Investing in Quantum Computing: VC's Perspective with Ion Hauer from Apex Ventures

[00:00:00] Rahul: Welcome back to Understanding VC. I'm your host Rahul. Today, we'll delve into quantum computing from the perspective of venture capital with Ian Howard. Ian is a principal at Apex Ventures, where he invests in European early stage deep tech startups in areas of quantum technologies, future computing, space, and AI.

[00:00:17] Rahul: He has over 12 years of experience at the intersection of technology breakthrough and financial capital. And he holds a PhD in quantum physics from Heidelberg University. Let's talk to him. Hi Jan, thank you so much for joining me today.

[00:00:31] Ion: Thanks for having me, Rolf.

[00:00:33] Rahul: Yeah, so I, I read about a Richard Feynman quote, you know, nobody understands quantum mechanics, I mean, tell me about it. it's not really easy to understand.

[00:00:45] Ion: it's true. It's quite, counterintuitive at times, especially if you're used to, let's say, classical physics, you know, from, from our macroscopic world. but once you dive deeper into the microscopic effects of it, it can be quite confusing at first.

[00:00:59] Rahul: [00:01:00] Yeah, I think it has to do with the probability thing, right? Nothing is certain, everything is just a probability. Everything is, I mean, the confusion starts there.

[00:01:10] Ion: Absolutely. It's something that also, I think the human brain isn't very good at. Grasping, you know, non non deterministic outcomes and probabilities. You see this in the, in the stock market when people try to, you know, sort of, yeah, extrapolate where, where the stocks are going and it's super hard for an individual to do.

[00:01:30] Ion: And so all of these kind of biases that people have kind of translate into. Making it very, very unintuitive to understand quantum mechanics. But I mean, it is like formally very, very well described and very well understood experimentally, very well understood. So it's very correct. It's just needs some time to, to get, to get used to it in a way.

[00:01:49] Rahul: Yeah. Yeah. So what is quantum computing?

[00:01:53] Ion: Well, I would say in a way, quantum computing is sort of the natural. Extension of classical [00:02:00] computing. So with classical computing, you have your bits, right? Your ones and zeros where you store information in, and then you have your algorithms that, that, you know, manipulate your, information, your, your ones and zeros to, to, to output, a result and all of this happens sort of on, you know, transistors on, on, on Silicon, typically that, that was the evolution of it.

[00:02:21] Ion: And in quantum, you have quantum bits, right? The so called qubits, which, are not just ones and zeros, but are basically, they can be a superposition of both, you know, they can be both at the same time. This is where it already gets a bit confusing, but that's just how it is, if we accept that for a moment.

[00:02:38] Ion: And I think the, the best, visualization for this kind of state is sort of this, this sphere, right? So you have a sphere where you have different states, for example, one can be at the top, let's say of the sphere, the North Pole and, and zero can be at the bottom. And so you can have pretty much everything in between.

[00:02:56] Ion: So that's kind of how you would, you know, visualize a [00:03:00] qubit. And these things are very powerful because they can store much, much, much more information than. than a classical bit, right? But it's just an extension of that, of that concept, really. and in the end, it's like, again, Feynman said, you know, nature is, is quantum mechanical.

[00:03:16] Ion: So, so the actual, reality is quantum mechanical. So it makes a lot of sense to use a quantum mechanical computer if you want to, to simulate or to calculate something like, proteins or like, like biological matter and so on. so again, that's, that's why I think of it sort of as a natural,extension to, to the classical computing.

[00:03:36] Ion: And in terms of, of operations, so the algorithms that you have, the functions and the loops that you have in classical computing, now they become quantum gates, right? So you have operations that you apply to those qubits and you need to. Sort of, manipulate them, you know, by shining lasers on them or, or a microwave or whatever.

[00:03:56] Ion: So there's different aspects of, of, manipulating those [00:04:00] qubits in order to make them do something right to simulate your, your drugs to simulate your, your financial transactions, whatever you want to apply this to, and then the last part is really this, the sort of the infrastructure, because it's not just Silicon, but there's like.

[00:04:15] Ion: five or six competing possible, hardware architectures, you know, be trapped ions, semiconductor, uh, superconductors, neutral atoms, photonic systems. So, and this is where it becomes interesting because that's totally. I'd say undecided for now, you know, what other sort of the winning platforms, I strongly believe there's going to be several of them, so there's not going to be a strong consolidation to only one, but maybe four.

[00:04:42] Ion: Who knows, right? This is still active research at the moment.

[00:04:45] Rahul: Yeah. So I'd love to know, uh, understand this a bit more deeply. So in terms of a classical computer, you have this input, that goes and then you get a single output, right? And, and usually. the calculation or storage of [00:05:00] data, it's usually in bits, which is two states. And with quantum bits, it's three states, essentially, right?

[00:05:06] Rahul: the zero, one, and the superposition.

[00:05:08] Ion: No, it's basically, it's one state and that one state can have all of this information. So it could be that the state is purely prepared as one. Or a zero, or as again, like if you think of it as an arrow on, on that sphere, on the, on that globe, it can point in pretty much any direction on that, on that ball.

[00:05:29] Ion: So, so that's kind of the one state that you have that you put into your system. and, and really the difference between classical and quantum here is classical. In classical, you have a very deterministic outcome. So you put something in, you get something out. And, if that sort of way is, is, is defined.

[00:05:48] Ion: You always get out the same thing because it's deterministic, right? and so if you want to, like, probe something, you want to simulate, for example, something with different parameters, you have to change the input parameters and then run the [00:06:00] calculation. In quantum, it's different. You prepare your system such that it's very close to, to the environment you want to, to simulate.

