Latest / Lab to Market Leadership with Chris Reichhelm / Building Useful Quantum Computers: Constraints, Customers, and the Two Religions of Quantum | Richard Murray

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Building Useful Quantum Computers: Constraints, Customers, and the Two Religions of Quantum | Richard Murray · Lab to Market Leadership with Chris Reichhelm

• 0:00
  Richard Murray: I think one of the things I've observed is that when you get started with a new technology, it may be in a university research lab, you, you might have a sort of technology vision, but often that's completely unconstrained.
• 0:12
  Richard Murray: You know, we wanna build a full to computer, that's it.
• 0:15
  Richard Murray: That, that's the thing that will win us, the Nobel Prize and beyond that doesn't really matter, but we all know that in the world of, you know, real technology and, and business, there are lots of constraints.
• 0:27
  Richard Murray: Um, and I think actually by the way, working within constraints can be a really creative process.
• 0:32
  Richard Murray: But you've gotta learn to do that.
• 0:33
  Richard Murray: And, you know, that constraint might be, well, okay, we can build this thing, but at what cost?
• 0:38
  Richard Murray: At what cost to the customer at, you know, what value will it provide?
• 0:42
  Richard Murray: Um, or any one of a number, like a timeframe, like, we wanna build this thing, but do we want to take 15 years to build it?
• 0:49
  Richard Murray: Do we wanna take 10 years to, or, but, but adding in constraints to your definition of the technology vision you have in order to lead to a. A more sort of outcome driven mission, you know, more, uh, application if you like.
• 1:04
  Richard Murray: Driven mission, I think is, is, is is really important.
• 1:08
  Chris Reichhelm: Welcome to the Lab to Market Leadership podcast.
• 1:12
  Chris Reichhelm: Too many advanced science and engineering companies fail to deliver their innovations from the lab to the market.
• 1:19
  Chris Reichhelm: We are on a mission to change that.
• 1:20
  Chris Reichhelm: My name is Chris Reichhelm and I'm the founder and CEO of Deep Tech leaders.
• 1:25
  Chris Reichhelm: Each week we speak with some of the world's leading entrepreneurs, investors, corporates, and policy makers about what it takes to succeed on the lab to market journey.
• 1:36
  Chris Reichhelm: Join us.
• 1:42
  Chris Reichhelm: What are the trade-offs that deep tech innovators need to make in order to realize their ultimate innovation?
• 1:58
  Chris Reichhelm: That is the topic for today's discussion and to help bring this to life.
• 2:04
  Chris Reichhelm: We're gonna set it this topic against the context of quantum computing, which perfectly illustrates I think, the inherent conflict.
• 2:20
  Chris Reichhelm: What do I mean by this?
• 2:23
  Chris Reichhelm: There's a lot of talk about what the world will look like, about the problems we're gonna be able to solve when we have realized truly fault tolerant, full stack quantum computing capability, that drugs will be discovered in a faster period of time.
• 2:43
  Chris Reichhelm: We'll be able to develop novel, advanced materials, we'll be able to better model more accurately, uh, climate change and so on.
• 2:55
  Chris Reichhelm: If we're a company like let's say Google, which is very active in the field of quantum computing capability with lots of money to throw around and invest, we can almost pursue this task and an academic slash RD type way.
• 3:11
  Chris Reichhelm: Hire the best scientists and engineers, incentivize them, let 'em get on with it.
• 3:16
  Chris Reichhelm: It's not as simple as that, but you know what I mean.
• 3:19
  Chris Reichhelm: But if we're just about any other company, and especially if we're a startup, that's not possible.
• 3:25
  Chris Reichhelm: And why?
• 3:27
  Chris Reichhelm: Because too many stakeholders around the table need to have value developed along the way.
• 3:33
  Chris Reichhelm: We can't wait to get to that point, which may be realizable 10, 15 years away.
• 3:40
  Chris Reichhelm: Maybe it's not that long now, but again, you know what I mean.
• 3:44
  Chris Reichhelm: So we need to demonstrate that we're delivering value along the journey, and in order to deliver value, we've, that means we have to develop commercial value.
• 3:53
  Chris Reichhelm: How do you make a project that's in motion commercializable?
• 3:59
  Chris Reichhelm: My guess that's what this topic is all about.
• 4:02
  Chris Reichhelm: Where are those compromises?
• 4:03
  Chris Reichhelm: Where are those trade-offs?
• 4:04
  Chris Reichhelm: Where are the trade-offs technically, operationally and commercially?
• 4:08
  Chris Reichhelm: To help me get into this today is Richard Murray, who is the co-founder and CEO of Orca Computing.
• 4:16
  Chris Reichhelm: And Orca is a UK headquartered University of Oxford Spin Out, which is developing full stack, full tolerant quantum computers.
• 4:26
  Chris Reichhelm: But they're not doing that right now.
• 4:28
  Chris Reichhelm: They're on a path that may or may not get them there, and so along the way they're gonna be making these trade offs that I've just referenced technically, operationally, commercially.
• 4:41
  Chris Reichhelm: Where is the line drawn for Richard and his team along that path?
• 4:44
  Chris Reichhelm: That's what we're gonna get into today.
• 4:48
  Chris Reichhelm: I'm certainly gonna get a huge amount out of this.
• 4:50
  Chris Reichhelm: I hope you do too.
• 4:51
  Chris Reichhelm: Let's get into it.
• 4:57
  Chris Reichhelm: Richard Murray, thank you so much for joining me.
• 4:59
  Richard Murray: It was great to be here.
• 5:00
  Richard Murray: Thanks for having me.
• 5:02
  Chris Reichhelm: Take me back, take us back, if you will, to when you and your co-founding team decided that it would be a good idea to build quantum computers.
• 5:21
  Chris Reichhelm: What was, what were you guys thinking at the time?
• 5:25
  Chris Reichhelm: What was the vision?
• 5:27
  Chris Reichhelm: What was the, I guess the motivation and what did you, what was the anticipation?
• 5:33
  Chris Reichhelm: What was the expectation?
• 5:34
  Chris Reichhelm: Sorry?
• 5:34
  Chris Reichhelm: What was the expectation?
• 5:36
  Chris Reichhelm: What did you think you would build?
• 5:38
  Richard Murray: That is a great question, and if you don't mind, there's a complicated story that I'll unpack a little bit for you because, um, so I'm not the academic behind the company.
• 5:47
  Richard Murray: I. So, in fact, my, my journey, I have a PhD from a long, long time ago, but I was working for the UK government on the quantum program that I helped set, set up.
