In this episode, we explore the revolutionary potential of quantum computing with Simone Severini of AWS. From Simoni’s unique career journey to the fundamental principles of quantum mechanics, we delve into the competitive landscape of quantum technology and its implications for the future. Highlighted is the excitement around its applications in fields like cryptography, AI, and material sciences, as well as the challenges of error rates and machine calibration. The episode concludes with insights on the importance of human interaction in driving innovation.

Introduction to Simone Severini

Jeff Dance:
In this episode of The Future of, we’re joined by Simone Severini, General Manager of Quantum Technologies at Amazon Web Services, also known as AWS, to explore the future of quantum computing. Before joining AWS, if I can give some background on Simone, he was a professor of physics at University College London, where he helped establish quantum computing with the computer science department and fostered industrial collaborations with many big companies like Google, Lockheed Martin, and Siemens. At AWS itself, he’s contributed to launching Amazon Bracket, the AWS Center for Quantum Computing in partnership with Caltech and the AWS Center for Quantum Networking in collaboration with Harvard, and also the Amazon Quantum Solutions Lab. He earned a PhD in the Quantum Information and Computation group at the University of Bristol and did his postdoctoral research at the Institute for Quantum Computing in Waterloo, Canada. He’s been involved with many of the earliest quantum computing startups, including playing a role as a technical co-founder. So we’re really excited to have you here, Simone, given your deep experience in quantum. And what an exciting time with where we’re at and with everything that’s advancing. It’s a really exciting time to think about where we are and where we’re going in the future. Anything else you’d care to tell the listeners about yourself and your journey into quantum computing?

Simone Severini:
So first of all, thanks for the opportunity of this conversation. My journey in quantum computing was random. I met a lot of interesting people. And because of that, I ended up doing different things. I worked a bit in academia. I worked a little bit in the industry. This is a space which I still think is extremely exciting. I started being interested in quantum computing when I was an undergrad. And now, after maybe 27 years, 27, 28 years, I still believe that this is a very exciting space. It evolved in ways that I don’t know if they were really predictable a couple of decades ago. The field was very academic. Academic means that even if people were talking about quantum computation, there were experimentalists that were doing experiments related to quantum physics on one side, not necessarily quantum computing, and on the other side, theoreticians that were trying to come up with new algorithms or some new ideas in quantum information theory to better understand quantum physics itself. And somehow, it has been a surprising journey for this space because now, of course, a number of people strongly believe that quantum computing is going to be very impactful. It’s exciting.

Jeff Dance:
It’s exciting, and it seems like a collaborative race, in a sense. It seems like every big tech company has an investment or a play in quantum and even at the country level, right? We see countries that are investing a lot of money, India, Australia, obviously, the US, but a lot of countries that understand this is a massive leap forward that can really change the game. It’s similar to AI. So it’s a great topic to be thinking about as we talk about today and the future. Before we get started on the current state, just on a personal side, what do you do for fun?

Simone Severini:
I like fiction. I like writing and reading fiction. I like authors like Borges, an Argentinian author. I like things that are fictional but feel like essays, so that they seem to be talking about something which is scientific but is not scientific.

What is quantum computing?

Jeff Dance:
Interesting. I love it. I love it. So we’re talking to a real person here. I had to establish that. We’re not talking to an agent here. This is a real person. As far as I’m sure we’ll have some folks that want to hear some of your depth of expertise. And then there’s others that really don’t know much about what quantum computing is. And so if you’re at a dinner conversation networking with some people and they’re like, describe quantum computing to someone unfamiliar with it. What do you say when you kind of give the general description of quantum computing?

Simone Severini:
It’s a multidisciplinary area. There is a scientific component, an engineering component. The goal of quantum computing really is to try to understand what the ultimate limits of computation are. Meaning we live in a physical world, and computers are machines that exist in our physical world. And so they need to be governed by the laws that govern the world in which they live. If you change physics, then you can change computation, which is a very deep idea that was developed, I guess, at the early stages of this area. Now we call quantum computation information. So quantum computing tries to understand what nature allows us to compute in terms of the amount of time it takes to solve certain problems and the amount of resources needed to solve problems. This is quantum computing. Quantum computing specifically tries to look at what you can do with quantum physics when it comes to computation.

And this is somehow the theoretical aspect of it. If we go down to what you might call the practical aspect, if we try to build machines, quantum computing is about building some machines that encode information in objects whose behavior is governed by quantum physics. These are very small objects, nanoscopic objects like atoms, which are the components of matter, or photons, which are the components of light. And quantum computers are then machines that manipulate the physical state of these tiny, small objects with the purpose of performing computation.

