢 **Steven Thomson** (00:06): Hi there and welcome to insideQuantum, the podcast telling the human stories behind the latest developments in quantum technologies. I’m Dr. Steven Thomson and, as usual, I’ll be your host for this episode. In previous episodes, we’ve talked with researchers working on developing some of the fundamental aspects of quantum technology. Today we’re going to talk about how quantum technology and quantum-inspired techniques developed in academia can be applied to solve real world problems. It’s a great pleasure to be joined today by professor Román Orús, a professor at the Donostia International Physics Center in Spain, and also a co-founder and Chief Scientific Officer at Multiverse Computing. Román, thank you so much for joining us today.

🟣 **Román Orús** (00:45): Thank you. It’s a pleasure.

🟢 **Steven Thomson** (00:48): Before we get into talking about Multiverse Computing and your role in the company, let’s first talk about your scientific journey to this point. And let’s, if I may, go right back to the very beginning. What is it that first got you interested in quantum physics?

🟣 **Román Orús** (01:03): Yeah, that’s a good question. Actually, I got interested in quantum physics when I was at the university doing my undergraduate. You know, I like many things in physics. I like relativity, cosmology and so on, but at some point I had you know I just had quantum mechanics and I had a remarkably good teacher and an amazing lecturer who gave an amazing course on quantum mechanics. And I was just amazed by how interesting, and let’s say counterintuitive the theory is and I just fell in love with that. So I decided that I wanted to continue with that. And then, you know, I just moved on finished and I graduated, I went on to do a PhD and it was very clear to me that I wanted to do a PhD on quantum computing.

🟢 **Steven Thomson** (01:51): So why quantum computing in particular then? What was it about quantum computing that really stood out and grabbed you?

🟣 **Román Orús** (01:57): Yeah, actually, I had a couple of…I considered a couple of options. I also considered doing a PhD in particle physics and high energy physics, and also on quantum computing. At the end of the day, I decided to go for quantum computing because I saw that there was a lot of potential. You know, we are talking about the year 2000 or something like this, and quantum computing was not at all at the same state as right now, it was like in a very, let’s see, let’s say embryonic state. The hardware was not there, many of the algorithms had not been yet developed and so on, but it was very clear that quantum technologies were going to have a deep impact in the future and that there was gonna be a kind of a scientific and also technological revolution at the end of the day. That’s what made me choose quantum computing, let’s say over other fields of physics.

🟢 **Steven Thomson** (02:51): So was the, the future potential of the field to, I guess, change the world that interested you?

🟣 **Román Orús** (02:57): Yeah, exactly. So I saw, I mean, it’s not that that I wanted to change the world at that particular point, because when I started my PhD everybody essentially believed that, you know, quantum computers could take a longer time to be developed than what we have seen. I mean, when people ask me, “Hey, when do you think we are gonna have a quantum computer?” I’d typically say, “Well, I don’t know maybe in 100 years,” or something like this. So I, more or less, I started working on quantum computing thinking that what I was doing, you know, I couldn’t see the impact in real life, probably in my whole life, but at the end of the day, I turned out to be wrong. Because the technology has developed much faster than what everybody expected. But what was clear to me is that the quantum technologies were in particular quantum computing was something that was going to change the world. Okay. It was very exciting. There were many open problems. And then I just decided to go for that.

🟢 **Steven Thomson** (03:50): Okay. So how did you get to this current point? So you’re currently a, a research professor. How did you go from PhD to research professor? What was your career journey?

🟣 **Román Orús** (04:01): Yeah, so, you know, I got my PhD back in 2006 at the University of Barcelona on quantum computing. Towards the end of my PhD, you know, I started working on quantum algorithms and towards the end - let’s say towards the middle end of my PhD - I started to work also on the, you know, quantum many value systems because everything was connected to, you know, some computational tools that are called tensor networks. Turns out that one could apply them to quantum computing. One could also apply them to strongly correlated systems. And at the end of the day, I kind of bridged the gap between these two disciplines and I was doing both, both things actually. So when I finished my PhD, I went for a post-doc to the University of Queensland with Guifre Vidal. I was there working for years in this group on tensor network methods. Very much focused on condensed matter physics. Actually, I went to the Max Planck Institute of Quantum Optics in Garching with Ignacio Cirac at the theory group also working on quantum information on strongly correlated systems.