[00:06:08] Ion: And then you just, and that's the beauty of it. You only need to run it once. Because all of the different scenarios, the different parameters settings are kind of embedded in that one calculation, right? And it gives you the result. The only thing is, and that is again something to understand, is that the result is not deterministic, it's probabilistic.

[00:06:29] Ion: So if you run the same thing with the same starting conditions, you might get a little bit of a different, outcome. So you need to run it a couple of times. To just get some statistics on, you know, what's the average outcome, what is the spread of it and, you know, but that's still much more efficient than the classical approach is, is just running it millions and millions of times.

[00:06:50] Ion: Yeah, so you're essentially putting all the input variables at one go, and then you're expecting this to come up with, a number of probable output, right? [00:07:00] Ah! This is where I lose it, you know? How does this happen?

[00:07:05] Ion: Well, I mean, the, the state itself, that's, that's quantum mechanics, right? You have trading us equation, you have a formalism that explains, how these states change. And the challenge is to sort of map the states and the system. So the whole gate sequence to construct it such that it mimics what you want to simulate in, in, in nature.

[00:07:35] Ion: Say if you want to, do drug discovery because you want to, to, to, to find a new, protein, for example, or then you kind of, you need to have sort of a a quantum twin of, of your system, of, of reality, of, of, of a body, a human body that reacts, you know, some organs that react with that protein. And so when you bring those two together, they interact.

[00:07:58] Ion: And so all of that, you need [00:08:00] to sort of, you know, map onto your quantum system. You need to simulate in your, describe in your quantum system. And once you have that, You can play around with, with some of the parameters and, and be like, okay, what if I change the molecular structure a little bit and, and then you don't have to do this in the lab anymore.

[00:08:17] Ion: So that's where you can have, like, you know, drug discovery times cut by half or less and, and, and, and costs cut by, by a factor of 10 or so.

[00:08:27] Rahul: Yeah, yeah. And you also mentioned there are like, like, unless in the case of classical computers where which is silicon based, there are like multiple approaches for quantum mechanics. What are like some of the more popular ones?

[00:08:43] Ion: I would say the most popular one that, that also gets the most attention is the superconducting one that, that IBM and Google are developing. just because it's also closer to, to, to silicon in a way. So, so the way like this is CMOS architecture. So, so the [00:09:00] way that, that these things are, are, developed are sort of, more, let's say a bit more incremental or closer to, to, to the classical computing infrastructure.

[00:09:09] Ion: but there's like completely different, systems, for example, trapped ions, right? Or even neutral atoms that you trap with laser light and then every atom is one qubit. And so you can change the state of it. and address each 1 individually, but they also interact with each other. So, there's another important aspect of those quantum systems is that the qubits and you want to have quite many of them in order to, to have a complex to be able to simulate a complex system is that they should be able to interact with each other.

[00:09:40] Ion: And then you get things like superposition and entanglement of. The different, qubits, so they, they're not separate, right, but they very much interact with each other like nature does as well. So, having a long range interaction across, you know, let's say IBM published 400, and produced 433, uh, qubits [00:10:00] out of which not all are like, let's say, useful and they're not all fully entangled.

[00:10:05] Ion: Right? But that's kind of the benchmark. I think they want to bring out a chip with a thousand qubits this year. and other systems have, have maybe downsides in terms of coherence and how long you can run the systems and so on. But atomic systems you can build today with a thousand qubits and they have long range interaction.

[00:10:24] Ion: So, all of these different systems have, have, have advantages and disadvantages. And I also believe that, that they will have different. application areas, for example, so there are some systems, for example, the superconducting ones. So superconducting, means it has to be cryogenically cooled, right? A really, really low temperature, which is quite a lot of energy.

[00:10:45] Ion: You don't put this in your house, right? It's like, it's like this basement type of super sealed and high maintenance, device that you can use then for something like cloud computing. So someone else can have access via the Internet and [00:11:00] classical. means. But there's also other systems that potentially can run at room temperature.

[00:11:05] Ion: And so just looking way, way forward, you can think about a sort of a personal quantum computer, or even like a business one in a smaller setting that you just have somewhere in a room. So, and that becomes much more feasible then. And so that's how I can see different architectures really coexisting for a very long time.

[00:11:24] Rahul: Yeah, so, so I was reading about this, I think, McKinsey robot. they mentioned that, you know, maybe we only need like four or five thousand of quantum computers or something for everyone. I was like reminded of what exactly we said, talked, said about classical computers, right? We said that, you know, the world needs only a few computers.

[00:11:44] Rahul: And now we have every, everyone has one. Yeah. There's also one interesting, tweet that I saw from Trevor Blackwell, he's a partner at Y Combinator, so he was talking about, you know, lack of, the lack of coherence, it happens especially with the quantum computers, [00:12:00] when the temperature increases, right?

[00:12:02] Rahul: and there's also talk about noise, but then he asked a question whether, gravity has a role to play in this, like, in the sense, like, if it's zero g, would there be like, uh, better coherence. Uh, oh, any thoughts on that?

[00:12:19] Ion: I mean, I wouldn't rule it out. I think the, the effects of gravity are rather negligible. and certainly the effort of putting such a system in space. only for the benefit of having micro gravity or zero gravity, is, is not worth sort of the, the effort, right? And the cost. So it's hard enough to, to build it on earth.

[00:12:44] Ion: So we're far away from, from launching quantum computers in space. and I don't see like the immediate, the immediate benefit, the, the biggest bottlenecks right now are the fidelities of, of the [00:13:00] Single,qubit and 2 qubit gates. So the operations you, you, you perform on like 2 qubits, you know, in order to entangle them, for example, to, to create like, interesting and useful states that fidelity is still not where it needs to be.