What is computation? Computation is a process that, given some initial configuration, in this case, an initial configuration of an object made of matter, transforms it via a sequence of operations into some final configuration. This final configuration should tell you something that is interesting for you. This is what it means to solve a computational problem—starting with some configuration of matter in the physical world, modifying it in some way, and then looking again at what you came up with after you have done a number of operations, a sequence of operations. In this final configuration, you will be able to read something that you could not read at the beginning of the process. This is the way I like to see computational dynamics, algorithms, right? So, of course, an algorithm; you could interpret an algorithm almost like a recipe for cooking something. Take half a kilogram of pasta, take a pot, put the water in the pot, get the pasta, and put it in the pot. That’s an algorithm, right? But you see, even in this example, which I’m not sure how good it is, but I spoke about something that is in the physical world. Take pasta, take a pot, put the water in the pot. An algorithm sometimes seems to be an abstract procedure because we think about programming language, for example, right? So get your favorite programming language and it takes x. If x is larger than y, then do this. These are operations, okay, but this x needs to be implemented in something which is in the physical world, like your kilogram of pasta.

Jeff Dance:
Right.

Computation as a physical process

Simone Severini:
One of the most beautiful aspects, in my opinion, of quantum computing is that it has been historically an opportunity to think about computation as a physical process. We shouldn’t underestimate this idea because, apart from quantum computing, to some extent, it turned out to be almost an accident. There were people thinking about computation and thermodynamics, computation and physics. Then, okay, what about if you consider quantum physics? Great. But then I’m not sure people expected that some scientists would come up with algorithms to solve some computational problems that have nothing to do with physics.

Today, people talk about quantum computing potentially breaking some cryptography protocols with quantum computers of the future. This is almost an accident. Quantum computers are devices that manipulate matter with some sequences of operation applied to a small subset of the objects that compose the system, like two atoms at a time, two electrons at a time, and so on. You can’t call a quantum computer a matter manipulator or a quantum correlations manipulator. Nobody understands what that would mean. But that’s what a quantum computer is, so it’s even before a machine that implements algorithms for solving an optimization problem, right?

The physics connection

Jeff Dance:
Yeah, I appreciate you breaking it down to some of the components. You have a background in physics as well, right?

Simone Severini:
So, yeah, my background is a bit unorthodox, and I’m in the wrong place because I studied philosophy as an undergrad. But then, while studying philosophy, I learned, because my teacher gave me a couple of books to read, that in logic, it’s interesting that logic seems like something very abstract, far from the physical world, right? It lives in this hyperuranium of ideas, platonic, platonic world of mathematics, where things are somehow discovered rather than invented rather than seen in the physical world.

But if you change, if you change physics, then you change logic as well. And once you start thinking about quantum physics, there is a whole different type of logic that applies there. In fact, this is somehow an opportunity for me to tell about what is the key ingredient in quantum computation, which is the superposition principle. People talk about qubits all the time, right? That’s the fundamental unit of a quantum computer. And so what is a qubit? It’s the quantum analog of the bit. Every physical object in the classical world, meaning the non-quantum world, that can be prepared can be set in two different configurations that can be distinguished from each other, like this pencil here. If I keep this pencil like this, in this position, I say, okay, this is a bit one. If I put it like this, this is bit zero. And I do this, I do computation. So one becomes zero, zero becomes one. This is a classical bit.

So if I have a billion pencils, then I can manipulate a billion bits. So a qubit is the quantum analog of this. And it cannot be represented with some objects that I can experience with my senses, an object around me. But, for example, I could look at the electron in an orbital around an atom, and let’s say I inject some energy and I move this electron up. I say that’s qubit one. I put some energy and the electron goes down, and this is qubit zero. But then there is a special property of quantum physical objects for which I could have this electron in a configuration, an ontological configuration, that is very special, which is zero and one. When I observe this object, with a certain probability, I will observe zero, and with a certain probability, I will observe one.

But before the measurement process, before observing the object, I can assume that this is zero and one. You see, from the logical point of view, this is very counterintuitive because, in logic, something is either x or not x, not x and not x at the same time. Either it rains, or it doesn’t rain. It’s not that it rains and it doesn’t rain at the same time. But this is one of the phenomena, a very special phenomenon of quantum physics, indeed counterintuitive, but this is responsible for a form of parallelism that cannot be obtained outside quantum computation, cannot be obtained by computers that are not quantum computers

Computational problems

Jeff Dance: Because today’s traditional computers are more about finding a path. Tomorrow’s quantum computers offer a parallel processing aspect that allows for a much higher order of magnitude of processing around algorithms and computation. It’s just a different level. It’s more tied to the law of physics and tying back to atoms and quantum physics, essentially. Is that a good summary? I’m listening to you, but thinking about hearing your thoughts on superposition, one of those key concepts of quantum is interesting. I appreciate you breaking that down.

