#329 Izhar Medalsy: How AI Solves Quantum Computing's Biggest Problem

Quantum computing has been "5 years away" for decades. So what's actually holding it back? In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself. Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error correction. They dive into how his team improved Shor's Algorithm from 80% to 99% accuracy on IBM hardware, without changing the hardware itself, and what that means for the future of quantum performance. The conversation also explores how AI is being used to optimise quantum systems, why classical computing will continue to play a central role in quantum development, and what milestones to watch as the industry moves closer to real-world applications. If you want to understand where quantum computing actually stands today and what will unlock its next phase, this episode gives you a clear, grounded perspective. Subscribe for more conversations with the people building the future of AI and emerging technology. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI https://x.com/EyeOn_AI(00:00) The 99% Accuracy Breakthrough (Quantum's Turning Point) (01:03) Why Quantum Hardware Alone Isn't Enough (03:50) Digital Twins Explained (The Missing Layer) (08:09) The Real Problem: Noise, Instability & Environment (15:43) From 80% to 99% on Shor's Algorithm (26:36) How AI Is Fixing Quantum's Biggest Bottleneck (33:53) Inside the Platform: From Circuit to Optimization (40:51) Logical Qubits & Scaling Quantum Systems (43:34) The Limits of Simulation vs Real Quantum Hardware (54:29) When Quantum Becomes Useful (Real Timeline)

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  The idea is that if classical devices could mimic quantum devices, then we would need quantum devices. So going back to the digital twin, those qubits, you're right, they're notoriously noisy, they're very unstable. That's the beauty of it because that's kind of the core of what quantum mechanics is.

• 0:18Craig S. Smith?

  That you can get to 80% accuracy with Shores on factoring the number 21. Is that right?

• 0:25Speaker 00

  We took this knowledge that we gained from our digital twin and implemented it on the IBM platform and got to 99% accuracy on Shores algorithm.

• 0:34Craig S. Smith?

  Can you start by introducing yourself and giving, give your background insofar as it's relevant, where you studied, what you studied and how you came to quantum elements? And then we'll talk about quantum elements, use of large scale digital twins and AI to make noisy, real world quantum hardware useful.

• 1:03Speaker 00

  Absolutely. Thanks for having me, Craig. I studied physics and chemistry back in Israel and focusing on nanoscale computational devices using quantum dots and other components. And these were kind of the early days of, you know, those kind of nanoscale computational devices. I did my postdoc in Switzerland at ETH Zurich, kind of diving deeper into the field. Started my career in the industry as a research scientist and very quickly switched to business dev and product

• 1:47Speaker 00

  and took the product that actually developed as a scientist to the market. And that was quite a thrilling, an interesting adventure. And switched to a couple of other companies and built my first startup for a few years.

• 2:05Speaker 00

  A very interesting journey, but maybe for a different podcast. And a few years back, alongside my two amazing co-founders, Professor Daniel Lidar from USC, who is really one of the world leaders in quantum error correction and quantum algorithms, and Professor Amelia Kobi from Harvard, who is a leading experimental scientist in quantum. We realized that in order to deal with those very unstable systems, meaning those quantum devices, you need to be able to use the ability of to augment those systems using classical devices,

• 2:48Speaker 00

  meaning creating a digital twins of those systems and best in breed classical tools that are used today for software development in order to control them, accelerate their development and ultimately enable end-user applications on quantum.

• 3:06Craig S. Smith?

  Yeah, and when we spoke before, I was asking whether this is, when you say using classical hardware, whether this is using classical computers to run quantum algorithms, which there is a lot of work in that field. But from my understanding, you're building digital twins of quantum hardware and then using AI to optimize that hardware. And you have some pretty interesting results. Is that right? And if so, can you explain that?

• 3:50Speaker 00

  That's absolutely right. The idea is that, you know, if classical devices could mimic quantum devices, then we wouldn't need quantum devices. On the other hand, in order to allow AI and other software components to accelerate the development of quantum, you need to give those developers a platform that mimics those quantum systems, right? As we all know, quantum systems are not an extension of classical devices. They are completely different species and completely different technology.

• 4:31Speaker 00

  So what we've done is we really went back to first principles and solve those equations that describe those quantum systems. We solve those equations on large classical computers, you know, supercomputers. So now you have a very realistic representation of any given quantum hardware. And it allows you to experiment and solve and develop different layers of the software stack that are specific to the hardware that you're interested in. Meaning if you are sitting in a lab developing your own system, you can use this digital representation

• 5:14Speaker 00

  to solve problems on how the system works and how to optimize it, et cetera. On the other hand, if you're sitting in a company that would like to develop an application, which hardware will you choose? It's very hard, you know, they're very expensive, they're very scarce. So if we're giving you a realistic representation of the system, you now have the freedom to experiment, develop, and augment different system and understand and how to work on them and ultimately execute your application with certainty and confidence.

