Get Quantum Ready with Yonatan Cohen, CTO at Quantum Machines

But yeah, certainly the idea of having scientific simulations doing science better and having quantum computing for ai. These are huge use cases and I think quite beneficial to the world as well. 

Yonatan Cohen: And I think that we pretty much know that scientific computing is within reach hopefully in the near term, like in this next few years.

And that's very exciting. If we can discover how certain materials work how molecules interact with each other, et cetera, there's a lot that we can reveal first, to understand how these things work and then, do material design better save energy to do drug design. Those are the allowing fruits that we're hoping to get to.

Yeah. And then AI, as I said, is still we don't know yet, but we're hoping that this is going to be a case. 

Richie Cotton: It was very cool. We had an episode last year about doing like simulations for battery tech and the computational space is just absolutely crazy. And they were like, they were partnering with Nvidia just like running huge tracks of GPUs, but it sounds like quantum computing's gonna make that sort of thing a lot easier if you can.

Do the smarter quantum computations. So you mentioned some of the use cases maybe a few years away. It seems like quantum computing's been this sort of coming soon technology for a while. So talk me through what are the sort of expected timelines for doing useful things with quantum computing?

Yonatan Cohen: As I said, I think that we think that for, niche applications in the scientific computing area then we may even see applications in a couple of years, right? 2, 3, 4 years. We're hoping to see serious use cases, but this will still be very niche applications. And then. In order to see something bigger with, we need to build much larger scale quantum computers and also quantum computers, where we deal with the biggest problem of quantum computers today, which is the noise or the error rate.

And for that we need to implement quantum error correction, and that I think would take us a little bit. Longer. So maybe, until we have large scale quantum computers with enough error correction so that we can solve large scale problems. I think it's more on the, seven to 10 years of horizon.

Richie Cotton: Okay. Seven to 10 years. That still seems like a fairly soon first, such a big technology. So you mentioned the idea of error correction though and trying to scale things. You just wanna talk me through what's the problem here? Why do quantum computers make errors and why do you need to correct them?

Yonatan Cohen: Yeah, so basically, we have errors even in classical computers, right? Every now and then you get a blue screen of death, right? And, your computer turns blue and you don't know why. And actually, for example, sometimes it happens because a cosmic ray hits your memory, the memory of your device, it literally flips a single bit of information, right?

And that drives the computer crazy. And it's just that with classical computers, this thing happens very rarely. And you can think of an analogy of, a classical bit is like a coin sitting on, on, on a table, right? So in order to flip the coin, you need to invest quite a bit of energy, right?

There's like an energy barrier to flip the coin, right? So it doesn't happen very often. Like you can't think about many processes that will just make the coin flip, right? But a quantum bit is more like a coin floating in space, right? So it can almost everything. It's analog.

Every, almost everything will move it around a little bit, will, and these will cause. Cause errors. But the nice thing is that we have a way to deal with it. We have to deal a way to find these errors and and correct them. That's called quantum error correction. And for that, what we need is we need to take several quantum bits, several qubits, several.

Of these, coins that are flowing in space. And then we need to and then we turn them into a single, what we call logical qubit. And that qubit is actually going to be protected from north. So it's going to actually be much more like that coin that sits on the on, on the table.

And we digitize using error correction. We digitize the quantum information, which is an amazing idea concept. That, that we have quantum Amer action that can really help us protect quantum information in a way that we can scale up quantum computers and build quantum computers, even though the qubits are very kind of fragile.

Richie Cotton: Okay. That seems like a very statistician approach. It's the individual bits of data are very noisy. Once you take the average, it's a bit more stable. So I guess that's the idea with the logical cobits is they're yeah. They're, less a bit more robust in terms of the statistic in 

Yonatan Cohen: Yeah.

And the problem with the problem is also, but the hard thing about it is it's you need, because how fragile the qubits are, you need to use quite few, actually many qubits to make a logical qubits. And then the overhead that you need to pay in terms of both the quantum resources.

And also classical resources because it's not enough to make, one logical qubit from a hundred physical qubits. You also need then to do this quantum correction process, which also involves getting some data from your quantum processor. And processing it quite heavily on a classical processor.