[00:13:13] Ion: So, it's something like, you know, 0. 99, something 8 or so, when you need to go to like, 3 or 4 nines, right? So 0. 99, basically 99. 99%. Accuracy that when you do something you don't break up the system and that's kind of we take for granted in the in the classical world, there's still a fidelity. It's only so almost perfect that we don't care about it if it's only 99.

[00:13:40] Ion: 98% or so that effect compounds and you need to have like thousands or millions of gate operations. and then at the end, you get like a very messy state that you can't really use anymore. So, so that's what we need to do now is to get better. You know, addressability of the qubits, better [00:14:00] hardware control and so on.

[00:14:02] Ion: And I don't think gravity is a limiting factor, but more like isolating the, the systems from, from outside radiation, from,vibrations and so on. That's maybe a much bigger effect. The vibrations you get from like a tram going past your lab and so on.

[00:14:18] Rahul: Yeah. So, besides the temperature, I also read about noise. So I was curious to know what noise, is this a sound or something else?

[00:14:26] Ion: No, it's a thermal noise. I mean, if if you're never at absolute zero and and in the ground state, so you always have some excitations of of the system. And if, for example, because you have to imagine, like, the, the, the, the chip is like cooled to. What 0. 1 millikelvin or so it's like, really, really,cold, but it's connected to the outside world.

[00:14:50] Ion: Right? So, in every stage, you need to sort of, cool the system a little bit because at some point, you will have a classical connected and you plug [00:15:00] in at room temperature. and so you have different stages of, of cooling, basically. That's why these systems that you typically see on, on, you know, cover of time magazines, they're like these chandelier thing, this, this inverted wedding cake structure, because only the bit at the bottom is like super cool.

[00:15:16] Ion: And the rest is like, not so cool. And at some point it goes to, to room temperature. And, and at every point you can have like, you know, currents flowing somewhere, just increasing like inefficiencies that increase the temperature only a little bit. And, and your superposition breaks down, for example. So that, that's the kind of noise we're talking about.

[00:15:35] Rahul: Yeah. And now, let's, let's talk a little bit about the history, right, and where we are right now,with quantum, quantum computing,I mean, I, I just read today that Richard Feynman was the one who actually coined the term quantum computing. Would love to know a little bit more about the history and the progress that we've made so far.

[00:15:54] Rahul: Yeah.

[00:15:55] Ion: Right. Right. I mean, the, the, the history of, of, of quantum mechanics is actually quite, [00:16:00] quite old, yeah, but the history of quantum computing, as well, I mean, it was 1982 or so, 1980s when, when, when Feynman sort of had this idea that, you know, nature is quantum mechanical. So, and, and classical computers were, were already like conceivable and, and, and even used in some settings.

[00:16:20] Ion: that, you know, there should be something like a quantum computer. So the concept of it was developed in, in, in the 80s. Right? And for good reason again, because it's kind of makes sense that a natural system should be,simulated or computed by by a quantum computer. but then it took a long time and what we're talking about today, typically, when we talk about quantum computers, and even things like quantum communication and sensing is that that's sort of the 2nd quantum revolution, right?

[00:16:46] Ion: But there was a 1st quantum revolution when quantum mechanics could be made very useful. in the 60s 70s with the advance of lasers, for example, and then let's you know Fast forward to the 90s. Everybody had like a [00:17:00] disc man or like a cd player and dvd. That's all quantum Effects right that you need to understand and commercialize, like make usable, for for a large scale application.

[00:17:12] Ion: And that's already and there we take quantum for granted, but we shouldn't that's already quite quite magical. You know, that you can have like a pocket, like a laser pointer and all that stuff. So, that's already quantum in a way, or MRIs, for example, in medicine was, was, was used. So. Already, you know, to, to, to measure the nuclear spin of fabric and then get an image out of that.

[00:17:34] Ion: So that's also quantum, right? So, so quantum has been with us in, in an industrial and in a commercial way for, for a long time for decades. Now, it's only now that with the 2nd quantum revolution, we get into a state or stage where we can actually manipulate these systems. Like I was talking about single atoms being a qubit and then you can shine laser.

[00:17:54] Ion: It's like we have very, very unprecedented control over these systems. and that's kind [00:18:00] of a step function. That's why people call it the second quantum revolution.

[00:18:04] Rahul: Yeah, and, and this happened in 2019, that, that's the moment that people keep talking about, right? The breakthrough where it solved like a mathematical problem that would have taken a classic computer 10, 000 years or something in 200 seconds.

[00:18:18] Ion: Yeah, I mean, sometimes people want to sort of pinpoint it to a date, but I always have trouble with that. I don't think it's, it's ever, you know, a single incident. And even that, that Google publication. Of, of quantum advantage, has been disputed and like, we're not even there yet to, to say, like, once you've proven quantum advantage over a classical computer that you can do it faster, better, cheaper.

[00:18:46] Ion: Or you do something that a classical computer could never do that's sort of the ultimate goal, but you need to do that like 10 times 100 times a couple of times to be comfortable that. Okay. You've not just once right and say, oh, that's the medical point. So once we have that, [00:19:00] you can go back and see.

[00:19:01] Ion: Okay. What were the sort of the milestones? What was the biggest? Maybe the biggest, really biggest milestone is still yet to come. We just don't know yet. And so going back, I would say, even in the. Yeah. 2000s and two tens there were like, you know, gradual improvements, incremental, improvements that, that all sort of played a role in, in, in getting us to where we are today.