• 5:57
  Richard Murray: And then, you know, I've been in and out of technology consulting on the commercial side.
• 6:01
  Richard Murray: So I came to this through the lens of being involved in quantum, involved in the policy behind quantum technology, quantum computing.
• 6:11
  Richard Murray: But actually I went around the country and met lots and lots of different academics with lots and lots of good ideas before meeting orcas to academic, uh, co-founders.
• 6:22
  Richard Murray: So Professor Ian Warmsley and Dr.
• 6:22
  Richard Murray: Josh Nunn, they were the ones who'd been working in the lab, uh, to develop this sort of cool technology.
• 6:30
  Richard Murray: Um, but in the middle of those two backgrounds I think comes a really interesting story that relates to what you were describing.
• 6:37
  Richard Murray: So on the academic side, they'd been working a lot on quantum networks, these sort of big optical networking type things.
• 6:47
  Richard Murray: Lots of different things.
• 6:48
  Richard Murray: They can be useful as networks, both quantum and classical, and they can, they, the academic team found that they could also be useful for computing.
• 6:56
  Richard Murray: So when the academic team and met, yeah, we knew each other, but when we sat down and talked about what Orca could be, there was a huge range of different things, some of which were not quantum computing, which we could have done.
• 7:08
  Richard Murray: And, um, in fact, for a long, long time we were sort of working out like, what exactly do we wanna do with all this amazing technology?
• 7:16
  Richard Murray: But, you know, could, can we do all of it?
• 7:18
  Richard Murray: And, and when I came in, I was quite keen that we didn't try and do everything, uh, by the way, to our conversation today, that has been an, an ongoing discussion about how much we do do and what, what we do focus on.
• 7:31
  Richard Murray: But from the very start, we, I tried to sort of set, you know, set the team up so that we weren't gonna do everything.
• 7:37
  Richard Murray: We, we called ourselves orca computing for a very good reason.
• 7:41
  Richard Murray: To keep reminding ourselves that we're trying to do computing, and if we'd have called ourselves and, but this is discussed, if we called ourselves Orca Quantum or Orca Photonics, then we could have been, we could have maybe not had that clarity.
• 7:55
  Richard Murray: So right from the very start, we got together as a team to focus on computing and, and the quantum computing.
• 8:04
  Richard Murray: And, and the reason for that was a few different reasons from the technology side, from the, you know, academic perspective.
• 8:08
  Richard Murray: They saw a great potential to see their research realized.
• 8:13
  Richard Murray: You know, in the, in general where they sort, they sort of came from a tech push point of view.
• 8:17
  Richard Murray: They wanted to see their, their staff made real, made more robust and all of this type of stuff.
• 8:22
  Richard Murray: But from my point of view, and maybe I've always had a more market facing point of view, I, uh, I saw that a lot of existing computers were moving more and more to photonics.
• 8:34
  Richard Murray: So more of the photonic, you know, more of the computers that we rely on having on the inside, more and more platonic components.
• 8:41
  Richard Murray: Also there's this huge overlap with the world of, um, hardware making, machine learning better.
• 8:48
  Richard Murray: So even though I wasn't totally from the market perspective, I, I think I was really keen on what the application of this cool stuff could be for computing.
• 8:57
  Richard Murray: And, you know, I guess the coming together of those two themes landed us squarely in the middle of photonic quantum computing, which is what we do.
• 9:05
  Richard Murray: Um, yeah.
• 9:06
  Richard Murray: But that it, it, it, there is, there are a few different perspectives that I think came together to maybe make us really excited about what we could do in the world of computing with our photo quantum technology
• 9:17
  Chris Reichhelm: and I suppose a certain amount of that kind of ambiguity, if you'd like, that, that it's not always a hundred percent clear, I think with any group of founders.
• 9:29
  Chris Reichhelm: You're not an academic founder, but you are a founder of the company, generally speaking, uh, as its, as its market, face and CEO.
• 9:38
  Chris Reichhelm: Um, but.
• 9:39
  Chris Reichhelm: You know, I wonder if that, you know, how common that is for that.
• 9:44
  Chris Reichhelm: And I wonder if there's a piece in, in the company's history around we want it to do computing, and if it's more quantum related and great, it's more photonic related, great.
• 9:58
  Chris Reichhelm: But we want do computing for particular applications.
• 10:02
  Chris Reichhelm: Would that be fair?
• 10:04
  Richard Murray: Yeah, I, from my perspective, definitely.
• 10:07
  Richard Murray: So, I mean, I definitely saw, you know, this huge sort of almost gaping hole where technology hardware could be better.
• 10:15
  Richard Murray: Um, sorry.
• 10:16
  Richard Murray: Computing hardware could be better.
• 10:18
  Richard Murray: You know, I spent my whole career watching computers evolve.
• 10:21
  Richard Murray: I, I do quite often focus on where I think the biggest market is.
• 10:26
  Richard Murray: And, you know, it's not that, it's not that hard to see that there's a massive interest and market for computers.
• 10:32
  Richard Murray: Even when we started six years ago before the AI boom sort of kicked off.
• 10:37
  Richard Murray: I think that's really important because I think with bots in general, you can do a lot of stuff with bots and I think a lot of bots companies end up in what I sort of call research mode, where they get really excited about serving the research community to build a new microscope that might serve another researcher.
• 10:56
  Richard Murray: And for me, that all sounded just too small.
• 10:59
  Richard Murray: Um, and it's, it, it is sort of like in the, in the world of computing that I see a real, a really exciting need for new technologies, new ways of doing it, and then all the way up to the top because of what we now know that computers use loads of energy that sort of, you know, worrying for all of us how much energy computers are using.
• 11:18
  Richard Murray: Um, also, I always say this, and I always think it's true that computers just aren't, computers just aren't very good at lots of things, so we.
• 11:27
  Richard Murray: I stood up on stage a few days ago in front lots of people and said, just, just think about how bad Siri is.
• 11:33
  Richard Murray: So everyone loves to talk about ai, but let's just be honest with ourselves.
• 11:36
  Richard Murray: How bad, uh, things like Siri or, you know, uh, satellite routing, uh, you know, uh, GPS type systems are and things like this.
• 11:45
  Richard Murray: Computers is, are really not very good and we like to think of them as human, but they're a long, long way from being any type of sort of having the computational power to be able to be more, more natural in the way that they approach things.
• 11:58
  Richard Murray: So, or maybe
• 11:59
  Chris Reichhelm: our expectations have become completely, our expectations are increasing because I think if you compare them to, you know, probably computing standards five years ago, they're much, much better.