Simone Severini:
Yeah, because in the end, if you think about a quantum computer, right, there is a lot of mysteriousness surrounding it. You have logic gates like in classical computation and operations that manipulate bits. In quantum computing, you have exactly the same thing. You have logic gates, but then you have this special gate, which creates superposition. And this special gate is responsible for moving you from classical computation, meaning non-quantum computation, to quantum computation. And this is very interesting. This step, which is so small, makes a big difference. Big difference in what way? Every computational problem out there has an associated complexity, meaning that as a function of the size of the input, you will need a number of steps, a number of operations, to solve that problem.

In quantum computation, there are certain computational problems for which the number of steps is much, much smaller. The term should be exponentially smaller, meaning that instead of requiring two to the n operations, you have n operations. This means that in theory, there are certain problems that may take a billion years even for the fastest supercomputer that we could ever build without quantum physics, but maybe a quantum computer could take a day.

And theory tells us that this is possible. Building machines, building quantum computers that will allow us to that for problems that are actually important and have an impact, this is a very, very difficult problem, right?

5 minutes vs 10 septillion years

Jeff Dance:
It’s a difficult problem that’s been worked on for over 10 years, right? But there’s been a lot of advancements recently. And one of the stats I heard, just to echo what you mentioned, was that they were able to process a… They had a significant milestone in computing a problem in five minutes that they said was going to take, I think, 10 septillion years.  And it’s mind-blowing to think about that.

Simone Severini:
Yeah, I don’t know my zeros there in a septillion, but so you see, there has been a number of experiments out there. I use the term “experiment” because this is where we are today. We are still in our experimental phase, a prototyping phase, R&D. It’s a long journey to build quantum computers at scale that will solve important problems. By the way, I’m not saying that the problems that quantum computers solve today are not important. That would demean the field without any reason.

But you need a lot of qubits. You need hundreds of thousands to millions of qubits in the fullness of time. And then you need a level of precision that we don’t have yet. What does it mean to have a level of precision? You see, quantum computers are really temperamental machines because of quantum physics itself. When you have your qubit, in order to find the state of the qubit, you need to observe it. Observation somehow perturbs the qubit, changing its state. When you have this qubit in a state which is 0 and 1, when you look at it, you have either 0 or 1, so you change the state. And somehow quantum physics objects tend to change their state because of their interaction with the external world. This is one of the mysteries of quantum physics.

To keep a quantum computer isolated from these external perturbations is very hard. This means that a quantum computer tends to lose its own physical state very, very quickly. Each time we apply an actual operation, we have a probability of error. So this means that we need to improve the precision to manipulate matter when we build quantum computers. We need to use some kind of redundancy. We need to encode information. If we want to protect information, we encode it in a single qubit. There is something called error correction, which prescribes us to encode information into a lot more qubits with some very complicated mathematical ideas, beautiful ideas, that allow us to protect information.

Errors in quantum computing

Jeff Dance:
In 2024, you said that one of quantum computing’s big challenges is overcoming these error rates, right? And would you say that’s still true today, that we still have a lot of obstacles in overcoming the error rates?

Simone Severini:
There are lots of different proposals to do that. There are proposals that are at the mathematical level, and people are trying to implement them in the physical world, like building machines. Which one is the best error-correcting code, meaning the best procedure for encoding information to protect quantum computers from errors? It’s unknown. There are many different proposals, and there is innovation happening. But I’m very happy, very excited.

To see that the entire industry is giving much more importance to error correction right now. There was a phase in which people were trying to build quantum computers with as many qubits as possible, right? This could be an interesting opportunity because maybe in that regime where you don’t protect quantum computers from error, maybe there could be still something very interesting. Maybe you could solve some beautiful computational problem, but this didn’t happen. The quantum computers built so far have been very useful to teach us how to build quantum computers of the future.

Right now, the industry seems to be focusing very deeply on error correction. By the way, one important thing that I failed to say is about the problems that quantum computers are solving and are going to solve in the future. There is a lot of talk about problems that have to do with man-made situations like optimization. Think about financial organizations that are interested in portfolios. Think about risk analysis or even machine learning.

Quantum computing, when it was conceived originally, was about simulating physics. Because in order to… I will use a term that physicists probably won’t like. In order to build a digital twin of a molecule, meaning…Instead of going into the lab and getting some molecules and manipulating this molecule, put two chemical substances together and see what comes out. Instead of doing that, doing that in silico, meaning with a computer. So writing down what’s going on with a molecule on a computer, it takes an immense amount of memory, an enormous amount of memory, to the point that there is no computer today that can really tell us exactly the behavior of molecules, no matter how big.

We would build that computer unless it’s a quantum computer. Indeed, quantum computers, when they were conceived originally, were thought of as machines to simulate quantum physics itself, because it’s very memory costly to do that with a traditional computer. The very first applications of quantum computers today and in the future will be about simulating physics. And I don’t think this makes quantum computers less interesting. Actually, in my opinion, this is what makes quantum computers very, very interesting. To some extent, the fact that one day a quantum computer will solve an optimization problem in 10 minutes instead of 50 minutes. Okay, excellent. Maybe this will have some impact in some businesses. I hope so. But to me, it’s much more exciting that we can use quantum computers as catalysts for better understanding physics itself, because that’s what will enable deep innovation.