So there's this hybrid, quantum, classical thing going on here and overhead, both in terms of the quantum resources, just basically more qubits. And then the classical resources overhead is quite large. So it's gonna take us while to build those systems, but we're on the way there. 

Richie Cotton: Okay.

That's very cool. So I guess. What's the current state of the art and how does it compare to classical computers? What are we up to with this scaling process? 

Yonatan Cohen: So today we have quantum working quantum computers that are somewhere between, a hundred to a thousand physical qubits.

The leading quantum computers out there are somewhere between a hundred and a thousand physical qubits. And there are already things that those computers, you think about a hundred qubits, right? Like we have, billions of bits in a classical computer, right? But still, there are already things that the quantum computer can do that are very hard for a classical computer to.

Okay. And even in practical for classical computers, those things are not useful yet. But there, there are well-defined mathematical problems that we can do with those a hundred qubit devices. But a classical computer cannot do you in a supercomputer will take it, many years to do.

And we do quite quickly on it on. Small scale one computers. That's very cool by itself, right? Like you do something well-defined, solve a mathematical problem with this a hundred cubic device that you cannot do with the biggest supercomputer on Earth. It's, and that's where we are plus.

There is another very important milestone which happened several different groups in the last couple of years. In that error correction was demonstrated to be work, not just working conceptually, but also showed the right scaling so that the more we use, the more physical cubits we use the.

The lower and lower the error rates that we see on these logical cues. And that's very broad. So we see that the concept is working, the scaling is working. Now. We need to scale up the system because, if you have about a hundred physical qubits for logical qubits, and practically right now you have one logical qubit or something like that.

So you can't do much with that. So once we do a hundred or a thousand logical qubits with very low error rates, that's where we think we have like the first applications. 

Richie Cotton: Okay. So we're still we're a few orders of magnitude away from having something that's really useful at the moment.

But but it tell, it, it's not like a real dramatic like. Billions and billions of times bigger before we get something useful. 

Yonatan Cohen: Yeah. And the progress is fast today. The progress of a few groups is, has been incredible. I can tell you I'm much more optimistic than I was three or four years ago about the timeline.

We do still have very tough. Challenges, like engineering challenges to solve. But I think that this machine is is, can really be built and to work. And to be honest, I think that the bottleneck is starting to be more okay, we. It'll take some time, but we'll build this machine. And then the question, the bigger question is, we need more applications, we need more use cases, we need more algorithms.

It start, it starts to show that we need, we don't need to shift our focus, of course, 'cause we need to do the engineering. And it's very tough and it's gonna take some time, but. We need to also have a lot of focus on, okay, what do we do with this computer once we have it? And you know what? Like finding more algorithms, more applications, and also connecting them to real world use cases of real companies and so on.

Richie Cotton: Okay. This is fascinating. So I guess talk me through what the current progress is at the moment and like what. What the, sort of the research on the algorithms needs to be. So I think that first of all, I have to say I'm less of an 

expert 

Yonatan Cohen: on the algorithm side. But I think that's today we have at the end of the day, very few quantum sort of routines.

Like you think about a sub routine, like something that could be done fast, much faster on a classic, on a quantum computer, and we can use those. But then the question is, can we map. The real world problems that we have onto algorithms where these SubT routines could really give. A dramatic speed up for the quantum computer, right?

And so mapping those real world use cases, whether it's, in again, material design, whether it's in optimizing financial portfolios, optimization problems in general, can we map some real world optimization problems to quantum algorithms and subs and really get benefits from the machine, those in those in those use cases and applications.

Richie Cotton: Okay. That's interesting. There are so many optimization problems just around, like almost everything like any business problem, it's oh, we're trying to optimize for some like maximizing some metric, but certainly yeah. You mentioned financial portfolios, I guess logistics as well.

Yonatan Cohen: Yeah, exactly. But you see the hardest part is that even if we even forgetting about quantum computers for a second, solving some of these optimization problems. We, if you just take the best kind of general algorithm that can solve these problems, this gen like for example, this traveling salesman, right?

Yeah. We we it's the it's a np complete problem. We, it's we don't know how to solve it non exponentially. It's an exponentially hard problem, right? But then for. Real world problems. We never do that algorithm. We actually use heuristics that would solve these optimization problems.