• 12:10
  Chris Reichhelm: It's just now our expectations are that much greater.
• 12:13
  Richard Murray: That that's true.
• 12:13
  Richard Murray: Yeah.
• 12:14
  Richard Murray: And we all expect those computers to be in our phone as well as performing this human-like interactions.
• 12:18
  Richard Murray: So, yeah.
• 12:19
  Richard Murray: But I mean, the, the big picture is that I do love the idea of computing.
• 12:23
  Richard Murray: You know, I love the idea of us as humans being able to process information.
• 12:27
  Richard Murray: I, I do really passionately believe that computers could be a whole lot better and a, a very, uh, sort of surface level photonics has always been this technology which had the potential to completely change computing.
• 12:40
  Richard Murray: So people have been talking about photonic computers without the quantum part, but for years now, for decades, and there's never been a moment where that made sense.
• 12:50
  Richard Murray: And now we've got the convergence of, you know, maybe new ways to do computing, photonics with obviously the, the need, urgent need for new types of computers driven by AI and, and our expectations that computers should be better.
• 13:03
  Richard Murray: So it seemed to me like the, sort of the most amazing moment to just apply some very new technology, but the, the need for that new technology has never been more, more pertinent.
• 13:15
  Richard Murray: Yeah.
• 13:15
  Richard Murray: Um, in my view.
• 13:16
  Chris Reichhelm: So what it, so the, the end goal, however.
• 13:21
  Chris Reichhelm: Is still to develop full stack fault tolerant quantum computing capability.
• 13:26
  Chris Reichhelm: Is that right?
• 13:28
  Richard Murray: Yes.
• 13:28
  Richard Murray: Well, on our technology roadmap, that's the, that's the endpoint that we observe.
• 13:33
  Richard Murray: But maybe if I was to unpack that a little bit, I think as a company we've changed quite a lot.
• 13:39
  Richard Murray: So that if, if I was to describe our vision, um, it is to build useful quantum computers and, and those just might happen to be fully fault tolerant, uh, quantum computers or they might not.
• 13:52
  Richard Murray: But the, the thing that now is our sort of north star is it doesn't matter what the technology is.
• 13:58
  Richard Murray: We want to see them being useful being, and you're smiling because I'm sure this is what we'll get into a lot, but there's a, there's a such a drastic difference between being sort of guided by a technology mission and, you know, the, the worrying thing there is that that technology mission might be wrong, you might get it wrong, versus a, I don't know, a more market led mission.
• 14:18
  Richard Murray: And in, in everything related to how the company approaches things.
• 14:22
  Richard Murray: We're trying to, and you know, I shouldn't present it as a, as a completed task, but we're trying to move everyone onto this new mission of just doesn't matter what it is, but what we're trying to do is build useful, commercially useful, uh, you know, valuable quantum computers in whatever technical form that may take.
• 14:39
  Richard Murray: And yeah, that's as, and that's worrying for lots of people.
• 14:43
  Richard Murray: 'cause it's so much more ambiguous.
• 14:45
  Richard Murray: It's harder to define it if it's not one very specific technology, but it's like, oh, it's useful.
• 14:50
  Richard Murray: Well, it's useful me, um, we dunno.
• 14:52
  Richard Murray: Let's find out.
• 14:53
  Chris Reichhelm: Yeah.
• 14:53
  Chris Reichhelm: But I think in, in, especially in deep tech, I think this is relevant.
• 14:57
  Chris Reichhelm: You know, we're gonna come back to all those points you just mentioned because they're, they're, they're very, very relevant and I think they represent maybe compromises or trade-offs is a bad term.
• 15:07
  Chris Reichhelm: I don't mean that in the pejorative sense.
• 15:08
  Chris Reichhelm: I mean it in the, in, in almost a practical sense and.
• 15:12
  Chris Reichhelm: And, and maybe in a sense that forces us to take a look to reevaluate what our real intentions are here.
• 15:22
  Chris Reichhelm: Um, you know, we see this in ai.
• 15:24
  Chris Reichhelm: There's a lot of talk about realizing a GI, uh, putting the safety concerns to one side for a minute, but, uh, with, without necessarily understand and understanding of the pros and cons of, of, uh, of realizing that goal.
• 15:43
  Chris Reichhelm: And I think deep tech innovators and, you know, I think, uh, true fault tolerant quantum computing capability is another one of those goals.
• 15:52
  Chris Reichhelm: We want to do that.
• 15:54
  Chris Reichhelm: I think scientists and engineers within the deep tech communities are particularly, uh, uh, good at focusing on a goal like that.
• 16:04
  Chris Reichhelm: Then just going for it regardless of the benefits.
• 16:09
  Chris Reichhelm: Yeah.
• 16:10
  Chris Reichhelm: It almost to to demonstrate least of all to themselves.
• 16:15
  Chris Reichhelm: That, that it can be realized that it can be done.
• 16:18
  Chris Reichhelm: That they can use their powers of, uh, of, of logic and reasoning and, and, and brilliant to, to accomplish such a, uh, a difficult, uh, a task, a challenge.
• 16:31
  Chris Reichhelm: Mm-hmm.
• 16:31
  Chris Reichhelm: Um, but then of course you rightly bring up, but you know, and you ask the question beyond it, which is but to what end?
• 16:39
  Chris Reichhelm: Yeah.
• 16:39
  Chris Reichhelm: And if we're going to apply such a, an enormous amount of capital and investment, not just in terms of finance, but in terms of human capital as well, and energy and resources.
• 16:52
  Chris Reichhelm: All of the stuff that goes into building all of, and realizing all of these innovations.
• 16:57
  Chris Reichhelm: 'cause it's not just money, is it?
• 16:59
  Chris Reichhelm: We know it's not.
• 17:00
  Chris Reichhelm: So, you know, then for what benefit?
• 17:04
  Chris Reichhelm: Is the, is there not something we can do?
• 17:06
  Chris Reichhelm: Is this not, is, you know, should we not be considering this potentially from a different perspective?
• 17:10
  Richard Murray: Yeah, I, I totally agree.
• 17:12
  Richard Murray: Um, I mean, one of the, about that trade off, uh, point as well, because I think one of the things I've observed is that when you get started with a new technology, it may be in a university research lab lab, you, you might have a sort of technology vision, but often that's completely unconstrained.
• 17:29
  Richard Murray: You know, we wanna build a fault tolerant computer.
• 17:31
  Richard Murray: That's it.
• 17:32
  Richard Murray: That, that's the thing that will win us, the Nobel Prize and beyond that doesn't really matter, but we all know that in the world of, you know, real technology and, and business, there are lots of constraints.