Deep innovation

Jeff Dance:
Deep innovation like solving medical challenges, as an example, where we have the physiology of ourselves that is also physics-related, or climate change, where the physiology related to climate change has physics involved. Stuff like this, they’re bigger, deeper, innovative problems versus the surface-level stuff that your traditional computers and AI might catch up to.

Simone Severini:
Yeah, now, of course, climate change is a very difficult classical problem that has to do with stuff that maybe is not immediately related to quantum physics itself, but yes, I agree with what you’re saying. In fact, I would even take a further step and say that we have no idea what the applications of quantum computers will be. There is a chance that quantum computers will be used to discover new materials, for example. In the Middle Ages, there were alchemists who wanted to turn lead into gold. Maybe a quantum computer will be a machine that will allow us to design materials according to specific properties that we want. Or maybe quantum computers will allow to do certain things, like maybe generate data that come from the quantum physics world, and will allow us to come up with a new theory that puts together quantum physics and gravity, because these two theories don’t mesh well with each other historically. And this could have a very interesting impact. Or maybe quantum computers will allow us to understand that quantum physics is wrong, and we will come up with a new theory of fundamental physics that describes things in a different way. I’m not sure if it was Teller, who was saying the science of today is the technology of tomorrow. That’s the way I like to see quantum computers. Quantum computers as machines to unlock innovation

Jeff Dance:
Yeah, so it’s sort of like this platform for innovation. It’s another tier of innovation, not only for understanding physics but for solving some really, you know, for discovering all that it can do. There’s still a lot of discovery because it’s a completely different field. Yeah.

Simone Severini:
Absolutely. I love the way you put it, concisely. In fact, thank you. So a platform for discovery, right? And then what this discovery… In fact, look, this is a beautiful… someone asked you what’s a platform for discovery, right? That’s a beautiful thing. I’m going to…

Jeff Dance:
Platform for discovery. Yeah.

Jeff Dance:
Deep innovation and new world discovery. Anything is possible. It almost seems godlike in a sense where it’s like, we’re…

Simone Severini:
Exactly. Yeah, yeah. There are, of course, laws of physics, right? So, in fact, we should only set the right expectations. It’s not that quantum computers would be a panacea, right? Every computational problem quantum computers will chew the data and then give you the answer, and it will take time, of course.

For example, some people say, a quantum computer gives you the answer to a computational problem instantaneously. Well, no, of course not, because you need to apply your gates, your operations, and that takes a certain amount of time. Computational complexity, in the end, is the ultimate arbiter, the ultimate referee of what you can actually do and what you cannot do in physics. But I think that quantum computers as a platform for discovery, a platform for innovation, is very beautiful.

Jeff Dance:
Right. Yep. Quantum gates.

Simone Severini:
And in my opinion, you see, apart from that, I like to see computers overall as devices for observing the world. So a computer is something that tunes your attention to certain information that otherwise would be hidden from you.

Jeff Dance:
Right, right, right. That ties back to your point of discovery. And I think we’re starting to see a lot more of that with AI recently, right? With just that notion of like, I’m going to discover something that I didn’t know about before with that interface.

Simone Severini:
With this black box, right?

Quantum computing & AI

Jeff Dance:
The AI black boxes that also have a lot of error rates, you know, upfront, right? Where you’re trying to solve for, there’s a parallel thing. So what’s your take on, you know, the merge? You know, it seems like, you know, as we think about the future, the convergence of technologies has been really interesting, not just the existence, but the convergence. So do you see quantum computing as a huge accelerator for everything we’re doing with AI?

Simone Severini:
So, if I think about the future of computing, there are a number of things that seem to be emerging, right? So first, that obviously, semiconductors have constraints and we need new approaches. And these new approaches will specialize computation. And this is already happening out there, right? There will be computers for solving different problems. For example, now there are systolic arrays that do something, GPUs do something else, and CPUs do something else. And of course, you will need a crystal ball here, right? So, futurology is not a science, but indeed…

Jeff Dance:
— discussion.

Simone Severini:
There will be different types of computers for solving different problems. Let’s say you want to simulate some quantum physics-related things, you want to simulate energy physics. Now, maybe you built a particle accelerator. Maybe one day, you can do that with a quantum device, which we will call quantum physics simulator, quantum computer, or whatever name it will have.

Jeff Dance:
Yeah.

Simone Severini:
And will be used for solving that problem that cannot be solved in other ways. Then we will have a computer that maybe uses thermodynamics, fluctuations, or heat for maybe generating some distributions and extracting some information. We’ll have maybe sensors out there for measuring stuff that then is going to be given to some machines locally. These machines will do something, and they will need to have a very, very specific type of hardware. And all of this will need a kind of software co-design, meaning that for each type of computer, you will need a special type of software, and you will design the hardware on the basis of what you can do with that machine, which also gives prescriptions on the type of software that are going to run.