[00:19:24] Ion: Right? So we're all standing on the shoulder of giants. It's not just that. Okay. One time Google turned it up and, and, and, and that was a magical moment, but it was a breakthrough. moment and, and it kind of very important, but not a single one and not, not, not the only one.

[00:19:40] Rahul: yeah. I think they built on top of the, research of two, two recent physicists, who were At IBM. And, they had actually theoretically proven this in 2004 or something.

[00:19:53] Ion: I mean, again, I would say even, even today and a couple of weeks ago, there was another claim and [00:20:00] so on. it's, it's not, Yeah, it's still not undisputable, right? You need to get to a level where there's just no way that people are doubting it. And at this point, because it's also a commercial thing, right?

[00:20:14] Ion: It's a bit like IBM versus Google. And they're like, okay, who, you know, it's a bit of marketing, you know, claiming something that you, you achieve because they've also, I mean, and to be fair, I mean, they're the ones that put. A lot of money into into the development. So they also want to, you know, reap something in the meantime.

[00:20:32] Ion: So I get it. but scientifically, we should just be a bit like cautious and observe and see. And at some point, this will for sure come up as a milestone in the quantum computing history. But I wouldn't say that it's sort of the emergence or the advent of anything.

[00:20:52] Rahul: Yeah. So, what are the use, potential use cases? commercial use cases, that is.

[00:20:58] Ion: Oh, there's so [00:21:00] many, because it's really, I mean, if you think about it, it's a platform,

[00:21:04] Ion: it's an architecture, right? It's not just one single thing, but it's just, it's just computing. And so what are the use cases of classical computers?

[00:21:12] Rahul: Everything. So,

[00:21:14] Ion: Recording this podcast here, right? It's everything, right? That's, that's the point, but let's say most naturally again, and starting from, from a logical point is that quantum computers can be used to simulate, to simulate.

[00:21:30] Ion: Quantum systems like natural systems like drug discovery or protein folding and all the like life sciences is certainly a field where cancer treatment and so on is like finding new vaccines and all of that is is you can simulate if you have your quantum twin of of the world, that will have a huge impact that that classical computers cannot cannot do today.

[00:21:52] Ion: and to me, this is sort of the most, again, most natural, most, most intuitive, area, but there's other areas like [00:22:00] logistics, for example, which are purely, I would say digital in a way. I mean, it's just, it's a mathematical problem, you know, the, the, the traveling salesman that has like 20 cities and he knows how far they are apart, but he doesn't know what's the shortest route.

[00:22:16] Ion: So I can visit all of them and get back to where I started from. And this problem is like super hard. It's like NP hard, challenge to, to tackle and it's very, very difficult for quantum for, for classical computers, but quantum computers can do this very well. I, like, if, you know, the infrastructure works, but in theory, they can do this very well.

[00:22:37] Ion: And, and so you will have that sort of, you know, improving train schedules and traffic control in the city is, And all of that things, and also the financial industries, you can do, pricing option pricing. For example, you can do faster and more efficient,

[00:22:55] Ion: more accurate. And it's just, it costs you less to do it.

[00:22:59] Ion: Once the [00:23:00] systems are stable enough. because again, all of the, the different parameters, because there's so many dependencies in pricing and option, all of them are kind of. You know, embedded into the system and then you run it, whereas classically, you need to make sure that you don't, you know, forget any, any dependencies on the outside world.

[00:23:20] Ion: So that's where quantum computers really, really excel. So also in a financial services industry, huge, huge potential.

[00:23:27] Rahul: yeah. And, in terms of quantum, quantum computing's, like, ability to disrupt the existing status quo, I think one area is cryptography, right? So, these computers could easily, make the existing cryptography standards like not viable anymore,

[00:23:49] Ion: absolutely. Absolutely. I mean, if you look at the RSA encryption today, Basically, everything which is encrypted today, you will be able to, [00:24:00] to crack, with a powerful enough, quantum computer. Right? So, so this is why people are working on, on two things. One is quantum, key distribution. So cryptography on a quantum level where it's like.

[00:24:13] Ion: mathematically proven that it's, that it's absolutely secure. Yeah, as I was saying, yeah, by measuring it, you, you, you change the, the, the state. And so, you know, basically that someone eavesdropped. and the other path is of course, like post quantum cryptography, which actually isn't quantum at all, but it's just finding the next.

[00:24:32] Ion: algorithm, classical algorithm, like the successor to RSA, that enables you to, to encrypt your data today, such that it is at least for a while, it's sort of unbreakable by a quantum computer until the quantum computer gets so powerful that it can break even that. So that's kind of a race, right?

[00:24:52] Ion: That's always going to be a race and there's always going to be the need for the next, sort of, generation of, of encryption. [00:25:00] Algorithms, whereas something like, like using the quantum nature to encrypt, the information that there's no, I mean, to our understanding of the world and quantum mechanics, there is no possible way to, to encrypt to decrypt that or to, to break that ever.

[00:25:18] Ion: you don't get more secure than that, right? And that's obviously important for defense use cases, governmental information. So really highly classified information, but it can become, sort of, yeah, an area that, that also telecoms and enterprises go into and they want to have the data really secure like strategy, or, or just important, company data.

[00:25:39] Rahul: And in terms of like factors limiting the space in terms of commercialization, what are those? do we have enough talent? And, uh, is the tech. the, the, the limiting factor here. and yeah, I think we should also speak about, you know, the whole product productivity [00:26:00] paradox, right? The, the cost of adopting this, is going to actually increase the cost. So it's going to make it less productive. That makes it less likely for a lot of use cases or a lot of these companies to adopt this, right? So

[00:26:13] Ion: Right. I mean, it's, but that's always the case with, with a new, especially with such a disruptive technology. So, in the beginning, not, not, I mean, still not many people, I mean, really make money, you know, net net profits, or there's still a lot of investments going in. there's some benefit coming out already.