• 17:44
  Richard Murray: Um, and I think actually by the way, working within constraints can be a really creative process.
• 17:49
  Richard Murray: But you've gotta learn to do that.
• 17:50
  Richard Murray: And you know, that constraint might be, well, okay, we can build this thing, but at what cost?
• 17:55
  Richard Murray: At what cost to the customer at, you know, what value will it provide?
• 17:59
  Richard Murray: Um, or any one of a number, like a timeframe, like, we wanna build this thing, but do we want to take 15 years to build it?
• 18:06
  Richard Murray: Do we wanna take 10 years to, or, but, but adding in constraints to your definition of the technology vision you have in order to lead to a, a more sort of outcome driven mission, you know, more, uh, application if you like, driven mission, I think is, is, is is really important.
• 18:25
  Richard Murray: And it's, it's so hard for people.
• 18:27
  Richard Murray: One of the things I've noticed employing people who tend to come from a university background is they hate me.
• 18:32
  Richard Murray: Give me telling them, or the market telling them constraints.
• 18:36
  Richard Murray: Like, oh, you could do this, but you are not gonna, because if you do it this way, this is not gonna be, you know, you're not gonna build this thing any less than a billion dollars.
• 18:44
  Richard Murray: So we're not, we're not gonna do that.
• 18:46
  Richard Murray: That fundamentally challenges them, I think because they're driven by the science.
• 18:50
  Richard Murray: They're driven by, you know, building this thing that no one has ever done before.
• 18:55
  Richard Murray: Yeah.
• 18:55
  Richard Murray: Um, so I think that's a really important challenge.
• 18:58
  Chris Reichhelm: Yeah.
• 18:59
  Chris Reichhelm: I, I like the point you raise on, uh, on the, on the piece around constraints and how constraints can actually be, uh, what's required in order to realize an innovation or a pathway to part of the innovation.
• 19:14
  Chris Reichhelm: And it re it reminds me of, uh, of airplanes in the Wright Brothers.
• 19:18
  Chris Reichhelm: I dunno if you know this story, but when they were trying to get the planes off the ground, literally, uh, there was another group, uh, a consortium of wealthy benefactors and individuals who came together and applied a, a, you know, uh, you know, uh, all resource, um, a resource intense, uh, gr group to, to build airplanes and to get 'em off the ground.
• 19:41
  Chris Reichhelm: And they had lots of people and lots of smart engineers and uh, and uh, and lots of capital to be able to do it.
• 19:47
  Chris Reichhelm: And then you had, uh, the Wright brothers.
• 19:49
  Chris Reichhelm: Who had none of that just, and it was, and it was a few of them.
• 19:54
  Chris Reichhelm: And, uh, and the Wright brothers won.
• 19:57
  Chris Reichhelm: Uh, you know, they were the first ones to get, you know, that's why we remember them and not this other group because they had constraints.
• 20:04
  Chris Reichhelm: Yeah.
• 20:04
  Chris Reichhelm: They had capital constraints and they had the human constraints.
• 20:07
  Chris Reichhelm: And so they had to figure out things in a different way.
• 20:10
  Chris Reichhelm: And, uh, I think with deep tech innovators, uh, and deep tech innovation generally, particularly in 2025, we're in an age where, uh, where we, where it's easy to think about the absolutes and the realization of these absolutes and only think in terms of absolutes, but to forget all of the wonderful benefits which may emerge, uh, along the journey, which we haven't quite put a label on, and which may not represent the absolute realization of gold, but may wind up delivering a huge amount of value nonetheless.
• 20:44
  Richard Murray: Absolutely.
• 20:44
  Richard Murray: It's a funny story that when we first got started, I should have said this, when we started talking about the introduction talker, when we first got started, we raised a siege pre-seed round that was 1.5 million pounds.
• 20:57
  Richard Murray: So we got started with a tiny amount of money and we, I remember us sitting in this office and, um, we were talking about what we might do and it was so obvious to us at that point that we weren't gonna build anything substantial with the funding that we had, you know, along the normal path of just, okay, let's just go out and build this massive system.
• 21:14
  Richard Murray: So we sat down around the table, what can we do, uh, with the, you know, money that we have available.
• 21:19
  Richard Murray: And we came, actually, came up with actually a much more resource efficient method of delivering a very first quantum.
• 21:26
  Richard Murray: And that was great for us because that meant we could build, build the system with just one.
• 21:30
  Richard Murray: It was cool because we just had one quantum light source and one detector rather than, you know, tens which other people might need.
• 21:37
  Richard Murray: Um.
• 21:38
  Richard Murray: So it was good for us 'cause we could do something, but then we, we quickly found it was really good for our customers.
• 21:42
  Richard Murray: We could, we could do something for them as well for that same, same amount of money.
• 21:47
  Richard Murray: But this very early on we were constrained and had to be creative to learn to work within that constraints.
• 21:56
  Richard Murray: And, okay, there is a trade off.
• 21:57
  Richard Murray: I don't think you wanna be too over constrained too often, but I think a certain amount of constraints or, or limits to resources at your disposal stop you just being somewhat like, I dunno, idealistic maybe in the way you approach things and do, force you to be much more grounded.
• 22:14
  Richard Murray: And I think that that does actually drive innovation, um, in a, in a useful way and Great.
• 22:21
  Richard Murray: Maybe one day I'll compare us to the Wright Brothers.
• 22:25
  Richard Murray: Yeah,
• 22:25
  Chris Reichhelm: yeah, yeah, yeah.
• 22:26
  Chris Reichhelm: Well, how difficult was it to convince your colleagues to pursue a, a commercial line and, uh, as opposed to the ideal?
• 22:38
  Richard Murray: Um, funnily enough, I think back then it was very easy 'cause everyone could see the constraints.
• 22:43
  Richard Murray: Here's the money we've got, what are we gonna do?
• 22:45
  Richard Murray: Let's do this.
• 22:46
  Richard Murray: That's great.
• 22:47
  Richard Murray: Um, I think then when you come into more resources, then maybe more of the challenge comes, like, you know, the question will be, well we've, we've now got more funding.
• 22:56
  Richard Murray: We've just secured a bit more funding or something.
• 22:58
  Richard Murray: Why are we still pursuing this constrained model?
• 23:01
  Richard Murray: Why don't we just flip over to the, you know, idealistic model of building quantum meters.
• 23:07
  Richard Murray: And it's funny, it's hard then to explain.
• 23:10
  Richard Murray: Well that's because, uh, that's what, and it actually's not that the market has spoken, it would, it's not as easy to say that we've got all these customers lined up for this meal resource constraint because you have to sell a vision.