Jeff Dance:
I think it’s interesting to think about quantum computers for certain types of problems in the future, this platform discovery. But then that requiring certain types of software, you know, correlated with that as well. That there’s some specialization as we think about where the quantum computing fits.

Simone Severini:
For sure. If you think about, and of course, you mentioned artificial intelligence, machine learning. See what is happening today already, right? With many different types of hardware that people are using and the different ways there are to compile this hardware and the different architectures.

But you see, I don’t feel that this specialization will only be architectural. Specialization will have to do with physics itself.

“We will do science in partnership with machines”

Jeff Dance:
Physics itself, yeah. That’s deep, that’s helpful. What are some of the things you’re excited about right now? And then I want to jump a little bit forward and talk about what do you think will come 20 years from now? I know that’s hard to predict. But what are some of the things you’re excited about right now? And maybe include some of the things you’re working on at Amazon.

Simone Severini:
So I am very excited about error correction in quantum computing. I’m very excited by the fact that people understand that this field requires sustained investments. This is not about the next quarter, right? This is about innovation and discovery. And I remain very curious to see every other day what the community comes up with right in quantum computing specifically. Every other day, there’s some interesting idea emerging. So let me…

So one thing that I really like these days, but this is tied to quantum computing. And please, in case, bring me back to the central topic of the conversation, is automated reasoning and verification. There seems to be a new phase in which people start using computers as assistants in proving mathematical theorems. Mathematics is the language, by using an expression of Galileo Galilei, the world of nature is written, the book of nature is written in the language of mathematics, right? And still today, it seems that this is applicable. So, mathematics is fundamental.

Innovation in mathematics means proving stuff that you don’t know whether it is true. And today, there are a number of very strong mathematicians that start to use automated reasoning for proving theorems. There is a very exciting proof assistant called Lean, which is pushing the boundary of verification. So, while you are trying to prove your theorem, the machine tells you whether what you have written so far is formally correct. This helps solve some problems, for example, the trust bottleneck, right? Suppose that there are ten of us, and we’re trying to prove a theorem.

Jeff Dance:
[laughs]

Simone Severini:
By the way, I don’t prove anything because as a scientist, I’m like Paul Erdős, the Hungarian mathematician, who used to say, when you stop doing mathematics, you are mathematically dead. So I’m mathematically dead for a long time because I haven’t done any science for a number of years.

But I remain curious about what is happening out there. If you want to prove something, let’s say there are ten people, we need to trust each other. We are trying to prove something together. I’m there maybe watching TV. I look at this proof and say, oh, let me check if it’s correct. Then I get distracted. In the morning, I send an email and say, oh, yeah, sure, it’s correct. We are done. Then, because I was distracted, there is a tiny mistake. We write a 100-page paper, and it’s wrong. We believe we proved something, but we haven’t proved anything because there was a tiny mistake. Machines help us overcome this obstacle because we don’t need to trust each other anymore. The machine will verify whether what we have written is correct, which is beautiful, I think. This is something very exciting, which I hope will lead to proof systems in mathematics that come up with conjectures, statements that we don’t know whether they are true or false yet, but also help us come up with new theories and new science.

Simone Severini:
And I want to believe that in the fullness of time, talking about the future, speculating on the future, one day we will do science by working, we will do science in partnership with machines. We will talk with each other as human beings. And we will also talk to machines, maybe in natural language, because mathematics is a semi-natural, semi-formal language. There are symbols, but there’s also terms that are natural language. Maybe we will just talk to machines, and machines will say, look, if you want to prove the following thing, I think this is a strategy. Why don’t we try to do X? Or why don’t you call your friend in Australia who already knows about this subject because he talked about it for the last five years. Okay, let’s get together. You, your friend, and me, machine, and let’s try to prove it. Then we work together with machines for actual scientific discovery. Maybe these machines will also say, okay, for this specific problem, you know what? You need to use a quantum computer. For that specific computational bottleneck, I’m gonna feed it to this machine on the cloud, which is a quantum computer. Then the machine is gonna get back the result, send it to a cluster of GPUs because maybe it needs to do some kind of machine learning task, then send it back to the quantum computer, then send it back to me. In this way, we will do science.

AI interfaces

Jeff Dance:
I like it. So, as we think about the future, the ability to talk to machines, have machines be our partners, maybe typing will be clunky at that point because it will be so conversational. We see some of that with AI today, where it’s like you can talk to AI. It’s like accessing these large language models of different types. There are now lots of language models that have specialization. But I think what’s different here is you’re saying like, hey, connected to this conversational computer, which is very futuristic. We have quantum computers that can be deployed to solve some of the problems that are unique to physics. What’s interesting about it to me is like it’s a very human-like interface. AI has already shown us the beginning of that.