[00:26:32] Ion: I think the biggest benefit, at least for enterprises is to, to start taking it seriously and digging into it, because if you wait another five years. then it's too late, right? Then, because if you wait until it's like, you know, commodity and it's foolproof and it just works, and then start to really think about, you know, how can I use this for my business?

[00:26:57] Ion: What does it mean for me? how do I even. [00:27:00] Yeah, program a quantum computer like programming. It is really, really different from from just working, you know, in computer science with with python and and so on. So, nowadays, and it's coming back to talent is is basically you need. Someone who is a good programmer and a quantum physicist at the same time.

[00:27:20] Ion: So you kind of need to have both and this is going to become simpler, you know, as, as pro quantum programming languages, become more and more even abstract, but just more, more, more user friendly, let's say, but still at this point, if you really want to, and how do you know if, if you really mapped your quantum system to.

[00:27:41] Ion: To to to a real world problem or not, you need to understand both right and understand how to do that. This is very difficult for now. And I think so. Talent is certainly a limiting factor. Where I would say even, I mean, Europe is far ahead, at least the European Union, in terms of [00:28:00] graduates that have sort of a quantum related, background, even like way ahead of the U.

[00:28:05] Ion: S. And I think that's at least for Europe. It's it's kind of a big plus point. Yeah, there's some other downsides that we can talk about in terms of funding, but in terms of talent, I think that's that's a clear USP.

[00:28:18] Rahul: apparently, University of New South Wales, they started, an undergrad program dedicated for, quantum computing. Yeah.

[00:28:25] Ion: Yeah.

[00:28:26] Rahul: so Like you mentioned, I mean, if you wait long, like for five more years, then you're already too late. So what is the way forward, for all these, companies who are trying to, it's not just solving a product issue, right?

[00:28:40] Rahul: It's also like you have to create that market, like there are not many customers at the moment and how do you convince? So, what is the way forward right now, for, for a lot of these startups?

[00:28:50] Ion: Yeah. I mean, one way forward is, is for sure. and that's going to be important for, for, for some time is, is to do so called, quantum hybrid. [00:29:00] Approaches where you still have a classical component, but, you're basically using a quantum computer and that you can do already today to take care of very limited, aspects of a certain bigger problem.

[00:29:16] Ion: Right? So the classical quant, the, The classical algorithm sort of takes care of the structure. You know what happens? What are the loops and the functions and so on? And then certain bits are sort of taken over by the quantum computer. Then that limited calculation is performed, result comes back, and then the classical computer takes over and does something with it, obviously not in the long term, the dream case and where you can leverage all of the benefits of quantum systems.

[00:29:44] Ion: But one, I mean, you can do something with it today again, just, you know, to, to use it and two, it, it forces you more and more and gradually more to think how, what role does the quantum computer play? And it's going to play a bigger and bigger role. [00:30:00] So you're going to, like, implement more and more classical procedures into the quantum.

[00:30:05] Ion: Part, right? And that because otherwise you're just waiting, right? For, for some magic to happen in the, in the hardware space where it's like, okay, here's a quantum system with 1000 qubits. They're all fault tolerant. You can just use it. And then, okay, what do I do with it now? It's much better to go step by step and sort of.

[00:30:28] Ion: learn this journey as, as, as, as the industry is involving, the tech is evolving, also learn how to use it, which makes much more sense. And so we see a lot of companies,investing already. Significant budgets today in that.

[00:30:43] Rahul: Yeah, and does it also make sense to, you know, try to solve specific problems, maybe like that, you know, predicting weather Something like you said close

[00:30:54] Ion: Yeah, absolutely. I mean, it really depends on on I would urge every enterprise to, [00:31:00] to look at this, you know, and think about the key. What can I take out of this? and then that really depends very much on the, on the, on the, on the industry. but, but, Just thinking, you know, what, what is the benefit of, of quantum computing for, for my, for my personal business and then have at least, you know, some like a team or so dedicated to, to just exploring this, like, you know, companies are doing with AI, obviously, and that that's the right thing to do.

[00:31:26] Ion: it's a different time scale, of course. Yeah, but, but still quantum should, should not be, you know, left for, for the others because you have basically like 3. you know, segments, one are like the IBM's and Google's that are really pushing it forward and, you know, investing a lot in, in trying to build this up.

[00:31:43] Ion: Then there's like the ones that are really taking it seriously and, and as I said, you know, actively exploring the use cases and just trying stuff out and spending some time and money on it to do it now. And then there's like a third class. Which exists obviously, which saying, you [00:32:00] know, it's not for us and it's not interesting, maybe it'll never come and all of that.

[00:32:04] Ion: So we'll just wait and see. And then when it's like a commodity, we'll. Might just, you know, purchase it and have someone else tell us how to use it because then we don't, we still don't know what to do.

[00:32:16] Rahul: yeah, then you're not gonna make much money, but Yes, so you also mentioned the hybrid approach, right? So how is the interaction between classical computers and Is it like a software layer or?

[00:32:30] Ion: well, both. I mean, yes, it's, it's mostly a software layer. Of course, you have, you have a hardware component as well. But, but you, as I said before, you always have that connection from the quantum system, which, you know, be it super conducting and very cool and the topology of it. Yeah. that somehow you have a readout and the readout at some point is going to be turned into classical digital information that is laid on our monitors and so on.

[00:32:56] Ion: So you always have an intersection somewhere. Right? [00:33:00] The question is. Where is it? And in the hybrid approach, it's just narrow. So the quantum part is just smaller and there's more classical around it. whereas in, in the fully quantum scenario, basically quantum plays the biggest role and the rest is sort of.