• 23:21
  Richard Murray: And that vision is that I believe that within, if we constrain ourselves to build quantum in a more useful way.
• 23:29
  Richard Murray: The, the market is bigger for that, but that's something that you can debate.
• 23:33
  Richard Murray: You know, I can, you can, people can disagree with me and say, well, actually I disagree.
• 23:37
  Richard Murray: I think that actually these much bigger quantum competitors have a much bigger market potential.
• 23:42
  Richard Murray: So I think, I think the challenge comes later when you have the option to pursue a much less constrained approach, maybe a much more scientific approach.
• 23:51
  Richard Murray: We're always having to explain why we believe in listening to customers.
• 23:55
  Richard Murray: As crazy as that sounds, you know, why, but in the world of quantum, you know, like, do our customers really know what quantum of eating should look like?
• 24:02
  Richard Murray: And you know, I think that to an extent they do.
• 24:05
  Richard Murray: Of course they do.
• 24:06
  Richard Murray: They know what a computer looks like.
• 24:07
  Richard Murray: They know how much money they'll spend on a computer, and the fact that it's a quantum computer doesn't change that.
• 24:13
  Richard Murray: Um, so it's, it's an interesting thing that, and by the way, it's, it's, it needs constant reminding.
• 24:20
  Richard Murray: I constantly have to sort of reiterate with my team and also to investors.
• 24:25
  Richard Murray: This is what we believe.
• 24:26
  Richard Murray: Um.
• 24:28
  Richard Murray: We are, we do believe in this, um, more commercial, uh, the commercial reality behind quantum computing rather than, um, yes.
• 24:37
  Richard Murray: A more idealistic version.
• 24:39
  Richard Murray: Yes.
• 24:39
  Richard Murray: Um, I call, I call it a religion.
• 24:41
  Richard Murray: I say I actually, sometimes I say there are two religions in quantum computing.
• 24:44
  Richard Murray: Those that believe in nothing but large scale fault tolerance, billion dollar systems, and we have a different religion that's about more immediate usefulness, commercial relevance, value.
• 24:57
  Richard Murray: And they can align of course, but they are, to a certain extent, quite different.
• 25:01
  Richard Murray: Sometimes
• 25:02
  Chris Reichhelm: it is a different path.
• 25:03
  Chris Reichhelm: It is a different path.
• 25:04
  Chris Reichhelm: Yeah.
• 25:05
  Chris Reichhelm: I love the point you raise about the constraint.
• 25:07
  Chris Reichhelm: When the constraint is lifted through additional resources like finance, suddenly that focus goes away or can get a little, you know, can get a little blurred.
• 25:18
  Chris Reichhelm: We, on the, on one of the recent podcasts we did, we talked about this a lot, how one of the.
• 25:24
  Chris Reichhelm: One of the real challenges for a lot of deep tech companies, especially with fairly young teams, is the pressure of so much capital and how when thing, you know, we start to see, we can often start to see things go wrong.
• 25:37
  Chris Reichhelm: I'm not, by the way, making a case to limit the capital you give to deep tech companies at all.
• 25:43
  Chris Reichhelm: But the, it's, it's how we react to the large s to having that resource.
• 25:51
  Chris Reichhelm: It's the, it's the pressure we feel as humans and our ability to deal with, with, with fewer constraints.
• 26:00
  Chris Reichhelm: And then the creeping back of that idealism and what we might build.
• 26:05
  Richard Murray: Yeah, that's a real pressure.
• 26:06
  Richard Murray: It's so, it's so important to, to me and then maybe the way I do things, um, and, uh, it, it's a sort of strange, wanting to make life slightly harder for myself than, or ourselves as a company that maybe we could do because we want to constrain ourselves and want to.
• 26:23
  Richard Murray: Uh, make sure that we think carefully about what we do to make the most of the, what I mean, even when we've got more resources, make the most of the resources that we have.
• 26:33
  Richard Murray: Um, and, and by the way, I think in this world of quantum computing, there are other companies with almost limitless amounts of resources.
• 26:41
  Richard Murray: You mentioned Google, uh, IBM also privately funded startups have essentially infinity money right now.
• 26:49
  Richard Murray: So you have to think to yourselves, and actually we went through a big think thought process about a year ago where we thought, well, if we, if we also operate in a totally resource unconstrained way, all we're doing is following the same path, but $990 million fewer and three years behind.
• 27:06
  Richard Murray: So we've gotta constrain ourselves in some way.
• 27:09
  Richard Murray: We've gotta think differently, otherwise we're just gonna end up at the same point.
• 27:13
  Richard Murray: But, you know, three years after those other, you know, if we're lucky, three years after that.
• 27:17
  Richard Murray: So we definitively decided to take a different path to be much more commercial.
• 27:22
  Richard Murray: And also to think harder to, in order to constrain ourselves according to the, what we thought the needs of the customers are and should be, um, in order to make ourselves competitive.
• 27:34
  Richard Murray: And it's always quite funny.
• 27:35
  Richard Murray: Like I do, I do think that we are still competitive with those companies that have raised way more money than us because we just had this much more commercial approach.
• 27:44
  Richard Murray: And because we've made life maybe a little hard on ourselves, um, in order to apply these constraints,
• 27:51
  Chris Reichhelm: I I can believe that I can, I can definitely believe that.
• 27:54
  Chris Reichhelm: I, you know, one of the things I've observed with, especially in, in, in the field of quantum where you have a number of different, uh, uh, players who are building, who are trying to build fault tolerant full stack quantum computers is the dec is the ultimate decision that's pending around focus.
• 28:14
  Chris Reichhelm: What are you ultimately really gonna focus on?
• 28:16
  Chris Reichhelm: Because if you're doing.
• 28:18
  Chris Reichhelm: Everything from the quantum development, the chip design to the, to the, uh, the cabling and, and the distillation, refrigeration, cryogenics and the firmware and the electronics and the software and the application layer and that.
• 28:33
  Chris Reichhelm: Oh, it's just so much.
• 28:35
  Chris Reichhelm: There is so much in there.
• 28:37
  Chris Reichhelm: Uh, and it's, it's not cheap.
• 28:39
  Chris Reichhelm: It's definitely not cheap, realizing all of that.
• 28:42
  Chris Reichhelm: And, and so even if you raise a big round and if you have lots of people, that money's gonna go pretty quickly.
• 28:47
  Chris Reichhelm: Yeah.
• 28:47
  Chris Reichhelm: If you're, so you postpone that make buy decision that most other established technology companies have to have to consider.