And then I became a professor, a general professor at the University of Mainz where I spent five years actually working on essentially on, on tensor…I work on numerical simulation methods. And after that I got a 10 year position as a research professor in San Sebastian at the, the Donostia International Physics Center, which is a great center for theoretical physics. And I just decided to continue working on, on, you know, what I had been doing until then. So essentially tensor network simulations for quantum matter, but at essentially at the same time, it was when everything started to boom in quantum computing as well. So I started to do once again, quantum algorithm and quantum computing, and actually, you know everything that I had been doing in the context of condensed matter physics turned out to be extremely useful also for understanding this new branch of quantum algorithms that people are proposing nowadays. And that’s how, that’s how I ended up being a research professor. Yeah.

🟢 **Steven Thomson** (06:05): So it sounds like the work you’ve done covers a lot of different fields though. You’ve mentioned many body physics, you mentioned a little bit of quantum optics and also quantum computation. Are these fields…do you find them very different to each other, or do you find that the overlap between these fields came naturally? Just because of the questions you were interested in?

🟣 **Román Orús** (06:24): Yeah. The overlap came naturally. Actually I, when I started doing research in my PhD, I started doing quantum information and quantum computation, but by that time, you know, since the hardware was, was not so developed the field of quantum information was full of, of people that at the end of the day, somehow diverted and started doing in parallel also condensed matter or quantum optics or even high energy physics. The point is that there is, there are natural overlaps between all these disciplines. Okay. And for me, it was very natural to, to work on, on both of them. So I, I always thought that I had one leg on quantum information and one leg on condensed matter physics. Okay. And, and everything that I was doing in condensed matter physics, it was useful for quantum information and the other way around. So I was…one of my, my research lines was to understand entanglement structure in, in quantum many systems.

So, you know, there are quantum correlations and so on, the ones that are used for teleportation and for quantum key distribution and all this very nice you know, quantum technology protocols, but the, the whole point was, well, how does this behave in a quantum many system? Because of course there is entanglement that, and there you start playing with phases of matter and so on. And it’s really an interdisciplinary field. It’s not that you can set very, very defined boundaries between fields. Okay. Actually, lots of results in quantum information that are very useful for condensed matter physics and also the other way around

🟢 **Steven Thomson** (07:50): I see. So you’re really at the interface then between the, the sort of theoretical computer science aspect of quantum information that works out “What are the computational operations we want to do?” and the, the hardware side that says, “Okay, we have these trapped ions or atoms or something, and how do we, how do we enact these computations?” And you’re somewhere in the middle, I guess, translating between these two different extremes?

🟣 **Román Orús** (08:12): Exactly. That’s where I am.

🟢 **Steven Thomson** (08:14): Okay. Interesting. So would you say that the sort of big picture goal of, of your work is the, the development of a quantum computer then, or the supporting the development of a quantum computer?

🟣 **Román Orús** (08:26): Yeah, well actually I would say supporting the development of a quantum computer and also the applications of a quantum computer as, as this right now you know, there are many…and also the investigation of, of new algorithms. Okay. So these, these new algorithms can be useful for many things. They can be useful for understanding phases of matter, okay, which is an open problem. But they can also be useful for understanding other types of, of physical problems and even for applications beyond, beyond physics. Okay. So, cause at the end of the day, if one has a quantum computer, you want to use it for something and, and you need to find applications in other fields, no. Also in industry, you know, how are you gonna apply a quantum computer to solve problems in finance, in energy, in, you know, material science, in health and so on?

Right now my current research goes a little bit along this direction, so how to develop applications and new algorithms for, for quantum computers. Okay. But also of course also using all this knowledge that we already have, and that we have accumulated over the years of, of all these numerical algorithms that we know for, for quantum many body systems. Okay. This is extremely useful, but it’s nice is that all these algorithms that we have been applying to study quantum many body systems over many years, now it turns out that we cannot supply them for instance, to improve machine learning. Okay. Which is an extremely, let’s say transversal tool. You can apply it to essentially everything or to optimization problems, and then you can mix it with quantum computing and so on.