• 5:47Craig S. Smith?

  And when you say build a digital twin, can you describe that as, is this pure math? Or is it something more tangible?

• 6:01Speaker 00

  So there are two components to building a digital twin. There is the physics part and there is the algorithmic part. The first thing is looking at a system in front of you and being able to describe the math that is behind its working principles. Specifically in quantum systems, you need to be able to describe what is the modality in front of you? Are you using superconductors or maybe you're using ions, right? There are many different ways today of realizing quantum devices, just to put it in perspective. There are about 70 different quantum hardware companies out there. Each one of them with their own kind of unique flavor.

• 6:43Speaker 00

  So the first thing is to look at the system that you're interested in creating this digital twin for. And saying, okay, what are the governing principles of those systems? What are the equations that if I solve them will give me a reliable prediction of how this system behaves? And in the same way that you look at any physical system, think about the spring, right? In order to understand how a spring works, you need to be able to solve the spring equation of this specific spring that you're interested in. So the same principle. Now, the other thing is, okay, you're now able to solve those equations, but they're very expensive computationally.

• 7:27Speaker 00

  So how do you do it efficiently in a way that you can enlarge them? So, once again, to put it in perspective, there are some tools out there, scientific tools, that can solve one or two qubits of those digital twins. But what can you do when you're able now to solve 50 or 100 of those qubits? You can now tap into every problem that the industry is tackling and provide the right tools because you solve the system at scale to a point where it's now very meaningful. So these are those two components, the physical representation, the ability to scale it reliably.

• 8:09Craig S. Smith?

  And when you talk about solving for individual qubits, I mean, quantum is notoriously hard to understand. Qubits are hard to understand.

• 8:26Craig S. Smith?

  Are you, are you talking about, does the digital twin simulate individual qubits? Or are you talking about optimizing all of the things around, you know, for sampling or for temperature control or for all the other, you know, isolating or eliminating noise, all the other things happening around the qubit? Or are you actually doing something with the qubit?

• 9:02Speaker 00

  That's a fantastic question. The third piece that comes into play when you need to provide users with a digital twin is the ability to also take into the account the environment around this system, right? Yeah. What is the noise that is affecting this system? And how does it evolve over time as you are operating this machine? So let's take an analogy from a different world and then come back to quantum. Let's say you want to develop your next generation airplane. The kernel that you're using in order to understand how the air is flowing

• 9:45Speaker 00

  over the wing of this new generation of airplane that you want to design is solving flow dynamics, right? You want to understand how those particles are moving across the wing. So you start up, you start with a single particle, you kind of solve the equation. And now the secret sauce is how do you enlarge it to represent millions of those particles, you know, that are flowing over the the wing? And if you want to take it to the next level, what does, what happens to the flow where the airplane is stalling? Or maybe when there is some non-laminar flow or some issues with the weather, you know, kind of,

• 10:27Speaker 00

  you have some other phenomena that are environmental now that are affecting this airplane.

• 10:34Speaker 00

  So going back to the digital twin, those qubits, you're right, they're notoriously noisy. They're very unstable. That's the beauty of it, because that's kind of the core of what quantum mechanics is. It's a statistical system, but it's also very hard, because we need them to be quiet enough in order to do computation. So what we are doing at Quantum Elements, we're solving the equations that not only governs how those qubits are working, but also what are the environmental conditions that are prohibiting them from working correctly with low enough noise?

• 11:15Speaker 00

  And we give the user this kind of recipe or all of those components that are now jeopardizing the performance of their qubits. Now, they have at their disposal all of those different knobs that control the performance of their qubits. They can go ahead and develop the mitigation in order to make them work correctly. And that's exactly the word that the industry is using. Air mitigation, air correction, air suppression. These are all tools that are built on top of the engine that we are bringing. And because we're able to do it with such high fidelity and accuracy, and it's such

• 11:56Speaker 00

  a large scale, it now uses, the users are, benefits, benefit is that they can now do it at scale with very high certainty.

• 12:08Craig S. Smith?

  And you, this is tuned to each particular hardware? Or the modality, as you put it, of the, that the hardware is using, whether it's superconducting qubits or trapped ion qubits? So what data do you need from the hardware provider to build the digital twin? It seems to me it would be not only the hardware, but where the hardware is positioned, in what kind of a room, and what kind of a building, and all of that?

• 12:51Speaker 00

  This is exactly it. And it really speaks, your question really speaks to the crux of what we're doing.

• 13:00Speaker 00

  So there are two aspects here.

• 13:03Speaker 00

  As you go up in the software stack, if we take the classical analogy, if you're developing an app, you need kind of some sort of a translational layer to the hardware that is underneath it, right? It can be very, up in the stack, it can be very abstract. As you go deeper, you need to know more and more about the hardware to a point that if you're developing an operating system for, let's say, a GPU or a CPU, you really need to understand all the intricacies and architecture of your system. So same with quantum. When we're working with hardware manufacturers, we would usually have a very intimate relationship with them.