• 28:57
  Chris Reichhelm: Where is our core focus?
• 28:58
  Chris Reichhelm: We, where can we deliver the most value and what are we gonna rely on partners for in, or, uh, and, uh, in order to, in order to build ultimately a successful company.
• 29:11
  Richard Murray: Yeah, you're right.
• 29:12
  Richard Murray: Actually make buy is one of probably the hardest decisions that I think we have to make because.
• 29:19
  Richard Murray: Mostly driven by the fact that so often, um, it's more natural for the team to approach it if they just make it themselves.
• 29:26
  Richard Murray: Um, so, and it might take more resources to your point, but you've got complete control over, it's easier, right?
• 29:33
  Richard Murray: Um, but the, the bigger picture is, well, no, actually, firstly, it's much more efficient if you do it another way.
• 29:39
  Richard Murray: If you focus on what you do well and you leave everything else to other people.
• 29:43
  Richard Murray: Um, but also if you invest the time and the effort into that.
• 29:48
  Richard Murray: But that investment, by the way, takes a different skillset to outsource a technology to a supplier is a much more diff a much different skillset to being a physicist and building stuff in the lab.
• 29:59
  Richard Murray: Um, so it requires it.
• 30:01
  Richard Murray: So you, you almost by default, fall into the way of working, which is just, well, we need more money 'cause of course we're gonna do it all ourselves rather than, again, more thinking, more efficiently in a more focused way.
• 30:12
  Richard Murray: Well, we don't need to do this.
• 30:13
  Richard Murray: And actually, here's a great company over here and it's gonna take us three months to get that company up to speed.
• 30:18
  Richard Murray: But once they're, and by the way, they can also ramp up production if we ever need them to so much more.
• 30:24
  Richard Murray: I think it's a great example.
• 30:25
  Richard Murray: Make buy 'cause it's just so often seen as a default option.
• 30:29
  Richard Murray: One way of there's any one way of doing it.
• 30:31
  Richard Murray: Yeah.
• 30:32
  Richard Murray: But when you, when you operate in a different way, focus towards, you know, achieving some commercial outcome, you've gotta do things differently.
• 30:39
  Richard Murray: You've gotta think in a different way.
• 30:41
  Richard Murray: Um, and yeah, outsourcing can, yeah.
• 30:43
  Richard Murray: The, the buy side can be much more important.
• 30:46
  Chris Reichhelm: Yes, it can.
• 30:48
  Chris Reichhelm: You, you've mentioned customers a lot, um, and uh, and you had in our earlier conversations too, do you think customers understand the quantum offering?
• 31:03
  Chris Reichhelm: Do they feel, and let me kind of expand on that question a little bit.
• 31:07
  Chris Reichhelm: Do they feel, well this is gonna be like classical computing.
• 31:10
  Chris Reichhelm: And I'm gonna type some stuff into my keyboard here and it's gonna just produce more accurate stuff.
• 31:16
  Chris Reichhelm: Is that what it is?
• 31:18
  Chris Reichhelm: Um, or do they, uh, and, and I guess it's depending on the software, maybe it will be, maybe it will be 'cause software will disturb and that to an extent, or, or do they see it as a truly new computing paradigm?
• 31:33
  Richard Murray: Really good.
• 31:33
  Richard Murray: Um, I don't, I don't think they know, I don't think they know, by the way, I was gonna be slightly provocative in saying that.
• 31:39
  Richard Murray: I don't think a lot of quantum companies know the answer to that question either.
• 31:44
  Richard Murray: So it's not like we're helping with that uncertainty.
• 31:47
  Richard Murray: I think.
• 31:48
  Richard Murray: Um, and this, I think describes the promise or maybe the potential promise, but also the opportunity of quantum of eating.
• 31:55
  Richard Murray: It can be both.
• 31:56
  Richard Murray: It can be one or one or the other.
• 31:58
  Richard Murray: It can be more of a, an accelerator to a class, an existing computer, or it can be a completely new thing, unlike anything they've ever seen before.
• 32:08
  Richard Murray: Those customers, they don't know.
• 32:10
  Richard Murray: Right.
• 32:10
  Richard Murray: You know, they don't, I mean, they might have employed some quantum physicists, but they're relying on the vendors to tell them.
• 32:15
  Richard Murray: And I think then the vendors don't really know.
• 32:18
  Richard Murray: You know, there's, like I said, there's different religions, so it doesn't help.
• 32:20
  Richard Murray: They talk to two different vendors and they get two different answers.
• 32:24
  Richard Murray: Um, so I don't think it's as easy as just saying you can just listen to your customer, because I think, and I don't think it's as easy just to say, well, yeah, we would just let this play out and see how it goes.
• 32:35
  Richard Murray: I think it does require, well, the starting point of a conviction that it works in a certain way.
• 32:42
  Richard Murray: Um, and then so in to a certain extent, we go to our customers and we say, here's our vision for quantum computers.
• 32:50
  Richard Murray: And by the way, most of them turn around and say, oh my goodness, this is much more understandable than anything I've ever, I've, I've, I've heard from so many different people, all of this confusing stuff about quantum is half of which I don't understand, half of which doesn't seem to really relate to what I'm trying to do within my business.
• 33:06
  Richard Murray: Finally, someone's actually explained it in terms of a, a value creator or, or a product.
• 33:11
  Richard Murray: Um, so I think that for me is the sort of initial moment where I think, you know, you've got something where you propose something and the customer at least says this makes sense.
• 33:19
  Richard Murray: Um, and then the next part is then to obviously move forward and validate with them to the point of them spending money and committing to you and obviously, you know, scaling operations with, with you and things like that.
• 33:30
  Richard Murray: Yes.
• 33:31
  Richard Murray: Yeah.
• 33:31
  Richard Murray: I dunno if that answered your question.
• 33:32
  Richard Murray: I think my answer is it does.
• 33:33
  Richard Murray: No one really knows.
• 33:34
  Chris Reichhelm: No, no, but that's, but I, but that's my sense, that's my sense.
• 33:39
  Chris Reichhelm: Um, and I think it comes back to these, these, I won't say arbitrary goals that we set ourselves as humans.
• 33:48
  Chris Reichhelm: You know, putting a man on the moon in 1969 was gonna be a good idea, not just through the fact that we, you know, it was of limited value to put a man on the moon.
• 33:56
  Chris Reichhelm: We could, you know, look at space rocks and um, and those sorts of things.
• 34:00
  Chris Reichhelm: And, uh, uh, but it was all of the second and third order considerations that.