🟢 **Steven Thomson** (09:55): So then you’ve mentioned a lot of different bits of work that you’ve done. Is there anything that stands out to you as something that you are particularly proud of?

🟣 **Román Orús** (10:02): Yeah. Well, actually, that’s a tricky question. So yeah, I, I’m proud of all the work that I’ve done. Okay. But that’s, that’s not a very politically correct answer, probably. I’m proud of all, all my papers. I think that to each one of them, there is something important in my, in my opinion, of course, but I’m particularly proud overall of the work that I’ve done in tensor networks. I think that you know, when I started working in quantum many systems there were many open questions. There were many physical systems that were essentially impossible to simulate say with full success on Monte Carlo, exact diagonalisation and so on. And then all the work that I did on …together with other people such as Guifre Vidal mainly, and also with Ignacio Cirac in particular as well, and other people on tensor network numerical simulation methods.

I think that was that was very important because it allowed to actually reach you know, other physical systems that were impossible to simulate. We proposed several algorithms for simulating say some models, some physical systems that were simply too hard for Monte Carlo. I think we did a great job with that and what we couldn’t foresee by then…our motivation then was to essentially, you know, find new numerical algorithms to simulate and to understand phases of matter that we cannot understand nowadays. But it turns out that in time all those algorithms have found applications that couldn’t foresee when we just invented them, no? So, so for instance, some people in the string theory, now they are doing also tensor networks, but, but most importantly, we are applying tensor networks to machine learning, as I was explaining to you right now, you know. So…And all those algorithms that we developed to similar, you know, systems into dimensions, they have our model. And so on, when it turns out that now we can apply them to, I dunno, to do a anomaly detection or to do classification fraud detection and so on. So it has, they have applications that go much beyond physics and and I’m very proud of that. Yeah. I’m very proud of, of seeing how those bits of research that we did turn out to have applications and implications much beyond what we actually expected. Yeah.

🟢 **Steven Thomson** (12:13): So is this where the idea for Multiverse Computing came from then to, to start exploring these applications for these techniques outside of the, I guess kind of narrow realm of quantum simulation and take it out into the real world?

🟣 **Román Orús** (12:25): Yeah, actually the motivation for Multiverse Computing was a bit broader. I mean, in part it was this, but also because we had this, this feeling, we had this internal question that somebody asked, I mean, it was well can one apply quantum computing to financial problems? Okay. Because we are talking here that quantum computing is gonna revolution, revolutionize the world and so on and so forth, but nobody knows why. We know that, yeah, we are gonna apply to, I don’t know, to, to crack some crypto systems and then cyber security will be, will be, will have problems. And so, but yeah, but, but what are gonna be the applications of quantum computers in real life? What can you do once you have a quantum computer? How, how it’s gonna change something? Okay. And then, you know, there are different verticals, different fields where a quantum computer quantum computer could have an impact.

Of course the first thing that comes through your mind is material science or chemistry, because those are quantum systems and then you say, well, okay, but there are other, you know, situations where there are very strong, hard computational problems and the most important…well, actually, and one of the most obvious is, is finance okay. In finance, in banks, in financial departments of large corporations and so on…it’s full of programmers, mathematicians, physicists, and so on doing lots of simulations of multicar lot optimization of machine learning and financial institutions. They are consumers of algorithms. And finance looked like a very natural, let’s say place to, to apply it a quantum computer. But by the time that we were thinking about this question there was essentially almost nothing done. There were a couple of papers where people, you know, they had proposed some ideas of how to apply a quantum computer to some financial problem.

But you know, it was not very clear what they meant and everybody that you asked, they, they meant something different. And then we just decided to, to think about that seriously. And we came up with very clear picture of what could be done with a quantum computer in finance and what could not, okay, and we wrote a paper about that. Okay. And, and that’s how that’s how multiverse started. So essentially the three authors of that paper were three of the four founders of multiverse. Okay. The paper was hit, you know, many people liked it. And, and that’s how, at some point we decided to, to start the company because we saw, okay, here, there are applications. Okay. We need to take this seriously. We see that other people are just launching startups. We have the feeling that we could do it perhaps even better. Okay. we have a very clear picture of what can be done in this field in particular with applications of finance. And I think that why not?