• 13:46Speaker 00

  We will work with them very closely to refine this model so it will truly represent everything that is going on with their system because it is being, it is used by their own developer in order to refine maybe the architecture or the design or how those qubits are working from an operational point of view. But when we're working with customers who are upper in the stack, that are maybe interested in application, the models can be more abstracted and in that regard, we don't need much from the hardware manufacturers.

• 14:26Speaker 00

  If we take another, you know, analogy outside of quantum, let's say you're developing a Formula One simulator. You have two paths as the guy who's designing the simulator. You can go to McLaren and sit with engineers and ask for all the design files and everything and they would say, okay, we need to sign an NDA and we need to have some relationship. But, you know, if you accomplish that, your model is now really true to life and probably the people using it will be the Formula One drivers of McLaren, right? But if you're, there is another path. You can borrow a McLaren, run a couple of laps on the track and kind of record the acceleration

• 15:10Speaker 00

  and the drift and everything and use that to have a more maybe abstract model of your car that will be used by novice drivers or maybe people are interested in developing a self-driving car, not necessarily the next generation of McLaren. So you can see. That you can tap into different users' personas and user, different users and participants in the quantum workforce, based on what their needs are and how accurate this twin needs to be.

• 15:42Craig S. Smith?

  So, yeah, so I mean, you answered this question, but maybe in more layman's language, how do you validate that your twin is a true representation of, say, IBM-heavy hacks device or something like that?

• 16:03Speaker 00

  So let me give you an example. I think one of the most famous algorithms out there is Shor's algorithm.

• 16:10Craig S. Smith?

  Yeah.

• 16:10Speaker 00

  It's an algorithm that allows you to factor a number, right? So what we've done is we went to the IBM platform as users and kind of looked at the published information that they have on their website, the performance of their system. We know what superconducting qubits are. We understand what the models are. So we wanted to see the relationship between how those systems are behaving versus how Shor's algorithm is behaving in terms of its accuracy, meaning when you're executing Shor's algorithm, how accurate is the result?

• 16:53Speaker 00

  So we went and executed Shor's algorithm on IBM system and got to about 80% accuracy.

• 17:02Craig S. Smith?

  Yeah.

• 17:03Speaker 00

  And we've suspected that there is something in the relationship between those qubits. And we know how to solve this relationship. It's called cross-talk, meaning when those qubits are influencing each other, right? In an ideal world, every operation that you do on one qubit should not influence the other. So what we've done is we've simulated thousands of times those IBM qubits with different cross talk combination, because if you had to do it on hardware, you will have to manufacture a new QPU with new cross talk, right? And then we realized that there is this sweet spot where we can inject a process called

• 17:48Speaker 00

  air suppression that can give us the best performance on Shor's algorithm for the specific IBM system. We took this knowledge that we gained from our digital twin and implemented it on the IBM platform and got to 99% accuracy on Shor's algorithm. So if the highest reported accuracy on Shor's in literature is about 70%, when we submitted the circuit, as is on IBM, we got 80%. When we implemented based on the knowledge we gained from our digital twin, the remedy for this cross talk, we got to 99% accuracy.

• 18:27Speaker 00

  So you see that the ability to close loop, to validate and to get the benefit of using this technology can be very quick. On the other hand, when we're working with a hardware manufacturer and we're working with one of the large public companies very intimately, we're literally sitting with their scientists and gauging how accurate is our simulation and digital twin against their experimental result and refining the model base.

• 19:01Craig S. Smith?

  Yeah, that's fascinating. How sensitive is that 99% to drift in the underlying hardware parameters? I mean, if IBM recalculates its chip tomorrow, do you have to relearn everything or are these optimizations applicable regardless?

• 19:30Speaker 00

  There are a few layers here. When you look at qubits, at performance, you're mainly looking at what is known as decoherence. How quickly are those qubits moving away from their optimal condition?

• 19:47Craig S. Smith?

  Yeah.

• 19:49Speaker 00

  These numbers are publishable, and they would usually change based on the calibration process that each of those manufacturers is doing, usually on a daily basis.

• 20:04Speaker 00

  There are other more intimate parameters that will govern the performance of those qubits, which we can extract just by using the system as any other user. So to your point, the model is the same model, but the noise and performance parameters have to be updated daily in order to keep track of how the system evolves day over day. And that's the benefit of using this kind of very intricate, physical understanding of the system, is that, once you solved it from first principle, the only thing left is to optimize the daily parameters that change due to environmental conditions.

• 20:47Speaker 00

  You know, things drift and temperatures. We need to remember those superconducting qubits are sitting in temperatures that are colder than space, right? So it requires just a milli Kelvin degree change where everything starts to drift and change. So that's how we do it.

• 21:05Craig S. Smith?

  Yeah, and so which noise channels and hardware parameters are you modeling? I mean, or is this all approximation?