And we don't understand, really subs. We have intuition why these heuristics work, but we don't, we cannot prove many of these heuristics. On the same manner, we cannot prove some of these quantum heuristic algorithms. Okay? So the only way to to know whether they will give us. A speed up or not is just to build a large scale quantum computer and to try those quantum machine learning algorithms, for example, on these, on, for these problems.

And to see whether it gives us any advantage over the classical heuristics or not. Because again, we don't, we cannot just do a mathematical analytical proof and just proof, Hey, this is going to work much better than this. So for many of these optimization problems and AI and so on, it's just.

Something that we would have to build the machine and try 

Richie Cotton: does sound like a bit of a chicken and egg problem. You need to have the machines in place so you can test the algorithms, but you also need the algorithms to make sure you've got use cases for the machine as well. So there's lots of work to be done.

Yonatan Cohen: Yeah. I could give you another like interesting kind of thing that is happening today. Someone has a quantum computer with a hundred qubits as I mentioned, and they run something, they say, Hey. This is quantum advantage. We ran this thing and sometimes it's even connected to a real world problem and they say alright, that, that's great.

Like I showed that quantum computers can do way better than a supercomputer here. But then someone takes, looks at this algorithm and they go, okay, wait. Maybe I can do it way better with a classical computer. I'll just do the algorithm this way or that way and then takes them like few weeks and then they optimize it.

And then they optimize it for the whatever hardware they run, like the supercomputer or they take an FPGA and program it and then you see, oh, actually, so you can do it classically and it's just as fast or faster or whatever. So it's this also a game where you come up with a quantum algorithm, you try it on a quantum computer, then someone comes with a classical algorithm.

And unfortunately in many of these things, I don't think it's going to be just, things that you can prove. You just have to try a new algorithm. See they work, they give us an advantage. We use a quantum computer. Maybe someone can find a classical algorithm that runs with a classical computer and it's it's gonna be a race like that.

Richie Cotton: Okay. I guess the competition is good. As the quantum tech gets better, it means that all the sort of classic computationalists have then got a challenge to, to keep up. What do you need to do in order to like, get started with this stuff? So suppose you're like trying to prepare your business for, okay, quantum computers coming soon.

What do you do first? 

Yonatan Cohen: It's a great question. Depending on the level of depth that you wanna understand the technology. Okay. At the lower layers or the lower levels of the stack quantum computers are very different. They're programmed completely different. The algorithms are different.

The basic operations are different. The programming languages the algorithm, the way that the way we express things is just different. And if you wanna get to that level, you just need to really. Learn the language of quantum computing. You need to learn a little bit of the language of quantum physics, to be honest.

Like the world behaves in a different manner, so our intuition is slightly different. And and so you have to do that. On the other hand, I think, I believe that a lot of the, end users at the end of the day may be masked from this. We will have, the experts would build the applications in a way that users can use them.

For their applications, and program directly in the application language. Especially for the first use cases, I think that's gonna be the case. So given that I think that many of many of the, for example, companies that think that Quantum could be relevant to them, I think that maybe, they need to be less focused on understanding all of these lower level details and maybe more focused on what problems do I have that.

Perhaps a quantum computer could solve faster. And that's, again, as I said, not a trivial question, but if they find those use cases, then they need to really care about those use cases. 'cause that's, there might be just a huge difference in the economics of using quantum computers to solve these problems.

And so if you have large scale computational problems, you really need to. Be focused on, do I have problems that a quantum computer is going to be transformational or disruptive when it comes in and work with the researchers, frankly, to maybe, and that and that goes back to what I said about the community needing to also put more effort on, on, on looking for these use cases.

So I think the end users could also help in that respect. 

Richie Cotton: Okay. So that's interesting that if you. I guess on a corporate level, if you're going to like really push for quantum computing, you need to think about what are the use cases, and that's gonna be what, where am I spending vast amounts on computation and I guess probably on optimization problems in order to make it worthwhile.

Is that, does that sound about right? 