🟢 **Steven Thomson** (15:01): So you mentioned there that there are lots of quantum computing startups beginning to appear. How is Multiverse different from other startups? Like, for example, take some of the, the very high profile ones that have have gone public recently. Like ionQ for example, that are developing quantum hardware. How is Multiverse different from a company like ionQ?

🟣 **Román Orús** (15:22): Well, there are two types of quantum companies, actually three there are the ones that do hardware, like ionQ or Rigetti, and there are the ones that do software, like like us. Okay. Like Multiverse Computing, like Zapata Computing, like QC Ware, 1QBit it, no? And then there are the ones that do software, hardware, and software. Okay. An example is Xanadu, for instance, they, they do a photon…they are building a photonic quantum processor, but then they are also doing some, some software. Again, these are the full stack companies. So Multiverse is a software company. We don’t do hardware. Okay. So this is the first differentiation with respect to, to, ionQ.

Now within the real of quantum software companies I think that we are pretty unique because we were born with a very strong focus on finance. Okay. Whereas most of the rest of the companies were completely generalistic when they started. Okay. But when we started, we were very much focused on finance. And then we’ve always been working very close to real problems, very close to the client. Okay. We don’t work on academic problems or on toy models. That’s from day one. We only work on a problem if there is a client that has that problem, because that, for that means that it’s it’s relevant. Okay. And and for us, that’s one of the defining factors. There are others. Okay. So the first one is the one that I told you, we only work with real problems, so no academic toy models. Second, we combine quantum computing with test networks and with other techniques, which are called quantum inspired. Okay.

And third, and this is very important. We try that, the solutions, we try that our solutions and our algorithms you know, they must be as easy to use as possible. At the end of the day one thing is a research department at a research center, and another thing is a company where you are building a product. Okay. So if you do research, you can do your research for your colleagues, for the rest of the people that are quantum engineers or, or, you know, quantum physicists. But if you are building a, if you’re in a company and you are building algorithm sense or, and solutions for the, for the industry, then, you know, the guy that is at the other side, they are not all these people. They are not quantum engineers. Okay. So, so they don’t need to know about Hilbert spaces and quantum mechanics to use whatever you produce. So you need to provide solutions that are, you know, the most powerful solutions in the world, but also super simple. Everybody needs to be able to, to use that because then you just, you know, you can access the whole market. I think that these are the three defining factors of, of Multiverse. And those are the ones that have, you know, set us a little bit apart from, from other quantum software companies.

🟢 **Steven Thomson** (18:00): So you mentioned software there. And if I understand, right, there are two types of software that Multiverse is specialized in. One is software to run on actual quantum computers. And one is more, I think you call it quantum inspired. So algorithms that can be run on classical computers. When Multiverse launched quantum computing was, and I guess still is, very much in its infancy. So are there examples of algorithms that can be run on current classical - current quantum computers, sorry - that can actually solve real world problems at the moment.

🟣 **Román Orús** (18:36): Yeah, actually that’s a good question. And that’s one of the also one of our defining factors, you know, many people think that for running something useful on a quantum computer, you need a quantum computer with 1 million qubits, such as a one that will have I, I don’t know, 100 years hopefully, or something like this. Well, that’s not true. Of course, if you have a 1 million qubit quantum computer, you can just do anything. You don’t have to think very hard to find applications. Okay. The hard question is, well, you know, we have these prototypes right now. They are noisy. They have few qubits, maybe 12, 20, 30, something like this. And then the question is, can we do something useful with these machines as, as of, as they are today? Okay. We don’t want to wait until we have a 1 million qubit quantum computer. I want to use these machines already for something useful.

Okay. And the answer is, is yes. So, you know, different applications of a quantum computer are gonna come at different speeds. Okay. For some applications say for, for factorization, for instance. So for this, you need a…or for, for searching on a database with Grover’s algorithm. For this, you need, you need a very powerful quantum computer with many qubits, error correction and so on, but there are other applications, okay, and other algorithms that are better suited for these machines and prototypes that we have now, you know. We think that there are two that are very promising. One is quantum optimization. And the second is quantum machine learning. Now for quantum optimization, there is this machine by, by DWave, it’s a, it’s an annealer, it’s a quantum annealer, and it’s actually providing very good results in some real life problems with real data. We’ve, we’ve seen that.