• 21:17Speaker 00

  So these are models. These are physical models. They're known as master equations. They're really solving the quantum system from first principles, right? From describing it from the physics side of things. And there are a few.

• 21:39Speaker 00

  Noise models that are very important. The first one is known as T1, T2, and T*. T* these are looking more at the rate of noise accumulation or decoherence in those systems. We can do that. There is one over F, one over F noise. That's another one. There is cross talk. And cross talk is a very notorious one. Think about this game of whack-a-mole, right? You're dealing with one guy and you think you're done. You calibrated this qubit. But then you realize that in the process of calibrating this one,

• 22:22Speaker 00

  the other guy went out of calibration because they're influencing each other. So we know how to model, to model that. And it's a very important one because when you have a few qubits, then, okay, you tap this one and you kind of think about the knobs of, you know, this analog machines, you're, you're turning them and, and you kind of, you, you have the secret sauce, you're the expert. But when you have a hundred, it becomes a bit harder. When you have a thousand, we'll forget it. So it probably would lead us to the next topic, which is how is AI kind of tapping into?

• 22:53Craig S. Smith?

  Yeah, that's, that's right.

• 22:54Speaker 00

  And the last one is leakage. Leakage is a, is also a very interesting one because you have to remember, you know, we're going back to a bit to quantum physics. Most of those qubits are using what is known as a two level system. You kind of move between, let's say, let's call it a zero and a one. It's a bit more than that, but a zero and a one. But sometimes there is a three and there is a four. And, and you know, those things are unstable. So that's another thing we can model.

• 23:21Craig S. Smith?

  Yeah. And for listeners who are not quantum experts, and for me, who's not a quantum expert. So you showed that that you can get to 80% accuracy with Shores on, on factoring the number 21. Is that right?

• 23:51Speaker 00

  Yeah, so we can take it from 80 to 99%.

• 23:53Craig S. Smith?

  99, I'm sorry, to 99. Yeah, 80 was, was, was where the industry was at.

• 24:04Craig S. Smith?

  Can you, yeah, explain what factoring 21 means?

• 24:10Speaker 00

  So I know it sounds like a very small number, right? My nine-year-old can probably factor 21, but it's more of a way to demonstrate that

• 24:21Speaker 00

  or to illustrate the fact that quantum can do something very unique. It can solve those factorization problems that ultimately tap into encryption in a very efficient way computationally. So the reason we are all relying on RSA encryption is because if the bit string is big enough for classical devices, it will take the age of the universe to factor it.

• 24:54Speaker 00

  And by doing that, to break the encryption.

• 24:59Speaker 00

  Quantum computers are working in, you can think about it as a, as a parallel way. It's almost like we lay in front of you all of those possibilities and the system will converge to the accurate solution in one go. And the way we've, to demonstrate that is, you know, Shor's algorithm is kind of solving this problem in an effective way. And that's kind of gave the, the industry or this field the, the acceleration and the confidence that we can now use quantum computers to deal with real world problems.

• 25:43Speaker 00

  The problem being still, that takes us to the, the kind of, where the state of the industry today is that to develop those algorithms at scale, you need very unique capabilities and approaches.

• 25:59Speaker 00

  So we're still at very early stages, meaning those numbers are still small, but I'm pretty confident that as the hardware evolves and the number of qubits grow, we will start seeing

• 26:14Speaker 00

  Companies that and scientists that are solving shores or factoring shores on much larger numbers, getting us closer to the point where Quantum can really start tapping into the encryption industry and disrupting it in the way that we think it will.

• 26:36Craig S. Smith?

  Yeah, okay, then, and where does, how is AI applied? I understand the digital twin concept, but then how are you using AI? And are they standard models? Or are you building bespoke models? Are they generative models?

• 26:58Speaker 00

  So I want to talk about the here and now first, and then we can maybe think about the next.

• 27:05Speaker 00

  One of the things that AI needs, and I'm sure your audience can appreciate that, is large-scale curated data, labeled data that is actionable and can be used to solve a specific problem. One of the things that we can agree on after a 30-minute discussion is that Quantum is scarce, expensive, and exotic, meaning that each modality has its own intricacies. They're not easy to come by, and even if you go ahead and dedicate

• 27:44Speaker 00

  a machine to train a specific AI model, the next day you can wake up and this generation of machines is already a history and you need to repeat the process again. What you, the advantage that you have with the digital twin is that you're not bound by any physical hardware. You can not only create a representation of the current state, but also of future state.