• 34:04
  Chris Reichhelm: That, that, uh, extended from that, from putting a man in the moon, all that we learned about science and engineering, that that, you know, that gave way in some ways to this great era of innovation that we've had since.
• 34:19
  Chris Reichhelm: Um, and I see that with Quantum, uh, a bit.
• 34:22
  Chris Reichhelm: And I think one of the benefits of going early with customers is you can have those conversations with them along that journey.
• 34:29
  Chris Reichhelm: I wonder, and, and this, you know, we're gonna kind of glide into the, you know, the, the, the topic of solutions and product market fit and, and products and, you know, what are we actually offering here and what are they gonna be able to do that they couldn't do before?
• 34:45
  Chris Reichhelm: And yeah.
• 34:46
  Chris Reichhelm: You know, how do we measure value?
• 34:48
  Chris Reichhelm: How do they measure value?
• 34:49
  Chris Reichhelm: But when you're talking to an audience of customers and what they're looking, I'm assuming is, you know, they're looking for, you know, some kind of competitive advant, you know, advantage.
• 34:58
  Chris Reichhelm: So look, you know, so actually it would be really helpful for you to discuss Orca computing and where you guys sit in that customer journey.
• 35:07
  Chris Reichhelm: Who are your target customers?
• 35:09
  Chris Reichhelm: Um, what kind of benefit are they getting because I know there's a strong machine learning component to their, uh, and a data distribution piece.
• 35:17
  Chris Reichhelm: So actually before I kind of go down that, yeah.
• 35:20
  Chris Reichhelm: Could you extend you, could you talk a little bit about the solutions that you guys are kind of building and why?
• 35:26
  Richard Murray: Yeah.
• 35:26
  Richard Murray: No problem.
• 35:27
  Richard Murray: In fact, before we do that, do you mind if I just make a sort more general point about all that, that product market Yeah, of course.
• 35:31
  Richard Murray: Market that you mentioned.
• 35:32
  Richard Murray: So, um, I, I, I don't think I've mentioned yet, but actually before, or I worked for the UK government on their, I, I help run their, their quantum program.
• 35:41
  Richard Murray: And I think it's interesting because there is a massive difference between how countries approach innovation.
• 35:47
  Richard Murray: If you've got a big innovation program and okay, you wanna see some succeed, so you just sort of.
• 35:53
  Richard Murray: Where the idea of innovation can be a lot more just related to sort of osmosis.
• 35:58
  Richard Murray: You know, you fund a few things, those things may or may not work.
• 36:01
  Richard Murray: It doesn't matter 'cause that company might sort of not succeed, but then the founders will go on to do something else that might succeed in that world.
• 36:09
  Richard Murray: Just innovation could be a lot more, like, let's say like osmosis.
• 36:12
  Richard Murray: You sort of hope that, you know, eventually the right ideas coalesce on the right sort of i right markets or the right, um, product solutions.
• 36:21
  Richard Murray: I, I don't think that model works at all for startups because, you know, we've only got one company.
• 36:28
  Richard Murray: We've only got a small number of customers that we're hoping to build solutions with.
• 36:33
  Richard Murray: So the idea of not being super focused on what exactly and, and then, you know, product market iterating on that product vision with them super closely.
• 36:42
  Richard Murray: But knowing that, you know, two months wasted going down the road that you don't think will work out is, might be fatal.
• 36:48
  Richard Murray: I think everything can be, needs to be much less like osmosis, much more focused on where you really think the product opportunities lie.
• 36:56
  Richard Murray: And, and, and I'm only highlighting this because I think this highlights the massive gulf of understanding between say, government and policy makers talking about innovation, but quite often without sort of a lot of substance without, without a lot of specifics and potentially university academics as well.
• 37:12
  Richard Murray: And then what, how companies operate.
• 37:14
  Richard Murray: And so, sorry, sorry about that slight setup, but I think that was interesting.
• 37:18
  Richard Murray: Um,
• 37:18
  Chris Reichhelm: we could have a whole hour on that.
• 37:22
  Richard Murray: Yeah.
• 37:22
  Richard Murray: But stop, and lemme because you set up nicely and I should't go through this without tell you more about, ok.
• 37:27
  Richard Murray: So, um, so I mean, firstly orca with, with the way that we build, uh, our quantum meters with photonics, the first thing it allows you to do is build these systems that look much more straightforward than what a lot of people imagine a quantum computer to look like.
• 37:41
  Richard Murray: So.
• 37:42
  Richard Murray: We, our products, they look like normal server rack, so they're sort of black boxes.
• 37:48
  Richard Murray: They roll off the back of a lorry, they get installed into data centers very easily without the picture that some people have of quantum computing is that it's a sort of golden chandelier, a lot of expensive equipment, um, and very different from the normal server like boxes that, you know, customers and data centers expect.
• 38:06
  Richard Murray: So, so that's the first half of what we do.
• 38:09
  Richard Murray: We build these quantum computers, but in a way that looks completely different, much more practical, much more like what existing data centers, for example, are use, are interested in using.
• 38:20
  Richard Murray: But that only gets you so far and because you know, you could install a computer and in sort of computer and inverted commas 'cause no one really knows what one has to do with this thing.
• 38:31
  Richard Murray: And really, in the early days we were largely sort of physic led.
• 38:36
  Richard Murray: We went to customers talking to them about photons and qubits and.
• 38:41
  Richard Murray: Pretty much a hundred percent of our customers had no idea what we were talking about.
• 38:44
  Richard Murray: Um, so we quite quickly realized that unless we had an application view, which actually is also in the way we allow customers to use our systems through the software layer as programming environment, but also a link to applications, um, it's very, very hard for those customers to really understand tangibly what what you do.
• 39:06
  Richard Murray: So actually from sort of the first year onwards, Orca spent a lot, a large, large proportion of our effort investing in applying our systems.
• 39:16
  Richard Murray: And what a great piece of work that came outta that is, um, most firstly the application, the link between quantum and generative ai.
• 39:26
  Richard Murray: So applying quantum to existing generative AI models and actually demonstrating how quantum can make those Gen AI models better.
• 39:35
  Richard Murray: We're just on the journey to demonstrate that.
• 39:37
  Richard Murray: But that alone would be a game changer for the world of quantum video.
• 39:40
  Richard Murray: It would be huge.
• 39:41
  Richard Murray: Um, and then slightly more sort of boring if you like, but also just as important is the, the way that we allow quantum computers to talk and communicate to with existing computers.
• 39:54
  Richard Murray: So it was also a realization that quantum computers don't, they're not a standalone thing.
• 39:58
  Richard Murray: What they need to do is integrate.