Okay. For quantum machine learning it turns out that the quantum machine learning are, there are many quantum learning algorithms that are, that can be run actually on, on current devices. They don’t require too many qubits. This is surprising actually, but, but it’s true. They don’t require because in quantum machine learning, many times, you don’t care too much about the speed up, perhaps what matters to you most is the precision of, of the algorithms, say for a classification problem, if you want to detect fraud. So 1% extra of precision in your algorithm turns out to be a huge amount of money of savings for the, for the tax agency, for instance, okay. Or something like this. Turns out that some of these quantum machine learning algorithms don’t require of, of big capabilities of a quantum computer. And however, they are extremely difficult to reproduce, okay, with non-quantum say machine learning, including neural networks, deep learning and so on. And this has even been certified in, in some situations by people such as, Google. So, you know, with the machines that we have now, this type of quantum machine learning is gonna be one of the first applications of a quantum computer. And of course, this is very important because this has applications in, in essentially anywhere. Okay. Machine learning nowadays is just anywhere in our lives.

🟢 **Steven Thomson** (21:25): So you mentioned the two main avenues, there are optimization problems and machine learning. You gave a, a few examples, but what are, what are the, the sort of big picture goals? What can these techniques do for the financial market in particular? And why did you choose to focus on finance as opposed to any other market?

🟣 **Román Orús** (21:44): Yeah, so we started focusing on finance because there were lots of problems, okay, that could be tackled with quantum algorithms…many things related to optimization and machine learning and other types of algorithms. Okay. Such as stochastic algorithms and so on. But in particular for optimization machine learning, there was a lot. And, and, you know, also the banks are, are very aggressive. They are consumers of algorithms, as I was telling you. And as soon as some bank starts using some, some computational tool, the rest of the banks need to start doing the same thing, because otherwise they lose advantage, and the fight between them is, is, is amazing, you know. So that was a natural thing to, to, it was a natural field to start with. Okay. It was just natural. And and then in terms of actual problems, while, you know, there were some proposals, for instance, for doing portfolio optimization. Okay. So you have an investment portfolio say for your pension funds. No. And, and you need to decide how to invest in order to maximize the returns minimize the risk so that when you retire you just get the maximum returns, okay. So when you mathematize this and put this in terms of formula terms on that is a typical intractable problem, it’s NP hard and all the solutions that you have with classical computers, they are suboptimal. And this is a typical thing to, to throw to a quantum computer.

🟢 **Steven Thomson** (23:04): So it’s, it is really provably an NP hard problem to do portfolio optimisation? Wow. I didn’t know that.

🟣 **Román Orús** (23:09): It is. Yeah. Yeah. It is. It is as hard as the, Ising model in physics, which is also NP hard, a generic Ising model.

🟢 **Steven Thomson** (23:16): Mm. So I guess this is where the connection comes from with the many-body physics then. So using techniques to solve the ground states of these many body physics problems, which can also be NP hard, I guess, in the classical case…

🟣 **Román Orús** (23:26): Exactly. It’s, it’s a very simple connection. You have these you have this complicated optimization problem in industry. Okay. It could be portfolio optimization. It could be optimizing the air traffic. It could be optimizing, I don’t know the configuration of a protein, whatever you want. You describe it in terms of a cost function that you have to minimize, and then you put the variables and the variables at the end of the day, you can discretize them in terms of bits. Once you write it in terms of bits, this is just as a classical spin model. Okay. And then you are in the realm of many-body physics, you can apply anything that you learn in body physics to this problem. Yeah. That’s how you make the connection.

🟢 **Steven Thomson** (24:00): I see. And if if you make this mapping to a classically NP hard problem, then that’s a classical problem. So where does the quantum advantage come in? Why, why use quantum computers to solve this type of problem?

🟣 **Román Orús** (24:12): Yeah, because the problem is classical, but finding the solution to the problem is, is extremely hard. So, you know, it’s classical. It just means that as a matrix is a diagonal matrix, but, but finding the configuration of the spins or of the bit variable that, that minimizes the function, that’s, that’s NP hard. That’s very, that’s very complicated. So they are…classical computers, the best thing that they can do is to explore the landscape of possible solutions, but it’s very inefficient. Whereas a quantum computer can put all these possible solutions in superposition, okay. And can explore the landscape of possible solutions, much more efficiently than what a classical computer could ever do. Yeah. That’s one of the reasons why quantum computers are much better for this type of problems also because they allow, you know, tunneling between different configurations thanks to the tunnel effect. They allow also stronger correlations in the, in the register. Okay. Thanks to quantum entanglement and so on.