[00:33:16] Ion: You know, you have a keyboard that you used to type your, your commands into. And then you have an output that basically shows you your database and your files and your graphs and so on. So that that part will always remain classical and also to be to be maybe super transparent. I mean, I don't think that at any point quantum computers will completely, exchange, you know, or make classical computers obsolete.

[00:33:41] Ion: It's like if you think of a, of a, of a cloud stack, you know, and you have like CPUs, you have GPUs, the graphical processing units, and then you will have qps, which is the quantum processing unit. And depending on your problem, you know, maybe even the algorithm is, is clever enough to, to pick, or at least to suggest to you how [00:34:00] much, you know, G P U, capabilities.

[00:34:03] Ion: do you need how much, Q P U, for example, But it's never that, you know, we will never have laptops again or like classical computers. Why should we not have that for very simple things that are just not not at all quantum?

[00:34:15] Rahul: Yeah. And also, you know, I read about this, school of thought, where, you know, people argue that, the human agency might influence the outcomes, meaning the, the, the, the person operating the computer, the quantum computer might need to be considered as part of the system because it has, so it has a big influence on the, the output.

[00:34:38] Rahul: So I didn't fully understand this. So then the argument is that, you know, we need to have standards there as well. So otherwise you would not have that certainty.

[00:34:46] Ion: Yeah, I mean, standards will will arise for sure. I mean, there's in a way there's some standards already being put in place by by just. Shaping how's like quantum programming languages [00:35:00] are built up, right? Like his kid, for example, and, and it's very, in a way, it's sort of close to python and, and that kind of helps, you know, sort of the transition and the logical thinking, but there's many aspects that are just plain new and, and, for example, you need to know much more about, you know, linear algebra and so on and matrix, yeah.

[00:35:22] Ion: Multiplication. And so it's a different kind of thinking and the people that are thinking about it and using it a lot, they will obviously influence where it is going and the development also of the software part of it and the programming languages. So, so that's kind of an effect. in the end, of course, we need to get to a state where it doesn't really matter.

[00:35:45] Ion: Like, if, if, for example, if you have a website, right, of course. Like the design will be different whether you or I make it, but overall, the, like the security has should have a basic, you know, requirements [00:36:00] or, or just the, the, the, what it is used for and how it, how you interact with the website should be fairly standardized, no matter whether you do it or I do it.

[00:36:09] Ion: So, so at that point, we've already come to a conclusion that, you know, that's how it is, or if you upload an app to the app store, they sort of. Certain requirements just to make it a bit more homogeneous and make a bit more expectable. Where do I expect the button or what, you know, how do things work in the quantum world?

[00:36:26] Ion: This is still like super open because no one has and there's not 1 person is going to decide it, but it's rather it's just going to converge. And so the people working on it, we'll have a big influence in setting some of those. De facto standards, even if it's not, you know. Not regulation or anything.

[00:36:44] Ion: It's just things that just grow into, into being. And at some point they're just, okay, this is how we're going to do things now.

[00:36:51] Rahul: Yeah. Yeah. Yeah. Now, from a VC point of view, is this emerging tech really VC [00:37:00] fundable, in the sense, I mean, like you already, we already spoke about the existing, existing companies like big companies, big tech companies like IBM and Google spending a lot of resources. And there are also, my understanding is that there's also startups who are well funded and who already has like a lot of platforms already in place.

[00:37:22] Rahul: So then is there opportunity for new startups to come in? If yes, where are those opportunities?

[00:37:29] Ion: So let's say, I mean, there's hundreds of, of startups, in, in the quantum space globally, and, and for good reason, because there's a lot of opportunity. also besides, you know, the incumbents and the classical, the big players. The, the fields, I would say, of quantum computing, especially on the hardware side, is sort of closing up because there's already quite a few big ones, well funded in the US, you know, public companies [00:38:00] and so on, like INQ, Rigetti, and some others.

[00:38:04] Ion: So that becomes more and more difficult to come up with a completely new hardware architecture and, and, you know, just start now. That kind of seems a bit, a bit late. but up until recently, a couple of, let's say, years or months ago, there were still opportunities. So it's not, you know, it's not ruled out.

[00:38:23] Ion: that there's still opportunities. I think the bigger opportunities are for sure, in the communication and sensing, part, especially early stage. If you want to invest in an early stage, you know, seed series a company today, you better bet would be to go with one of the communication or sensing, startups.

[00:38:43] Ion: At the same time, reality is that, you know, the quantum computing part of, let's say the overall quantum technologies ecosystem

[00:38:54] Rahul: yeah,

[00:38:55] Ion: is also where most of the money lies, right? And most of the funding, so it's, so it's [00:39:00] disproportionate by a factor of 10 or so, how much money goes into quantum computing. So, as a VC is always the question, you know, what's, what's the journey?

[00:39:08] Ion: So, so there's going to be growth funding required at some point. Where does that money come from? I mean, there's huge governmental programs as well. There's tens of billions of dollars worldwide, allocated, and committed by, by governments for, for these kinds of programs for, for these, for quantum technologies. So that's, one, one sort of, funding source, you know, just governmental contracts, like the DLR, for example, in Germany is, is, is handing out. And so that makes a lot of sense, but just know that, yeah, if you start today with a quantum startup in, in, let's say communication sensing and so on, you'd have to think really big, like about the, the, the size of the market and where this can be going, because the exits, let's might not be as.

[00:39:56] Ion: As, as large as a startup that really builds up an entirely [00:40:00] new hardware architecture.