Simone Severini:
So there’s lots of papers these days written on the interface of AI and quantum. These are super hyped subjects, right? When you put them together, it’s an explosive mixture. So I think we need to be open-minded about the possibility that quantum computers will help.

Jeff Dance:
It’s an explosive mixture, yeah.

Simone Severini:
We need to be open-minded about the fact that machine learning is going to be very useful in quantum computing, for example, for calibrating machines, calibrating quantum computers. By the way, there are many different ways to build quantum computers. I haven’t said that, but probably it’s not relevant at this point. I won’t go back.

Jeff Dance:
Lots of companies are doing different ideas around quantum computers.

Simone Severini:
Yeah, it’s a bit like religion. Everyone has their own story. Exactly, exactly. We’re not exactly sure which one is going to be successful. Maybe there will be many successful stories in the end.

Jeff Dance:
Yeah, everyone has their own flavor.

Jeff Dance:
Like many AI models, most of the big tech companies have their own AI platform now.

Simone Severini:
That’s right. Indeed, we have launched a cloud service called Amazon Braket that offers different types of quantum computers. We launched it back in 2019 specifically because customers were asking, what’s going on in quantum computing? OK, the best way to answer this question is to look at it by yourself. Play with this quantum computer, see where they are. But look, going back to your question about AI and quantum, there is definitely momentum. There are even conferences these days that discuss these two topics. It’s early. It’s still unclear whether quantum computers are going to be impactful in AI. What is interesting is that if your data are quantum data, meaning if they come, let’s say, from a quantum process that is happening there, a quantum computer is…

Jeff Dance:
Mm-hmm.

Simone Severini:
I’m not sure what is the best term, connected to this process. I can read this process, be part of this process, because maybe it happens inside the computer itself. There is an opportunity for some potential speed-up for learning what’s going on with the process. There is an opportunity to generate some data that could potentially even be fed to some classical type of computer, like GPUs, and trying to understand what’s going on. So what I’m saying are two things. In fact, three things. First, it’s super early to determine what the interface of quantum and AI is. Second, data that come from quantum physics may be particularly interesting in this space. And third, AI may help quantum computing. That’s what I was mentioning. But maybe there is something else here that…

Jeff Dance:
I mean, even with math, right? If you think about math, is it fair to say that, you know…

Simone Severini:
There could be. There are some overlaps, for sure. Mathematically, there are some interesting overlaps, like something called tensor networks, for example, which is a very exotic term, but yeah, it appears in machine learning, it appears in quantum computation information. There’s one fourth thing that came to my mind, and then it just disappeared, but I tried to catch it again. It’s very difficult to upload the data on a quantum computer. So if you give me, let’s say, I have a million images, a million photos, get these million photos, feed them to a quantum computer because maybe we discover an interesting algorithm, it’s very hard to do. It’s unclear how to do it, right? There is a chance that from the point of view of the time that it takes to run a computation, maybe you save some time.

And on the other side, the amount of time it takes to upload this data is enormous. By the way, one thing that crosses my mind that I would like to mention is this notion of time. These days, it’s about resources, about extraction, this idea of extraction. You are given a system, what you can get out of the system in terms of using it to speed up something. It’s almost a missed opportunity to just be focused, framed around this specific idea. Because maybe now quantum computers seem to be very interesting because they will allow us to optimize resources like time and memory. But maybe it could turn out that quantum computers are very interesting because of something else, for example, energy.

How much energy do we need in order to run the same computation that we run with another type of computer? And again, energy, as you know, is going to be a big, big deal in competition in the future. And we see that already. An enormous amount of energy is needed to run stuff. There is a chance that competition is not even going to be a bottleneck at some point. Maybe the bottleneck is the amount of energy you have.

Jeff Dance:
We more norms found in energy, right? It won’t be. Yeah. It’s not the bottleneck. With advanced quantum computing, perhaps it won’t be the bottleneck.

Simone Severini:
Right and then, maybe, maybe quantum computers will allow us to solve problems with the same amount of time that is needed to solve these problems without a quantum computer, but maybe they will save some energy. Because of this reason, they will be interesting from the commercial point of view. So I don’t know. I’m saying something, I won’t say that this is random or pseudo-random, okay? So energy, there is no clear evidence that this is going to be the case. But what I’m saying is, let’s try to be as open-minded as possible about what a quantum computer can do beyond saving time and memory.

Jeff Dance:
Yeah.