🟢 **Steven Thomson** (25:05): Mm-Hmm , okay. Interesting. One specific thing I want to ask you about here is you’ve mentioned tensor networks a few times, and of course, a lot of your previous research has been on tensor networks. I actually come from a many-body physics background, so I’m quite familiar with using tensor network techniques to, to study many body problems. How do tensor networks have an application in something like finance? Because I guess if I think of a tensor network, I think of, okay…I want to, to look at some kind of one dimensional quantum system that has, for example, area law entanglement, or some quite specific properties where these these techniques have been developed and where they work very well. How is it possible to use something like tensor network techniques in finance?

🟣 **Román Orús** (25:45): Yeah. So, so you just mentioned one-dimensional many-body systems. So so, you know, there are, for instance, as an example, so there are many optimization techniques for, for one, many systems. One that is very popular is the DMRG, the density matrix renormalization group, but there are others, but at the end of the day, you know, with tensor networks…tensor networks at the end of the day is nothing but a mathematical tool to represent high dimensional vectors. That’s it. Now it turns out that this high dimensional vectors show up in quantum many body physics. Okay. But they also show up in many other places, for instance they show up in machine learning also. So you want to describe neural networks for instance, or you want to describe some support vector machines or some other type of machine learning algorithms. At some point you are gonna hit high dimensional vectors.

And this doesn’t have anything to do with quantum mechanics, but once you have the same mathematical formalism, you can throw tensor networks also, also there. Okay. Moreover, so you know that there are optimization algorithms for tensor networks, but they are for finding ground states of Hamiltonians. Okay. But now you can decide, well, you know, my Hamiltonian is, is a classical Hamiltonian and then I can also use tensor networks. Okay. So I mean, people have been using tensor networks for classical systems since the sixties. Okay. The first one that they started, this was Baxter doing exactly solvable models. And he was not…Baxter did nothing quantum. He did everything classical. Okay. So how to make the connection? Well, I just told you, you have a classical optimization problem in finance. You want to optimize your portfolio, you rewrite it in terms of bits and suddenly you think that those bits are spins and, and that’s where you have your tensor networks you can just throw your favorite 10 network optimization algorithm to optimize a portfolio. For instance, that’s, that’s one, that’s one thing. And also in machine learning, you can just use tensor networks to improve, or even to develop new machine learning algorithms. You can also train a tensor network to recognize faces, detect fraud and these type of things in the same way as you train neural networks.

🟢 **Steven Thomson** (27:38): So what are the plans for Multiverse Computing going forward? Are you continuing to focus on the finance market or do you have plans to broaden your scope and look into other industries where optimization problems occur?

🟣 **Román Orús** (27:50): No, we are, we are broadening the scope. Actually this is something that is already happening this year. Right now, as, as we are speaking 2022, we, we have started to work on our fields. Also, we, we have started to work in energy, okay. With some well known energy providers. We’ve started also to work on manufacturing on logistics. Okay. Problems such as predictive maintenance. You want to a factory and you have to predict when a, when a machine is gonna fail. Okay. This is a typical machine learning problem. These type of things we are also starting to work towards the end of this year in, in health, in chemistry. So we are diversifying our portfolio of, of solutions.

You know, we started in finance because for us, it was very obvious. And that was the thing that we understood the most. We had several pilot projects in finance and were very successful. But the core of the algorithm of the things that we did in finance, I mean, that’s, that’s universal. You can apply that to, to anything at the end of the day, where we use optimization, machine learning and something else. And then, you know, the application is easiest for finance. Okay. But now, you know, the same say optimization algorithm could be applied to problems in energy and you just have to change the data and change the front end, and so on. So that’s, that’s what we are doing now. We started in finance, but now we are exporting all that we learn in finance to other to other fields.