• 28:14Speaker 00

  To a system scale that is big enough that allows you to generate massive amount of data to be able to address some of the issues that the industry is facing today. The most present and critical issue that we're dealing with is noise, right? And errors. So one of the things that we are doing, and we're gonna release it very soon with our partner, is the ability to do large-scale error correction using a digital twin. Now, if you want to develop, let's say you're a scientist in a company that is really interested

• 28:56Speaker 00

  in now dealing with errors on a large scale, and you want to develop a decoder that is solving those errors, not only for the current generation of your machines, but also for the future one, you need to be able to generate a lot of labeled data that will allow you to train this AI-based system and decoder in a robust way. So we're giving you the platform to do it very economically and in a very robust way. One of the things that I didn't mention about using those large-scale simulator versus hardware is

• 29:39Speaker 00

  people sometimes hear this notion of shots in a quantum computer. You need to run multiple shots. Sometimes it's 10,000, sometimes it's more. Why? Because when you run a quantum computational cycle, you get a snapshot into the statistical representation of the accurate answer. Meaning you need to run enough cycles in order for the answer ultimately to be statistically significant enough to represent the right answer. That's how kind of wild quantum is. When you're running our digital twins, one experiment gives you the full picture

• 30:23Speaker 00

  of all the statistical representation of your answer. It's called the density matrix. It gives you the whole universe of solutions. So when you have this power and at scale with the right architectural configuration of errors, you can now, as the end user, say, okay, you know what? I have a new idea. I think ML, AI, reinforcement learning, whatever. If I have enough labeled data that is representative of the system that I'm interested in, I can now build the engine that will allow me to solve those future problems. So think about us as this development platform that gives you the flexibility

• 31:07Speaker 00

  to generate and augment and use large-scale data for your AI needs. And that's one of the things that excites me the most.

• 31:17Craig S. Smith?

  And on the labeling, what exactly are you labeling? What's an example of the labels that you're applying? And how do you curate them?

• 31:31Speaker 00

  So think about, you know, if you know that you're, you need to do those, let's say, error correction cycles. In error correction, you're doing a process that is known as syndrome extraction. You want to understand what are the syndromes that are governing your errors and how to deal with them. If you, as the guy designing the system, can change the knobs of your hardware virtually, see how it taps into the output of the error, you now have a direct link between hardware vulnerability and error accumulation. So now you've connected the dots, right?

• 32:15Speaker 00

  So if your cost function is, I want to minimize the number of errors. And you're now curating the data in a sophisticated enough way that allows you to start connecting the dots between the governing principles of what generates the error and how the errors are being Are manifested on this large scale. You now have the ability to start minimizing the governing components that ultimately are jeopardizing the performance of your system. So because, you know, one of the things that we always say about the quantum system is the moment you ask the quantum system a question, it collapses, right?

• 32:59Speaker 00

  It kind of, the notorious thing about quantum is you cannot stop at a given point and say, okay, what is your state? If you're asking the quantum system, what is your state? You've, you've killed the process. The process will not continue. You cannot say, okay, what's your state? And then, okay, you can now continue. But you can do that with a simulator. So it gives this superpower to stop at every given point, understand the underlying mechanisms and continue.

• 33:28Speaker 00

  And this allows you to be very selective on the parameter space that you are using, how you're training, how you're labeling. You almost have the full recipe of what is going on. With the caveat that obviously you need to make sure that then you go and test it on hardware and et cetera. But at least you have the control knobs at your disposal.

• 33:53Craig S. Smith?

  Yeah.

• 33:55Craig S. Smith?

  And can you sort of walk us through the pipeline from a user uploading an algorithm through the simulation engine to the AI training on those runs? And proposing a remedy for the hardware and optimization for the hardware?

• 34:18Speaker 00

  So if you join our platform today, you will be asked a few questions. We will ask you.

• 34:30Speaker 00

  You know, what kind of experiment are you running? So let's, let's for an example, move away from error correction. And let's talk for a second about error suppression. Who describe what error suppression is in a minute. So what kind of experiment are you running? Are you connecting to a physical machine? Or do you want to create your own virtual machine? If you're connected to a physical machine, we can extract the system parameters for you and kind of feed them into our model. What kind of modality are you using? Let's focus for a moment on superconducting qubits. And what is your system size? Let's assume you want to use a grid of five by five. Now we will give you a canvas.

• 35:15Speaker 00

  You will have a canvas, a digital canvas with those qubits. You can now control the connectivity between those qubits. And you can literally go to each qubit and update the system parameters. The frequency, the T1, the T2, the leakage, the cross talk, all of those things that I talked about. And from that point on, this system will behave as a physical system. You can upload a circuit. So there is a tab, you press the tab, you upload your quantum circuit. And now you can run this simulation. And we allow you to inject pulses into your circuit to deal with the different noise sources that you have. So now think about the following process.

• 35:59Speaker 00

  We give you the tools to create your machine. We give you the canvas to run your circuit. And it's a very fine detail, but it's important. The circuit runs in the time evolution domain. It means, it means it behaves in the same way it does on hardware. I'm saying that because

• 36:23Speaker 00

  Most, all of the simulators out there work as discrete systems. They're not tied to a physical entity. So now you can run this circuit and you can inject different remedies. So now let's think about a model that you can develop. You can think about the iteration and the placement of those injection pulses. And now start running multiple experiments, you know, thousands of iterations where you're optimizing location, shapes and components. Where all of the time you're monitoring the fidelity of your circuit, the performance of the circuit.