The “quantum apocalypse”

Jeff Dance:
That makes sense. What about, you know, with AI we have some great advancements. We also have more concerns around, you know, cybersecurity and cyber hacks and how people are, cyberattacks and how people are using the technology. You always have good and bad with technology. Technology kind of has a life of its own sometimes. When it comes to quantum computing, you know, we talked a little bit about some of the things that quantum computing can help with, but also just being a broader, deeper innovation platform for discovery. What are some of the concerns that we need to be thinking about at the same time and preparing for, given that things seem to be picking up some momentum? And obviously, maybe they’re in the hands of good stewards, countries, right? And companies that have ethics. But can you think of scenarios that we need to be intentional about and thinking about the negative impacts that quantum computing could have? Are there any?

Simone Severini:
Yeah. There is this algorithm that goes back to the mid-90s called Shor’s algorithm, which is a quantum algorithm, right? That needs to run on a quantum computer that allows to solve a specific computational problem, which is at the core of some protocols that are routinely used today to secure information. So once there will be a quantum computer of a certain size, meaning with a certain number of qubits, with certain properties of these qubits, a certain level of precision, then that quantum computer will be a threat because it will be able to decode that protocol and would render it useless. Whoever has that quantum computer will be able to…

Jeff Dance:
—crack the codes.

Simone Severini:
Exactly, read your secrets, learn your secrets. When this was discovered, it sparked great interest in quantum computers. Because again, if quantum computers were conceived as machines for doing physics, then the fact that a quantum computer can solve something that has nothing to do with physics, in fact, is a beautiful mathematical problem, a mathematical artifact, it’s almost an accident, but an accident that…

Jeff Dance:
Read your secrets. Yeah.

Simone Severini:
Fortunately or unfortunately fueled the attention that brought us where we are today in quantum computing. Great. So this to me is an interesting opportunity. An interesting opportunity, why? Because these cryptographic codes need to evolve. Now, for many, many years, we’ve been using the same things. Now, people start finally to come up with new interesting mathematical ideas to design new protocols that are conjecturally strong against quantum attacks. What do I mean “conjecturally strong”? I mean that we don’t have a formal proof that tells us, well, for sure, a quantum computer will never break this code. Or even a traditional computer will never break this code, right? We don’t know that.

Jeff Dance:
Absolutely.

Simone Severini:
We believe, because people spend a lot of time thinking about it, that these new protocols are safe against quantum attacks. There’s a chance that this timeline stretches, at some point, then this timeline becomes shorter, it’s unclear. But the titles sometimes that we see out there, the “Quantum Doom,” the “Quantum Apocalypse,” or “the world of crypto, as we know it is over,” right?

There are already these mathematical machineries that allow us to protect information against quantum attacks. So what people need to do out there is to listen to what NIST is suggesting, what these security organizations are proposing and the protocols they are suggesting, and start today because of the inertia that it takes to implement new crypto protocols in a large enterprise. Start today by auditing where the vulnerabilities are, assessing how large the blast radius is, and start replacing protocols. Follow the instructions, the suggestions that security organizations are proposing. There is this concept of defense in depth. Now you use a certain protocol, put another protocol in front, for example, which is known to be quantum-resistant.

But definitely, people should start moving, right? So audit where your vulnerabilities are. Interestingly enough, people often talk about quantum computers again as some kind of magic. A lot of security out there is based on the education of people that work in your organization.

Because if your password is 123456, nobody can save you. And it doesn’t matter whether you have a quantum computer or not. So audit vulnerabilities and educate your people to make sure that your security posture is the right one, independent of the computational power of your droppers.

Jeff Dance:
Yeah, that makes sense. That’s good to hear. I think that the notion that we should be preparing now, though, means that things are continuing to advance. We’re seeing a lot of momentum. If we were to say one last question on the future, like what advancements in quantum technology and quantum computing are you hoping to see in your lifetime?

Simone Severini:
I would like to talk about the impact. I don’t know how quantum computing will evolve. What we see now is that people are trying to build computers, obviously with more qubits, but with error correction, which is very interesting. There are many different ways people are trying to build quantum computers.

There is a chance that some of these machines will be very good at solving some very specific problems. There will not even be quantum computers that can solve all problems that a traditional computer can solve, but only a tiny subset of computational problems. Maybe again, a specialization of hardware. I believe this is a plausible future for this field. What I would like to see myself.

I would like to see a quantum computer that solves something that in science is valuable. Something that… I don’t want to say will justify the existence of quantum computers, not that we need a reason for quantum computers, because I believe that we need quantum computers. There is a lot of interesting things happening out there in technology, and there are some people who want to go to Mars, for example, which is a fantastic challenge. And I’m sure that in trying to go to Mars, we will learn a lot of interesting things, like the process, the journey itself. I mean, the journey to go to Mars, but the journey to build that rocket, the journey to understand how human beings need to behave in the amount of time it takes to go from here to there, the food that we need to eat, just to say some random stuff, right? So the immense amount of different things that we need to learn.

Jeff Dance:
Right.

Simone Severini:
The journey itself, yeah. New stuff that we need to discover, like when we were going to the moon, right. The Kennedy space program. Think about the amount of stuff we discovered while trying to go to the moon. This happens every time that there is a grand challenge, and the journey is what really matters. In quantum computing, it’s a similar thing, right. It’s not really zero one. In fact, sometimes people ask you, how many years before what? We are working.