🟢 **Steven Thomson** (29:14): It’s really interesting to see how these tools developed in fundamental physics can be applied in this way. I guess speaking as a theorist, I remember coming to the end of my PhD and looking at what my options were and thinking, well, if I want to use the skills that I’ve learned in my PhD, research seems like the obvious way to go. Academic research seemed like the obvious way to go, but it feels like now there are more options, I guess, than there were however many years ago that was. So do you look for, for physicists to employ, do you only employ physicists or do you also look for people with other skill sets?

🟣 **Román Orús** (29:47): Yeah, no. We, we are actually looking for, for people with a wide variety of, of skills. I mean, of course we are looking for physicists. We are looking for quantum engineers. We are looking for people that know also about tensor networks, but we also need you know, people that know about machine learning, software engineer, software engineers, so people that really know how to code professionally. Okay. This is also very important for us. So yeah, we, we are looking for a wide spectrum of, of people and, and here in Multiverse there are people actually for, from many different backgrounds. So, I mean, it’s not just people that did quantum computing as a PhD. We also have particle physicists here and so on. So at the end of the day, what matters to us most is not, I mean, what, you know, your, your background, your knowledge is important, but what matters most to, to us, or at least to, to me is, is the attitude.

Okay. So, you know, if you have the right attitude, if you have a PhD in particle physics, you are gonna learn quantum computing. That’s not a problem. Okay. but but if you know a lot about quantum computing, but you don’t have the right attitude that…that’s not gonna work. So so, so that’s what, what most matters actually. So you want to have this attitude of actually learning, being able to, you know, try to navigate many different things. Machine learning, tensor networks, quantum computing and software engineering and some other stuff. That’s what that’s, what’s important. So, so we are looking for a variety of roles right here. Technical people, of course but not just physicists, also mathematicians computer scientists, engineers. Yeah. A little bit of everything.

🟢 **Steven Thomson** (31:23): So it sounds like there’s a lot of different things going on. How do you manage being a researcher and also being involved in Multiverse at the same time? These both sound like extremely busy jobs.

🟣 **Román Orús** (31:34): It, it, yeah. It’s, it’s, , that’s true. So I don’t sleep too much apart from that, apart from that. Well, so the lucky thing is that both let’s say activities are complimentary. So, so, so there is, it is actually like two sides of the coin. Okay. I’m essentially doing very similar things as research and as in, in the company Multiverse you know, the research that I can do on quantum many body physics or on quantum algorithms. Okay. that’s of course, useful for multiverse and anything that we develop here at Multiverse about software and so on. It’s gonna be very helpful also for doing research on, on the other side. So at the end of the day, it’s just like a natural continuation, right. There is really, I mean, both of them are attached to me. They are not independent. and I just try to navigate both of them, but of course you are right. This implies you know, I don’t have too much free time. I have to run the company on top of that. I have to supervise my PhDs and students and also postdocs and so on. And but I, I think it’s very exciting. I mean, I, I actually feel very lucky that I’m able to, to do this. I think that is a very unique moment in, in history where quantum computing is actually becoming a reality and it just don’t want to be, let’s say, excluded of, of this, so, so I think that’s, that’s a very interesting time.

🟢 **Steven Thomson** (33:01): Well, you mentioned there all the different people who are part of Multiverse and also running a research group and having PhD students. So one question that I like to ask every guest here is that physics has historically been a very male-dominated field. And in your sort of role where you’re employing people, both in your research career and also for Multiverse, how do you approach this, this issue? How do you ensure that your workplace is made up of a diverse group of people and is as welcoming as possible to as wider range of people as possible?

🟣 **Román Orús** (33:33): Yeah, so that’s, that’s very important for us here at Multiverse. So and also, and also in the research at the DIPC. So, you know, particularly at Multiverse, we receive applications from, from everywhere in the world. Okay. Literally from, from everywhere. And, and we receive applications that are, you know, that are really, you know, very different from…each one is very different to, to each other. So, so in order to ensure that this is an interesting environment, I mean…well, we have a 30% of, of women here at Multiverse, including technicians and also, you know some sea level people and, and some of the directors included. And and we, we take that very seriously. There must be a balance, okay. There must be a balance.