• 37:04Speaker 00

  And when you're hitting the optimal point, you know now that you've kind of closed the loop on this cycle. From that point on, you can now start thinking about training a model that will find the next iteration when conditions changes to hit the same performance level and again and again and again. So it gives you the flexibility, it gives you the canvas to do all of this work. And obviously, there is a huge cost benefit here, right? When you're working on real quantum hardware today, you know, if you go to the leading providers, you would probably pay a few thousands of dollars an hour to run a circuit. You can do it in a much more economic way on our platform.

• 37:48Speaker 00

  And when you're gaining your confidence, then obviously you go to the hardware, but you understand what you're doing and you understand the root cause for the things that work and do not work.

• 37:58Craig S. Smith?

  And can you give sort of a before, I mean, you did earlier in the first time we spoke, I mean, we did, for example, the 80% to 99% on factoring 21 with Shor's algorithm. But can you give some other before and after results? Yeah.

• 38:25Speaker 00

  Absolutely. One of the things that the industry is moving towards is logical qubits. It means when can we take those physical systems, you know, qubit is the

• 38:46Speaker 00

  Analog of a transistor in classical devices. But the problem is they're very noisy. So you need an ensemble of them in order to do a logical computation with a low enough noise level that they can work at scale.

• 39:07Speaker 00

  Until not long ago, the only system that was able to show that, at least on superconducting qubits, was the Google system.

• 39:16Speaker 00

  Using our system, our digital twin and our augmentation capability, we were able to take an ensemble of qubits from IBM and connect them in such a way that they now perform as logical qubits. Meaning, they can now go ahead and do meaningful computations as you scale the number of those systems. So that's one very interesting achievement that we were very proud of because we were the first to show it outside of those systems. The other one is, thanks to our capabilities, we were able to push the boundaries of the performance of Rigetti hardware

• 40:01Speaker 00

  to a point where they can show 99.9% fidelity on their qubits because we were able to optimize all the governing conditions of this system. And now, obviously, it serves to a point where you can squeeze more performance out of this system. Another example is that we showed that we can significantly improve the performance of a given algorithm using our capabilities to a point where if before that, the noise level were just too high, once you add our capabilities,

• 40:44Speaker 00

  the circuit is at a point where it can really perform and give you the results that you're expected to.

• 40:51Craig S. Smith?

  We talked last time about

• 40:56Craig S. Smith?

  claims of quantum advantage.

• 41:00Craig S. Smith?

  And and those claims are routinely challenged by running the same algorithm classically. Does a digital twin change that verification of of claims of quantum advantage? I mean, can your platform be used as an independent arbiter? When someone claims that they've reached quantum advantage? Or there are reasons that it can't be neutral, your system?

• 41:36Speaker 00

  I I think not only that it can, it is used and we're using it internally to benchmark performance and different algorithms and different approaches on whether or not you're able to get to whether it's logical qubits or below threshold or quantum advantage. Now, that being said,

• 42:02Speaker 00

  every system's scale has its limitation, right? Let's take

• 42:08Speaker 00

  an approach maybe from

• 42:11Speaker 00

  or an example from classical devices. Let's say you want to develop the next GPU.

• 42:17Speaker 00

  You know, GPUs today have tens of billions of transistors in them. The design tools by the leading companies, whether it's Synopsys or Cadence, as advanced as they are, they cannot simulate the whole GPU at once. You cannot run finite element analysis of 60 billion transistors at once, et cetera. So there is a finite limit to the number of of the scale of the system that can be used. But can those simulators and those design tools?

• 42:49Speaker 00

  Predict the performance and tell you whether or not this design is suitable to hit a certain benchmark? Absolutely. So that's exactly the same approach here. We can benchmark and we can give you the tools to develop. We're not saying, and we will never say, that we are replacing quantum computers. We are accelerating the development of quantum computers and accelerating the development of quantum algorithms and giving the industry the best-in-breed tools, combining digital twins and AI to do that. And one of the things that you can check along the way is whether or not a specific configuration and a specific approach can get you to a point of demonstrating quantum advantage.

• 43:34Craig S. Smith?

  Is there a clear line between classical simulation of quantum hardware and what you actually need quantum, a quantum device to do? I mean, at some point, what you were referring to, with the, well, the classical simulation has its limits. Is there, is there a clear line or is it sort of as companies push simulation that that line keeps moving?