[00:40:01] Ion: yeah. And has there been any exits? So I think I've read about, IonQ, which went public via SPAC, but then it didn't do well. So has there been any exit? It's still a bit early, I would say. So for sure, INQ is, is a very prominent,example. And, I guess to subtract sort of the, the the macro perspective and the whole, like maybe, you know, having done a SPAC wasn't like at the time it was a bit, you know, a bit more trendy. Maybe not the best decision.

[00:40:37] Ion: It's hard to say, you know, what the alternative would have been, at least with that path, they could, they could secure some funding. It doesn't mean that the underlying tech or, or what they're developing isn't good. So, so it's, they're still making good progress. so I certainly not write them off for sure, but, it's not, it's not, the time hasn't come yet for, for the sort of the.

[00:40:59] Ion: [00:41:00] Bigger consolidation or like larger companies buying off, you know, quantum startups and so on just because it's all still very early So and if if you compare that to a sort of classical vc 10 year fund life cycle that is of course a challenge right there is a challenge because You you do something, you know, which is Yeah, you need an exit, but you also want to fund something and then create a company, which is like disruptive.

[00:41:27] Ion: And in that area, because we're not talking about e scooters, we're not talking about, you know, 10 minute toothpaste delivery, this kind of innovation takes. Much, much longer than, than let's say a business model innovation or so. so things take time. There's still ways for every VC at every stage. If you're super early, like pre C for example, then you can do secondary, so you don't even need to IPO or sell the company.

[00:41:53] Ion: You can just take some money out, LPs and then. Maybe you find a way to, [00:42:00] I don't know what the follow on fund or so to still, you know, retain a share in that startup. If you really believe in it and you just want to stick with it for for longer than 10 years, let's say there's there's possibilities to do that.

[00:42:13] Ion: but, but, yeah, it's something to to keep in mind. Yeah.

[00:42:17] Rahul: Yeah. And some deep tech funds, they have 12 or 15 year funds. So. That also probably makes sense.

[00:42:24] Ion: Yeah, for sure. I mean, that that that helps a little bit.

[00:42:28] Rahul: Yeah, and like, okay, in terms of assessing like a quantum computing startup, right, what, what is the process like?

[00:42:36] Ion: I think so. I think we've reached sort of a technical understanding well enough for that not to be the bottleneck. I mean, if we look at a startup, we look at the technology and then we can understand whether it's like, let's say, you know, legit or not. So, so we can see that. And most of them really are because they come from, you know, [00:43:00] scientific background.

[00:43:01] Ion: So we see very little, I wouldn't say none, but very little cases where. people just make something up, let's say, or it's like super exaggerated. Typically, it's what they claim also also makes sense and it's technologically feasible. So then the differentiator really becomes the business side. So are you able to generate traction in whatever way, like interest, time, money for sure, like POCs.

[00:43:28] Ion: And so with, with customers, right? So can you get someone else than just us as a VC? Interested and engaged into what you're doing. That that's probably the most,vital, you know, differentiator when we look at different deals and some are just very early and they tell us, you know, I know because we're still.

[00:43:48] Ion: Developing and others are a bit further ahead and just wants to, to co develop together with their customers or like development partners and so on. and, and this is of course a much better [00:44:00] sign.

[00:44:01] Rahul: Yeah, and, You know, people talk about science risk and engineering risk when it comes to assessing things like this. Could you explain? I really want to understand the difference between a science risk and an engineering risk, especially in the context of quantum computing.

[00:44:20] Ion: Absolutely. So the, the, the question is really about the maturity of, of, of the technology. So at some point you're, it's, and you start with the science risk, which is, is it at all scientifically. Possible. Like, is it, is it feasible that, that you get, certain effects to, to, to run that? That's sort of the, the very early stage.

[00:44:45] Ion: and that kind of transforms into the engineering challenge. Let's say where you need, for example, to do this on a large scale. So you can mitigate the scientific risk by showing that it worked once or 5 times, but then you have the [00:45:00] engineering. Challenge to, to come up with a process to scale it to, to build a thousand, 10, 000 of it, depending on what it is.

[00:45:09] Ion: Like if it's a whole quantum computer, if you build 50 or so respect, you know, that's already, that's already engineering. If it's like a quantum repeater, for example, that you need to enhanced, and amplify your signal every 50 to a hundred kilometers. and you want to do this lot, then you need a lot of them.

[00:45:29] Ion: So then we're talking about, okay, you need to build like 50, 000 to to show that. Okay. This really scale. So what I've done in a small package, I can now do on a large scale. That's kind of the, the engineering,challenge and and thus, wherever there's a challenge, there's a risk that it doesn't work.

[00:45:47] Ion: So, okay. And with quantum, yeah, a lot of it is still in the scientific area. So, so just again, build one, build one quantum computer, and then we'll think about scaling it, but you do [00:46:00] have to think about it from the beginning. Like you can't just blindly built one and then turn the knob and be like, okay, now I'm scaling, but rather while you're building the first one.

[00:46:09] Ion: You think about every component. Okay. How is this going to look like in 10 years? Is this something should I use this component or that or this one scales better? So I'm going to use that because I know where I want.

[00:46:21] Rahul: Yeah. And you know, I heard another VC talk about this. Usually the founders operating in this space are from scientific background and like you said, you know, then scientists usually operate with the mindset of, doubt. But if you're a business person, you have to operate with the mindset of conviction. So it's like opposing kind of thought, which makes it a bit challenging.

[00:46:47] Ion: Absolutely. Absolutely. But when you look at. Purely business driven teams and maybe some of the not so technical areas. then, then you have the other risk [00:47:00] on the other hand, that it's like overly optimistic, right? And we get like startups and companies built around business cases that just don't work or only work in a very low interest rate environment.