Yonatan Cohen: Yeah, exactly. And I think it's a very healthy thing because, we need the end users to come and say, here is my hardest computational problems. Can a quantum computer make a dent here? Or not. So the, because that will give us a lot of focus on, on, first it will give the algorithm, the quantum algorithm guys and the quantum application guys focus on what, where do they need to look?

But it'll go all the way through the stack, like even. For we do something much more lower level in the stack. We're enabling the people who build the quantum computers. But even that, there's certain optimizations that we could do once we understand where the work where the applications or is this the first applications are going to, how, what are they going to look like?

Richie Cotton: Okay. So it just sound like there's a big need for trying to figure out what the big applications are gonna be. So it sounds like you as like a quantum computing builder, like you're quite receptive what should the applications be? Give us your hard problems and we'll try and solve them.

Yonatan Cohen: Yes. Yes. Yeah. 

Richie Cotton: Okay. All right. And you mentioned that your company works slow down in the tech stack. I'm really not entirely sure what the tech stack consists of. Can you talk me through what does the quantum computer even consists of? 

Yonatan Cohen: So quantum computer, first of all, consists of quantum hardware.

We call it the quantum processor. This is where we have the qubits. That's where the magic happens, the superpositions and entanglement, and that's where the magic computation happens. But there, there is another very important system which is called the control system. That's actually what we do at Qual machines.

And that's a classical system. It's a classical hardware, which we call it drive. The quantum processor basically sends the signals. That make the qubits do the basic operations that qubits can do. If you know the, if you want the gates of the quantum computer the basic operations, mathematical operations, for example, flipping a qubit.

So you wanna flip abit from zero to one. Or you wanna put it in a combination of zero and one, you need to send a little pulse to the qubit and that pulse electro related pulse, for example, in some implementations need to come from this classical control system. So that's the second part, and that basically constitutes the hardware of the quantum computer.

And then you have several. Software layers. So you have the lowest level API, which is basically the interface of the controller. But then you have, the compiler and and quantum computing language programming language where you basically program this gate operation.

You say, I wanna flip the qubit, I wanna flip this qubit. I wanna do this operation on these two qubits, et cetera. And that needs to. Get compiled to the instruction set of the quantum controller that then drives the quantum processor and at the end gets the results. And returns 'em to the user.

Richie Cotton: Okay. Wow. Lots of ways of tech that I guess I should've expected. You start to with some very low level, we flipping s and then it goes up to the, I wanna be writing some code to, to control this thing. Or actually, so you mentioned there's a quantum like a programming language.

What's the programming language? So there are several programming languages, and again, when we talk about programming languages, there's like several layers, but typically people talk about what we call gate level programming language. This is this level that what I mentioned, like what you talk in terms of what operations do I do to cubit.

Yonatan Cohen: So I do this to this cubit and that qubit, I wanna do this operation. This qubits is very low level. When you think about in the classical computer, you don't, you never actually talk in this level. So there are many companies just. Wanted to clear that, there are many companies actually above that line that takes it from this level to a much higher level where, you program like similar, more similar to how people programming classical computing.

But still today, when people talk in the community about languages, they talk about this gate level languages and there are several languages like. By IBM and Code AQ from Nvidia and search from Google and and many others. There is still not a single programming language that is controlling the field.

And I don't, I'm not sure there will be, but there would probably be several other words. So I'll take the lead. 

Richie Cotton: Okay. Alright. So yeah, sounds lots of opportunities to learn different things. Those all sound like quite low level, is like a Python interface. Can you just be like, okay I'm running this with the language journal.

Yonatan Cohen: Yeah. I should say most of them are embedded in some other programming languages. So for example, Kiki is. Essentially it's embedding Python. So you work in a Python environment and then there's a library in Python with a packaging Python. You in install and then you connect to a quantum computer.

And then using this Python package, you you you create these quantum programs. Then you send to the quantum backend, right? In similar manner, CUDA Q is in, in Cuda, in the Cuda platform and so on. So in that respect, it's very similar to, typical software development environments.

It's just that those basic operations, that the language itself the ax, the symbol of the language is, these. Qubits and operations on qubits, and you need to build your quantum program, your quantum algorithm from this new basic operations and mount on the device. 