• 44:16Speaker 00

  We'll have to say we keep on surprising ourselves with the scale. When we started this journey, we thought we will be capped at a few qubits. And then we've developed a new solver that pushes the boundaries. We can now simulate above 100 noisy qubits. The reason I'm specifying noisy is because those qubits take into account noise models and interactions, et cetera. And this number of 100 is very critical because it taps into quantum error correction, specifically quantum error correction distance 7, which was claimed to be the cap where classical devices cannot simulate quantum hardware anymore. We think

• 44:59Speaker 00

  we can push the boundaries beyond that, but there is definitely a limit. Whether the limit is 200 or 300 or at that range, you know, yet to be seen. But the important piece is, how do you participate and

• 45:18Speaker 00

  Provide those tools as the industry grows and how do you use those tools furthermore to orchestrate things that quantum hardware just is not able to do, you know. I think the days of running quantum design and quantum development softwares on quantum computers is still very far off. It means that there is no escaping from the fact that classical devices will govern and will push the develop of quantum computers in the foreseeable future. And that's the thesis behind everything we're doing.

• 46:03Speaker 00

  It's not a question of whether or not they will participate is it's clear that classical devices are the tools to push quantum forward. The question that keeps us awake at night is how do we provide the industry with the best tools in order to do that in the most effective way?

• 46:26Craig S. Smith?

  Yeah, is, I mean, you referred to it already, but is there a limit to, to, in terms of qubit count or depth of noise complexity in, in what you can simulate or what you, in your digital twin?

• 46:45Speaker 00

  I, I think it's fair to say that with the current tools, we're probably looking at the hundreds of qubits, not more than that.

• 46:54Craig S. Smith?

  Yeah, yeah. And you, you mentioned the modalities, you're working on superconducting and trapped ions. You mentioned when we spoke that you're working on photonics. What are the other modalities that this applies to and, and how portable is a digital twin between different hardware families? I mean, can you apply lessons learned in one to another architecture?

• 47:32Speaker 00

  The underlying physical models are quite different. So if you want to do a decent enough job, you need to understand the physics very well. And you need to be able to represent all the governing noise models in a way that, you know, your results will be accurate enough. So we have to be very selective on the modality that we're picking. At the moment, like you mentioned, superconducting qubits, ions. We're also looking into spin qubits because we have a lot of domain knowledge into that. This is still a modality that's kind of,

• 48:17Speaker 00

  you know, in its early days, but they're moving actually pretty quickly. As you go up the stack, moving away from tools that are used by the hardware designers to tools that are used, for instance, dealing with errors, then you can be a bit more abstract. And then there are components that you can migrate from one modality to the other. So in that regard, it's pretty easy for us to provide quantum error correction platforms for the different modalities

• 48:58Speaker 00

  and in a much shorter timeframe.

• 49:03Craig S. Smith?

  Yeah. And who's your primary customer in this ecosystem? Are they the hardware vendors, the algorithm developers, or the end user verticals like Pharma or Finance?

• 49:20Speaker 00

  We are currently moving along the whole software stack.

• 49:26Speaker 00

  In quantum, everything that you're doing has to be hardware aware. Classical devices, you know, a zero is a zero and a one is a one. Once you've figured out how to manipulate those transistors, from that point on, you know, you might have different flavors of your operating system, but the underlying physics is the same, the underlying operations are the same. In quantum systems, everything has to be hardware aware, meaning you're, the way you're calibrating and operating the system, what kind of pulses you're pushing, then how you run circuits, how do you deal with errors on the circuits, and then how do you deal with noise and air correction on the application layer, it all has to be hardware aware.

• 50:10Speaker 00

  So in that regards, the users are users that are participate, our users are users that are participating today in all of those efforts, meaning we have some users that are literally designing their next generation machine, they want to understand how to optimize their qubits and their gates. There are users that are interested in eliminating errors or noise on the circuit. They're using the platform to execute circuits and reduce the noise. And there are users who are interested in error correction and developing new decoders, they're using the platform in order to do that.

• 50:50Speaker 00

  And very quickly, I think you will start seeing end users using this platform to, in the combination of AI, to be able to accelerate and improve the performance of their algorithms.

• 51:08Craig S. Smith?

  And on the hardware side, I would guess that most hardware providers are using the platform. I mean, if you're showing this kind of optimization, why wouldn't they?

• 51:30Craig S. Smith?

  But, but does that, how does that affect the dynamic, this sort of competitive dynamics between hardware vendors? I mean, if...

• 51:43Craig S. Smith?

  It becomes pretty clear, I would think, who, which modality or which hardware architecture is giving the best

• 51:57Craig S. Smith?

  the best performance, at least in the digital twin.

• 52:03Speaker 00

  That's a wonderful question. And I think it talks into the flexibility of what we can deliver and how we're doing that. So obviously, one approach is to come in and say, okay, give us all of your secret sauce, you know, you've developed this system and you've poured billions of dollars into your and now tell us everything. You know, it might work for a handful of customers, but we come from the industry, we understand how sensitive it is and we appreciate that and we are grateful for our customers. Another approach could be, this is the

• 52:44Speaker 00

  general model that represents your system, you know, just because of the physics is the physics. We're giving you all the knobs, you go ahead and you tweak this model for your system, you don't need to tell us how it works. We don't, we actually don't care. We will give you the best engine to now go and run this twin at scale for your development. So we're giving them the canvas and the mechanism. And now they're going it and doing it in their own proprietary way and we're out of the picture. And this way, we kind of have this firewall between their secret sauce and their know-how and their IP and our capability.