We are building quantum. People are out there building quantum computers. A lot of very interesting human beings right now are working together to build quantum computers. And there’s not going to be a switch. Tomorrow morning at 6 AM, we’re going to switch the quantum computer XY37 random name. And I remember there was a science fiction short story on this, Who was it? Stanislaw Lem? Maybe I don’t remember. They say, “The ultimate question is, is there a God?” and they switch on the supercomputer and ask, “Is there a God?” and the computer says, “Yes, now there is. Anyhow, sorry about the parentheses. It’s not zero one. maybe there will be different phase transitions historically, the journey itself is very, very interesting because people will keep discovering.

Jeff Dance:
No.

Previously unseeable data

Simone Severini:
Interesting stuff, maybe in material science, maybe in architecture, maybe in programming languages, maybe in maths. But to go back to try, and apologies, to try to answer your question, what I would like to see? Well, I would like to see a quantum computer that is used by people everywhere on the surface of the planet. A PhD student that is in a country like Kenya, my father spent most of his life in Kenya, East Africa. He died there a year ago. I would like to see someone like that in Kenya, playing with a quantum computer to try to discover something new. And this something new is valuable. Not just to write a scientific paper because you need to finish your PhD, but because the information that the quantum computer will be able to present to us won’t be able to be extracted. In fact, “extract” is not a good term. Won’t be able to be seen unless we use the quantum computer.

Jeff Dance:
Thank you. So to summarize, sort of ubiquitous access to quantum computing where we are doing valuable things and we’re seeing the reward and the value from the journey that we’ve been on for a while now. But it’s not to undervalue everything we’re gonna discover along the way. We’re gonna discover a ton of things along the way of this journey, not only about computing, but about physics.

And so I think that helps as we’re thinking about companies getting in the space. Obviously, countries are in the space, but the notion of preparation and learning, I think, is one that is unique. I appreciate you sharing those things. Last question is, just what’s been most rewarding being in this industry so far for you?

We’ve seen a lot of advancements recently, I’ve been reading lots of articles, and I’m curious, you’ve been in the space much longer than most, yet we’re also at the beginning, right? So as far as your journey, what’s been most rewarding going along the way?

Simone Severini:
You see, to some extent, I don’t want to say I’m an outsider, right? Because I see what is happening in quantum. I spoke to virtually every person that is interested in quantum out there, right, in the last 10 years. But I am an outsider in the sense that I do not understand experimental physics. If you ask me, oh, what’s the difference between these two materials, I have no idea, right? So I need to trust people.

And to me, this human interaction, the human interaction has been really priceless. I’ve interacted with so many different people. People that are super experts in a special, you know, neutral atom. People want to build a quantum computer using neutral atoms. I spoke to mathematicians that maybe they couldn’t care less about quantum computers, whether they’re going to be built or not, but they are interested in some mathematical machinery that they think is beautiful. And they study that only on the basis that there is some beauty in it. So, short answer to your question is that human interaction has been incredible. I think that I am very privileged.

To see how people that end up talking to each other and have different expertise then have done interesting stuff that led to something in the physical world, in the real world. People building something, which could be a machine, could be a theory, could be an organization, and so on.

Jeff Dance:
I think that a really interesting aspect of quantum computing is the connection to the physical world. It’s clear that you’re a deep member of this elite community and that your network is unique. I’m sure Einstein would be interested to chat with you and talk about all the advancements that have happened since he came up with the theory of quantum physics.

Simone Severini:
Einstein was not very happy about quantum physics, right? In fact, he was complaining a lot about quantum physics, but history vindicated, history had its revenge in a very subtle way, in an ironic way because…

Einstein couldn’t stand the fact that in quantum physics there are some events that influence each other at a distance. He ended up writing the paper that opened up all these fields and now is quantum entanglement about physical correlation between objects where you do some operation on one of the two objects, and this operation has some influence on the other object even if it’s theoretically in another galaxy. Einstein couldn’t stand that and paradoxically ended up coming up with the fundamental theory behind this.

Jeff Dance:
I get it. I think one other key takeaway from our conversation is this notion of discovery. It’s clear to me that your mind being kind of open to discovery and the future of this being a platform for discovery is unique. It seems like just that explanation of how Einstein stumbled upon this, right? It was an element of discovery. Really appreciate the conversation, your depth of insight, and your honesty and authenticity as well.

Simone Severini:
Thank you.

Jeff Dance:
I’m grateful to have you on the show, and I’m sure the listeners will be intrigued by these perspectives because we’re reading a lot about quantum computing now, but the depth of where we’re at and where things are going is still abstract to most of us. So thanks for being here, Simone.

Simone Severini:
Thank you. Thank you.