Richie Cotton: Yeah.

So you got a very like different math at the sort of low level. 

Yonatan Cohen: Yeah. Other than that it's the same. It's this low, and that's why I'm saying like at the end of the day, I think people will wrap those, especially 'cause there are, we at the end we don't have that many quantum routines that are going to give us split ups.

They might be taking different ships and different applications, but I think they will wrap those in, certain things that are going to be much higher level. And you will just use them as a user. You may not even know your programming on a quantum computer or a classical computer. You just say, okay I, I wanna try to run this on a quantum computer and see if it runs faster or something like that.

In in, in the future. 

Richie Cotton: Okay. That all sounds this is potential for it being quite convenient. Eventually. I'm curious if I wanna run my first quantum computation, how do I go about doing that? 

Yonatan Cohen: So today you can, there's actually a few cloud platforms. You can use, you can go to Amazon bracket for example.

This is where Amazon's is, is has their quantum cloud service, and they actually give you access to different machines, different backends from several different quantum computing vendors from the industry. And you can log you can basically, you can, you can connect to a quantum backend and then, again, they give you a language that you can write.

The quantum algorithms, it's all in Python, as I mentioned and you write it, you write your quantum circuit or quantum program and you run it and you get the results and you can play around with that, today. Tonight. 

Richie Cotton: Okay. Yeah, that sounds very straightforward. I love that. And in terms of getting stuff like is what's the sort of quantum equivalent of Heller world?

What's a good simple first thing to try? So the first thing to try is, yeah, just take a, even a single qubit and do some operations on it and measure it because, we have this thing in quantum mechanics where, you know, this cubit, it's it's like a bit in the sense that it has.

Yonatan Cohen: It can be zero or one, but it's not zero or one. It's always in some kind of a combination. We call it super position. And you can start with a qubit in zero, completely in zero, and then you do some operation to it and then it's in, half, zero, half one, and then you can try to measure it and first see that.

Half of the times when you do this, you get zero and half of the times you get one. It's a way to, truly create a random number generator, for example, that you truly, you may do this operation in your qubit. It's now half and half, and whenever you, and if you repeat this experiment a thousand times.

About half of the times you'll see it in zero, about half of the time you see it in one. So that's the very basic behavior of a quantum picked, right? And then you play with two qubits and see how you can make them talk to each other. And what does that mean on the fact that you measure one? Do you, does that mean you.

Do you know that the state of the other one, do you not know the state of the other one? Does it matter what operations you did before to your qubit? So that's just to play a lot. I think, to be honest, I think that quantum physics is not so complicated, but it was never taught using this basic.

Quantum bits and quantum operations. It was always taught in terms of, the structure of the atom and and all these like complicated things where you need to know complicated math. Here we have very isolated quantum bits, and if you just play around with some basic operations, you actually get a pretty good understanding of how these qubits behave pretty quickly.

And that's pretty cool to just learn quantum mechanics this way, right? And get some intuition of how these things work. 

Richie Cotton: Absolutely. Yeah, as well. I remember doing little bits of study this back in university sort of 25 years ago. I was like, yeah, it was actually, it was like all complex linear algebra.

It was very it was quite tricky math. So I like the idea that you can just play around, write a bit of code, you can just see the results of what's going on with with the computations at, get a feel for it. You mentioned important sort of thing. Quantum physics principles that you think are important to know if you want to try some of these like playing around with these computations.

Yonatan Cohen: At the end, it's, there are two quantum phenomena which are at the heart of, quantum mechanics and then quantum computing. And they're at the heart of, the difference between quantum and classical, and that's superposition and entanglement. So superposition is this phenomena that basically says that, Hey, we have a system.

It has states. It can be, it could be zero one, or if we have four states, it could be 0, 1, 2, 3, 4 and in classical physics, it's, you just need to choose one of them at every point in time, and you move between one and the other, right? So a car, it moves. With time on the road, right? And, but every point in time it has a single position.

Out of all of the possible positions, same way, bit will be either zero or one. It can move from zero to one in time. In quantum mechanics, we say the system can be basically at least mathematically in all of the states. At the same time, right? With different sort of weights. So you can be a little bit, the car can be a little bit here and a little bit here and a little bit there, right?