• 53:30Speaker 00

  And I think it works very nicely.

• 53:32Craig S. Smith?

  Yeah. In what you're building, if everyone optimizes their hardware and then optimizes the algorithms and then optimizes for specific use cases, at some point, have you guys optimized yourselves out of, out of demand? I mean.

• 54:01Speaker 00

  You know, the world luckily is always in pursuit of better and faster and bigger. So I, you know, if this day will come, we'll probably have a follow up podcast on how does the life of a startup executive looks after he's retired and won the big prize. But I think we have a lot of challenges until this day will come.

• 54:29Craig S. Smith?

  Yeah, and then, you know, I hate to ask timeline questions, but I always do because people always want to know, what's your sense in that you see this progress of when quantum hardware is really going to be useful in in in real life cases, not just tests?

• 55:00Speaker 00

  I think there are few accesses that are moving in parallel that gives me confidence on the timeline being closer than we think. The accesses are noise level of the system, number of qubits, and the number of qubits that are needed in order to run a specific algorithm. In all of those accesses, we're seeing an acceleration beyond what we expected even a couple of years ago. So to put it in kind of more tangible terms, if we thought previously that we need 20

• 55:42Speaker 00

  million qubits in order to run a specific algorithm with a specific depth and size, with a specific noise level from those qubits, the noise levels that we can tolerate today are higher, the number of qubits that we need is smaller, and the size of the algorithm that we need is smaller. Having said that, I don't think there will be a step function. I don't think we will wake up one day and Quantum will solve all the problems that it should solve. What I think we will see is that Quantum is adding benefit.

• 56:28Speaker 00

  Sooner through some hybrid approach, it is taking a portion of the questions that it's very good at solving from classical systems, solving it, feeding it back to the classical system and allowing us to accelerate in this way to a time horizon that I think is within a couple of years.

• 56:53Craig S. Smith?

  And you're using digital twins and AI to optimize quantum systems, but there's a lot of hype about AI and quantum. I get pitched all the time on using quantum to run AI models. What is, and it's again, it's very confusing to the layman. What, what claims about that combination do you see as genuinely promising and which ones make you roll your eyes and dismiss?

• 57:38Speaker 00

  Well, I'm an expert on how AI can accelerate quantum development. I have to put a caveat. I'm not an expert on quantum AI, meaning AI that is running on quantum systems. But

• 57:56Speaker 00

  I think we need to be very careful when we are extrapolating from our knowledge on classical devices into quantum devices.

• 58:12Speaker 00

  And I think there are still some proof points that we need to see before we can declare victory on AI running on quantum systems.

• 58:25Craig S. Smith?

  Yeah. And then finally, what are the milestones that we should be watching for in the next three to five years to assess advances in the quantum trajectory?

• 58:42Speaker 00

  One obvious is the noise level. We gauge those performance metrics in terms of fidelity.

• 58:52Speaker 00

  Today, best system fidelity. And we have to be very careful here. Usually, people will report single qubit gate,

• 59:03Speaker 00

  which is a bit, it's not a bit, it's much easier than two qubit gates, right? When we're, when people are talking about qubits and quantum system, they're talking about entanglements, this ability to connect qubits. So two qubit gate noise levels are still higher. So we'll see those numbers become better. So 99.9 and then another nine and then another nine. So that's one metric. The other one is the qubit count. How many qubits do you have, physical qubits? I'm not mistaken, IBM is targeting, I think, 10,000 qubits by the end of the 23rd. So that's kind of the rough number. So hopefully, they can track that number. That would be a huge achievement.

• 59:49Speaker 00

  And the third one is the translation of physical to logical qubits. Currently, there are strong indications that we need a few tens of them, maybe 70 or 50. If we can get to a point where we need, you know, a few of them, maybe less than 10, it means that those 10,000 physical qubits can now be a few thousand logical qubits. That's a huge achievement. So I would say those three accesses, noise, count, and number of logical qubits would be the metric to watch. And obviously, to keep on watching the progress on real-life applications and quantum advantage that hopefully will come pretty soon.

• 1:00:34Craig S. Smith?

  Yeah, okay. Well, this has been fascinating, also clarifying.

• 1:00:42Craig S. Smith?

  You're a good communicator, because this stuff can quickly become

• 1:00:50Craig S. Smith?

  too difficult for a layman to follow. Okay, so let's leave it there. I'm going to end the recording. Unless, is there something I haven't asked that you want people to know about?

• 1:01:06Speaker 00

  No, I had a great time. Thank you for having me.