And, and we when we do our hiring process, we take that into account. We take that into account. So, and also in terms of, let’s say, geographically speaking, we, we just hire the talent wherever it comes from. And, and then it’s naturally diversified. Of course, we have contacts and agreements with the local universities and, and here at Multiverse, we have people from Taiwan, from Mexico, from Moroccl, from Spain, from France. I mean, it’s like really an international company. It just happens naturally, people apply. And because you have a lot of visibility and then, you know, we just hire the people that we think that we have to hire. And naturally it’s, it’s very diverse. Okay. If at some point we notice that there is a strong concentration of something, we try to break it. We try to, say “Hey, look, maybe we are hiring too many people from the Spain”. This could happen. Okay. Actually it happened not so long ago. And then we decided, well, okay, that’s true. And maybe we have to make this more international because we also have offices in Toronto and we also have offices in Paris and so on, and, and this is needed, no? So when we do the hiring process, we take all this into account. Okay. We, we try to balance, okay, the company as much as we can. And, and this is actually, you know, creating an extremely nice atmosphere extremely nice atmosphere to work in. Yeah.

🟢 **Steven Thomson** (35:29): I see. So it is something that you actively consider when running the company yes. And actively work to maintain. Interesting.

🟣 **Román Orús** (35:34): Yeah. Yeah. It’s important. It’s important because, I mean, I think it’s, I think it’s healthy. It makes the company healthier. Yeah. Otherwise it wouldn’t work so well.

🟢 **Steven Thomson** (35:45): And do you know if if Multiverse is more diverse than other companies doing similar things or comparably diverse to other companies, do you have any data on how Multiverse compares…?

🟣 **Román Orús** (35:56): No, this, I, I don’t have, I don’t have data on, on the other companies. So I cannot compare. Okay. I think it’s pretty similar say to other companies that I’ve seen say in Canada, but or Toronto, for instance, where we also have a very strong presence Compared to European companies I don’t know, there are not so many companies to compare with, so, and the ones that I know they’re smaller than us, so then it’s difficult to compare.

🟢 **Steven Thomson** (36:22): I see. Well, maybe to, to round this off, then what advice would you have as someone who has experience in both the research and the industrial environments? What advice would you have for someone looking to get into the fields of quantum technology, quantum computing…?

🟣 **Román Orús** (36:40): Well work a lot. . Yeah, no, but I will say that if, if you have a clear idea of what you want to do and you really like it, okay. You, you really like, you really need to like, and to love what, what you do, because you know, you’re gonna spend most of the time in your life working and you better like your work, or better don’t do it because and that’s, that’s very important. You must have fun. I always told that to, to my students when I was teaching in mind. And and I still here I have a research position, so I’m not teaching, but I still say that to everybody, if, if you are not having funding for two or three days in a row, then there is a problem. Okay. So so you need to, to really like what you are doing.

And then I will say that, well, you know, keep tuned to all these opportunities that are happening, study and work very hard. Quantum machine learning, optimization…machine learning is very useful. Okay. Quantum computing is also very useful. Try to have as much background as, as you can. There are lots of free courses, lectures, and so on, on the internet that you can do. And then they just give you a very good background on top of whatever you do at the university. So I would just try to go for all these free resources also say, and, and keep an eye on what is moving and, and also don’t be shy. So, I mean just go for it, have ambition, try to, to go in the direction that you want to go.

🟢 **Steven Thomson** (38:01): Perfect. Okay. I think that’s a good place to wrap it up there. So if our audience wants to learn a bit, a little bit more about you where can they find you on the internet, on social media, anywhere like that?

🟣 **Román Orús** (38:13): Yeah. Yeah. You just Google me and I, I show up everywhere, so…

🟢 **Steven Thomson** (38:17): Okay. We’ll we’ll leave links to your website and social media profiles on our own website, along with this podcast episode. So thank you very much, Professor Román Orús for your time today.

🟣 **Román Orús** (38:27): Thank you. It was a pleasure.

🟢 **Steven Thomson** (38:29): Thank you also to the Unitary Fund for supporting this podcast. If you’ve enjoyed today’s episode, please consider liking, sharing and subscribing wherever you like to listen to your podcasts. It really helps us to get our guest’s stories out to as a wide audience as possible. I hope you’ll join us again for our next episode. And until then, this has been insideQuantum, I’ve been Dr. Steven Thomson and thank you very much for listing. Goodbye!