And this is the super position, right? The other phenomena is entanglement. That's when we say, when we start combining system, if we take, two systems, two bits, for example, each one can be zero one, and now there are four states to the combined system, right? Can be 0, 0, 0, 1, 1, 0, and one, one.

So we have four states and those, and now the entire system, the combined system can be again, in a superposition of all of its states. So a superposition of 0, 0, 0, 1, 1, 0 1, 1. And the number of states that such a system will have is exponentially with the number of of systems, right? So if I have, only a hundred bits, a hundred qubits I already have two to the hundred states that I am I can be in all of them at the same time with different ways.

So I have two to the a hundred. Ways, which means that nature and hundred bits is nothing. I can. I can have a system with a million bits of two to the million that's like way bigger than the number of atoms in the universe. So somehow nature sits there with a list of numbers, which is larger.

The length of the list is larger than the number of atoms in the universe. So God has all of these numbers that he needs to keep track of and they're changing with time. And that happens because, we have. So many degrees of freedom. And it happens because this combined system has, the combined states, and you can be in all of the different states.

And that also means that you cannot any, anymore separate the system to its sub-components. There are situations where you cannot tell me that this part of the system, like this qubit is in zero or one, or even a combination of zero or one. Without telling me what the other one is. It's, they're only together in this superposition of the their own, like the total combination of states, right?

And that's called entanglement. And we really need to be able to create this entanglement by having these cubits interact with each other in order to explore the entire space of this enormous number of states that, that we have. That together with superposition is the, are the two phenomena that we use in quantum computing.

So to complete this, at the end of the day, the only advantage that we get from a quantum computer is when we can take advantage of of what we call interference. And that's the fact that this superpositions the, this waste that we talked about for these superpositions. Throughout the computation, they can either combine because the system goes in kind of two different paths or exponential number of paths, and those paths have those weights that can either combine and give us a higher probability.

For the right solution, hopefully. And then lower probability for the for the wrong solution. So maybe I should say there are three phenomena, superposition, entanglement, and then interference. Interference is the way that we eventually get the quantum speed up because we use the fact that those quantum amplitudes, these weights that I talked about interfere like waves in the ocean and the other amplify one another or.

Or cancel one, one another to. Get some advantage from the fact that the computer goes in this crazy parallel universes and connect with faster. 

Richie Cotton: Okay? You make it sound very straightforward, just three concepts to understand and then yeah you've got computational magic. A, so the superposition idea.

This is like a Schrodinger's cat analogy, right? Where you've got a cat in a bag and and until you open the bag, if the cat can simultaneously be alive or dead, and once you open the bag, it's like it resolves one seat or another. I'm not sure if there's an equivalent analogy for the for entanglement and for the interference, but yeah it sounds once you get the hang of that you got the pro, I guess you're moving into a probabilistic world from rather than just trus and falses everywhere.

And that seems you've got most of the concepts. In order to take advantage of this then you've got a few quantum physics skills. You've got a bit of programming skills, using the, these cloud services. Is there anything else you need to know to take advantage of of quantum computing at the moment?

Yonatan Cohen: No. I think today that's pretty much it. Again today it's mostly about learning those things, understanding how they behave. Starting to identify the use cases where quantum is going to be important from this knowledge of how they operate. Yeah. 

Richie Cotton: Okay. Alright. It seems like for a, from an individual point of view, it's fairly straightforward to get started.

We started chatting briefly before about what organizations need to do, what businesses need to do. I do you have a sense of are there any organizations that are getting started with quantum computing, trying to take advantage of it and what are they doing? 

Yonatan Cohen: I think many organizations are starting to gear up towards quantum computers.

So of course there are the companies who are building quantum computers like Google and Amazon, and. IBM and they're also exploring, of course, use cases, potential use cases, both for themselves and for others. Then there are the, like that sort of like pure end users. So you know, banks like HSBC and Goldman Sachs and and companies like Volkswagen or or Lofi Martin have all done research on various use cases and how Quantum can help solve some of their.