[00:47:12] Ion: And so should that exist, right? Or is some form of caution,you know, Makes sense to to have it and to have a deep. Thought about which, which way to go and what is commercializable and whatnot. So, but yeah, for sure the best teams we see, and, and, and we invest in, you know, have both these, These aspects and are good at business as well,

[00:47:35] Rahul: Yeah. So, it's, it's a hard time for, all kinds of startups. how hard is it for, quantum computing startups in terms of fundraising?

[00:47:46] Ion: right? I mean It really depends on so I mean there's obviously like the stage aspect of it like the earlier stage you are the Less harder, relatively speaking it is, right? [00:48:00] So, we still see a lot of pre seed, seed, even series A funding rounds, Sure, at different conditions than two years ago, but then also two years ago was like,an anomaly.

[00:48:11] Ion: so there's still activity. And also if you compare it to, because it's deep tech and you compare it to, you know, some, let's say shallow tech, startups that are having a much harder time, like if you're a FinTech right now, or so it's, it's even harder to, to do so in that case, it's relatively. good compared to other possible scenarios.

[00:48:32] Ion: but overall, like looking at over time, of course, it's a bit more challenging right now. We, we see a little bit of, of relaxation now, you know, hoping that next year could be, you know, picking up momentum and just having more activity. So a number of startups founded in that area is decreasing a bit, but that always comes and goes a bit in waves.

[00:48:53] Ion: So, so I wouldn't put too much emphasis on that. we're really just there, you know, looking for, for [00:49:00] exceptional teams that, that, that are building something in that, in that space, because in the end, we don't need to invest in 20 of them, right? We invest in two or three and that's kind of our. Portfolio structure

[00:49:12] Rahul: Yeah. And, in terms of progress, right? So let's say, a startup is at series B funding stage in quantum computing. Will those startups already have a customer or like, you know, what would be the progress that they've made?

[00:49:27] Ion: Yeah, for sure. I mean, the, the, expectations on, on, revenue levels and so on are obviously quite different from, from, typical SAS business, right. again, because of the maturity of the entire industry. So it's not to blame the startup that they haven't. You know, gotten to 100 million ARR, but you should definitely at that point have proven that, that you have, again, something worth, you know, spending time and money on.

[00:49:59] Ion: and [00:50:00] so, I mean, for sure, if, if, if, if you're going towards series B, you'd have some double digit, millions of revenue, in, in, in some sort, you know, typically it's like project base. It's not yet. Fully recurring because again, companies don't know yet where this is going and they're not planning super far ahead, but like more on a year by year basis, which is fine for now, but at least, you know, they see the value.

[00:50:26] Ion: Maybe they have again, like, repeating customers and, and, and follow on projects and so on. So you can look at all kinds of different metrics, that, that, that show that, you know, there's real business value and interest from, from customers there.

[00:50:41] Rahul: Yeah. And, why are you, so interested in quantum computing as an emerging tech, personally?

[00:50:48] Ion: Well, I mean, I am a quantum physicist myself. So, so I did my PhD in quantum physics at Heidelberg University, at a time where [00:51:00] just wasn't, at least for me back then, really feasible to, to, to spin out anything or commercialize anything of, of, of that. But I've always kept sort of this, this Love and fascination and interest in, in quantum physics for sure, following it over the years and, and so it's, it's kind of natural for me to, to be in that, in that space and, and combining it with, with a business perspective, you know, to see how you can create value beyond sort of the scientific achievements, that, that for me is sort of the, The perfect intersection of, you know, technological breakthroughs and, and financial capital

[00:51:40] Rahul: So, let's summarize this, right? So, if I were to ask you, what would be your advice for somebody who's interested in studying quantum computing, on a founder who wants to build some, a startup in quantum computing, or like a VC who wants to fund [00:52:00] quantum computing startups and businesses, who may or may not be interested, what would be your advice be for all these people?

[00:52:07] Rahul: Sensei. Sensei.

[00:52:14] Ion: to look deeply into the field, you know, and, and, and research, you know, quantum computing, languages, programming languages, maybe even take a course at a university. I mean, there's entire quantum degrees or, or, or very quantum heavy, and quantum focused degrees at a, at a bunch of universities, hundreds of universities worldwide that offer that.

[00:52:37] Ion: so if you're thinking about a career path, you know, next to data science and say, classical I. T. I think quantum and quantum information is like, very important. It's going to be super important in the future. So worth, you know, brushing up skills and getting also even like a degree in that in that area.

[00:52:56] Ion: Very future proof. for founders, [00:53:00] As we discussed before, I think the biggest opportunity nowadays and for some time will will be in quantum communication and quantum sensing. so you can use sort of your, your tech expertise, in in those areas and to to start a company and there's funding for that. Likewise for VCs, right? if, if they're new sort of to, to the topic, look into those areas and also trying to find a strong consortium with, with a quantum expert. So, I'm very much for strong syndicates where one party, you know, can bring the, the quantum, expertise and others that don't have to be, you know, fully made out of, Of quantum physicists and their team in order to be able to invest in quantum computing.

[00:53:46] Ion: So that way we can just leverage more more capital for this for this cause for this important, breakthrough and lastly, for the enterprises, it's really about getting. Into it right now and not waiting [00:54:00] five years and be a laggard. So, so you want to be spending time and a certain amount of budget and maybe allocate a small team for the topic of quantum and then understanding what are the different quantum areas that benefit your business, in the future and then start working on them today.

[00:54:18] Rahul: Yeah, thank you, thank you, thank you so much.

[00:54:22] Ion: My pleasure.