Heavy computational problems and they work typically with the quantum computing vendors together to, again, as I said, and with the quantum algorithm guys. So there, there's a lot of that going on, and I think it's really the pace of companies that are doing. More and more in this space, especially on the sort of research level that they really look into what can we do with this thing that is relevant to us.

I think that is growing and I hope that it will grow further because we really need as much of that as possible. We need as much of that as possible. Also, because, we want, we don't wanna wait until we have, millions and millions of qubits. So the more people work on it besides finding new use cases, many times they find.

That for this use case that we thought that we need 20 million qubits, all of a sudden now, oh, we found an algorithm. They grew it with half a million qubits. And that's critically important to where we are in the, progress with the hardware and software, the development of the machine.

Then there is other types of organizations were even putting more resources on them. That's, of course, governments. So the DOE labs, for example, in the US are putting a lot of emphasis and this is in, we're seeing an increasing interest in quantum computers, how they can solve for certain use cases and how these computers can also be.

Integrated into their data centers and their envi their software environments, how can they be programmed in a way that also works together with, for example, how you would program a supercomputer, because at the end it's supercomputer quantum computer. We have to work together to solve these, some of these difficult computational problems.

So the quantum just solve. Part of the problem, not the entire problem. And so there is a lot of interest from these supercomputing centers, national labs, et cetera. And I believe actually these will be the early adopters, the first adopters of quantum purists. 

Richie Cotton: Okay. Yeah. So it seems like finance is I think finance is usually the cutting edge the cutting edge of technology.

So finance use cases for quantum computing seemed to be leading the way. And then scientific use cases were particularly the big, supercomputer organizations. Okay. Alright. And before you mentioned that progress has been very rapid in the last three or four years. Can you talk me through what the big breakthroughs have been and what might be coming soon?

Yonatan Cohen: Yeah, I think there are two very important elements that are making very fast progress these days. One of them is Quantum error Correction. As I mentioned, I think that, maybe up until two or three years ago, there were demonstrations, but we didn't see the full scaling and how it works like.

Really well. And now I think this has changed. It's clear that quantum meric correction is, we're at the point that the performance of the individual elements is good enough so that we can really see how it scales, how we, how you could build a machine if you just scale it, it's not that simple.

Of course it's but you can see how we, you scale it. The error will go down, you'll be more logical. Qubit and so the work that we've seen from Google in this respect, I think was the, perhaps the most important and interesting, but also works from continuum and career. And that's another the other big thing that's happening is the.

In quantum competing we still have, we don't have the silicone transistor yet, so we have different types of qubits that are still competing. What would be the sort of silicone transistor? So we have super qubits, we have trapped ions, we have neutral atos and spin qubits, and.

Neutral atoms based quantum computing, I think has been steadily moving it at a very fast pace, especially towards scaling up the systems to at least tens of thousands of qubits in the next few years. And it's, so we're like the, this kid on the block that is making everyone like. Also work harder because the other platforms need to match.

Cubits, I think is, was, and maybe is still leading in many respects, but neutral atoms are just like moving very fast. So everybody has to work harder and and I think that's the second thing that's happening, which is which is really shaping how the field is going to look like.

Richie Cotton: Okay. So error correction and scaling with the neutral Latin qubits. Okay. That's cool. And. What are you most excited about with the world of quantum comput? What's what's yeah, what's excite you? 

Yonatan Cohen: There are two things. One is just on a personal level, I think that quantum com, like I'm, I'm a physicist, so the thing that drives us crazy as physicists is this, this outstanding problem in physics of, how you unify quantum mechanics and general relativity. And I personally think that quantum computers may help us solve, or at least understand if we're going in the right direction in solving this issue, which is in my mind at least the biggest issue in physics today. It's like unifying our basic understanding of the universe.

These two theories are very fundamental to how we understand reality. On a philosophical level and, and we have to find a way to unify them and a quantum computer can help us. So I think that's on a personal level. And on the more like how do we make an impact on the world kind of level, I think that I'm excited to understand how quantum computers may be impactful for ai.

Again, it's an outstanding problem. Something I think we can prove and there are lots of ways to dismiss it. There are also lots of ways to wait, why to believe that it might be helpful for ai and I'm just excited to, for source V, whether it's going to accelerate AI and second, understand why.

'cause again, I think it's a very deep kind of question. So 

Richie Cotton: absolutely. The the idea of combining the quantum physics and general relativity there's a very longstanding problem in physics. It's a very different problem to. I'm trying to optimize my financial portfolio or optimize my logistics.

But yeah I love it. It's a fundamental thing about understanding the world. I like that idea. 

Yonatan Cohen: Yeah. And, sometimes these, optimize like an optimization problem. Like fine, maybe, we're using it in order to optimize our financial portfolio, but there's something deep about, computational problems and what, whether something is computable and how fast you can compute something given certain rules on the physical system that computes, right? So there is something very fundamental about those things on the scientific level that I think is interesting and perhaps we discover some of these more fundamental philosophical questions because.

We found a way to optimize the financial portfolio, so there's some connection. 

Richie Cotton: Excellent. And 'cause you mentioned it is a very un clear thing whether quantum computing is gonna have an effect on ai. Do we have any sense of what the impacts are gonna be? 

Yonatan Cohen: No, I think we don't. And I'm not the person who would say it'll have a huge impact.

I I frankly don't know there were very, before the AI. Explosion. So basically up until 2012 there were the, Neur networks was a very small community of people who believed that this is this is going to be, crazy impactful. But it was mainly a niche, right?

It was pretty much a niche, and it was not so much mainstream to think that this is going to work so well and then. Someone made it work for a specific problem 2012 and it was, and since then it exploded and. The reason again, it is just that it worked specifically on the GPU and and it just worked right and before it.

So it's not like the, our understanding, the day before and the day after change we had the same algorithms. We had the same kind of the under the basic understanding of what it, what does Neur networks do and why we maybe have an intuition that they will work was the same. Pretty much, we understand maybe a bit better today, but we cannot prove exactly why this works, right?

So the key element was to try those things on a real machine and see whether it works or not. And I, I hope that with quantum computers it'll be similar, but there is no reason to necessarily believe report or it wouldn't. 

Richie Cotton: Okay, so maybe new algorithms coming, maybe some, form of better intelligence that just magically works well not magically, but works because we have access to a different computational approach.

Okay. It's exciting times we'll see what happens. Just to wrap up I always want new people to follow who's work are you most excited about at the moment? 

Yonatan Cohen: A couple of companies that one, one I mentioned neutral atoms. There is, there are several players in neutral atoms.

Qra is perhaps the leading kind of player now in neutral atoms. And we're seeing, really great progress by qra. And I'm very excited to see, and I'm following their progress and seeing how. How fast they're moving, which is really fascinating to see. And I think this, as I said it's shaping the field.

So neutral atoms in general, but Kira is, the leading player there I believe today. And then and then I think that in in trapped ions we have a new player here in Israel actually quantum art. And so the always the problem with draft ions is that they're actually working incredibly well.

Like the error rate is very low. But scaling up is hard. Continuum and Ion Q are doing great work. But in many cases it has to do with moving ions, like individual ions around. It's very difficult to do. And quantum art, which is this new company here in Israel, they have a different way of doing the scale up.

And I think that's a very interesting way, maybe trapped ions all of a sudden becomes. Easily scalable and that's going to change the picture. And then of course Google and Amazon and IBM, they're, these are the sort of biggest companies that have invested tons of resources on Quantum and are making, huge progress with superconducting qubits.

And that's still, I believe, the. Perhaps the leading payer still today and they're doing now a lot of work on error correction. And I wanna see how fast we're making progress in building those logical qubits and scanning those systems up. 'cause tho those systems are also the fastest system.

If we can do. Scale up of these systems and do the error correction where Google demonstrated already on a single logical qubit, but now scale the systems up, which is difficult. Engineering problem. Then those systems have a huge adventures 'cause they're just very fast.

So these are there. The different players. 

Richie Cotton: Okay. I do love that there are lots of different competing technologies. The competition is rif. I think that's gonna Ben benefit everyone else with all these different ideas being thrown around. So yeah, exciting times and lots of interesting things to, to watch out for.

Alright, brilliant. Thank you so much for your time. Your. 

Yonatan Cohen: Thank you so much. It's been great.