• 40:00
  Richard Murray: They need to be side by side with existing computers in a hybrid way.
• 40:05
  Richard Murray: And to do that really well, you can do it in a sort of hand wavy cut type way, in a way that customers don't think is very good.
• 40:11
  Richard Murray: But we, we needed to really invest in, um, close, we call it close coupling, close integration between the quantum and classical computers so that those two systems can work really well.
• 40:22
  Richard Murray: So well in fact that the user doesn't really see that they're two different types of computers, even though behind the hood we know that the quantum system is operating completely different from the classical.
• 40:31
  Richard Murray: We provide that integration layer so that customers can sort of understand what's happening, not see them as too, too different a thing and, and easily operate with those things.
• 40:43
  Chris Reichhelm: And so how will the customer know that they've got quantum capability?
• 40:48
  Chris Reichhelm: Will they be able to process more data or perform more Yeah.
• 40:51
  Chris Reichhelm: Complex tasks.
• 40:53
  Richard Murray: So the short answer is that in the performance that they, they're able to achieve.
• 40:57
  Richard Murray: So I mean, we do benchmark our systems to show a performance uplift.
• 41:01
  Richard Murray: You know, there's no point in working with these quantum systems unless they can be shown to be better.
• 41:07
  Richard Murray: And in this world, the customers we speak to, they, they are all in on very, very detailed technical benchmarks of machine learning performance and things like that.
• 41:17
  Richard Murray: So, so one answer would be that they just, they see, uh, uh, an improvement in their model.
• 41:23
  Richard Murray: And the, the types of results that we've started to see are things like you can perform the same operation with fewer GPUs, so fewer spend less money with nvidia, which of course is useful for lots of people.
• 41:35
  Richard Murray: Um, you, that also correlates to a potential very significant energy saving, and this is the one that maybe really gets me outta bed knowing that current AI models consume a, a, an unsustainable amount of energy in a way that should really worry us if we're thinking about using AI so, you know, sustainably in, in the future.
• 41:56
  Richard Murray: So the idea of reducing the energy consumption, those models, not only helps of course with carbon footprint, also makes them economic.
• 42:03
  Richard Murray: The final really core result, which we're still validating, but if, you know, if we demonstrate this we're, we would be a huge shift in the quantum market is an ability for those AI models to be, to be more expressive, to, to start to predict results that fall outside of the training data.
• 42:21
  Richard Murray: So normal AI models are based on just lots and lots of training data.
• 42:26
  Richard Murray: That's how they work.
• 42:27
  Richard Murray: But they're normally quite constrained to working within the sort of more or less, the sort of limits of that training data.
• 42:34
  Richard Murray: As soon as you start to ask it, ask for data that pulls outside that training data, it struggles.
• 42:40
  Richard Murray: So say for example, say in the world of using AI to predict new molecules, which is where we spend a lot of our effort, the model will be very, very good at giving you a mole molecule or a new drug that looks very similar to an existing drug, finds it much harder to predict a completely new drug and create new molecule that no one has ever seen before.
• 43:01
  Richard Murray: Um, in that world of completely new molecules, completely different types of data, we see that our quantum system can really boost the performance way away from the existing training data set.
• 43:14
  Richard Murray: If even a little bit of that ends up being true, uh, you know, of course we've validated, but that, that is huge.
• 43:20
  Richard Murray: And that's that really then you can start to think of your AI system as much more, um, creative, which we think AI is creative, but reality is not so much.
• 43:31
  Richard Murray: So yeah, if even a hint of that is true, that could be enormously powerful in seeing AI evolve into much more creative tasks.
• 43:40
  Richard Murray: Um, now I'm not going to go too far down that road because all the, the results, what we've seen are things like molecules, things like, um, industrial processes, quite constrained things.
• 43:49
  Richard Murray: I'm not gonna talk about AI become, becoming cognitive or anything, but, but really tangible industrial results that come outta making AI slightly more creative, slightly more expressive, different from the training data.
• 44:03
  Richard Murray: And, um, that makes me really excited and, and all the while building on this huge backbone of quantum of eating, which we all know will keep evolving, keep getting better.
• 44:13
  Richard Murray: Time after time, time, but now linked to an application that makes a huge difference.
• 44:17
  Richard Murray: If, if you can, if you can land it.
• 44:19
  Chris Reichhelm: Yeah.
• 44:20
  Chris Reichhelm: It, it's, um, I mean it's very exciting to hear you talk about that and the, the, the benchmarks you guys are currently going for.
• 44:33
  Chris Reichhelm: It reminds me a little bit of the late nineties, early two thousands when Oracle used to run this big campaign around their servers and the performance of their servers, and it was a very, very effective marketing campaign.
• 44:50
  Chris Reichhelm: It was generally in the Economist, and they had a white backdrop and red lettering.
• 44:54
  Chris Reichhelm: Oracle servers run X percentage faster than IBM's or someone else's and use less energy.
• 45:03
  Chris Reichhelm: I don't think they ever said and use less energy.
• 45:04
  Chris Reichhelm: No one talked about it in those days, but, but they, it was that, it, it was the clear benefit.
• 45:10
  Chris Reichhelm: It was a clear, it was.
• 45:12
  Chris Reichhelm: It was a clear message, it was gonna be useful for engineers who were looking for efficiency, um, or were looking for just performance.
• 45:21
  Chris Reichhelm: It was all about the performance of their servers and, uh, and, but it, it got the message home.
• 45:28
  Chris Reichhelm: And I think that's something that the, I think customers can relate to.
• 45:34
  Chris Reichhelm: If they can say, we can do your job more efficiently, we can make your AI or your machine learning platform, um, much more creative or more able to process complex tasks more accurately.
• 45:48
  Chris Reichhelm: That's a, I mean, that's a, as you rightly say, that's a game changer.
• 45:53
  Richard Murray: Yeah.
• 45:53
  Richard Murray: And it's funny in quantum computing, because I think the last five years of quantum computing, it's been that long.
• 45:59
  Richard Murray: First Quantum have it sort of merged about five years ago, the last five years of quantum computing, quantum has survived on the basis of.
• 46:08
  Richard Murray: The, the applications are groundbreaking.
• 46:10
  Richard Murray: We can invent new molecules and all that, but without being very specific and yeah, about it for being promoted as the, the first change, uh, or paradigm shift in computing that has been, you know, many decades.
• 46:23
  Richard Murray: But I think now users of the customers are much more sophisticated.
• 46:26
  Richard Murray: We've seen Goldman Sachs, we've seen, you know, BP invest quite heavily in, in their own quantum research team.
• 46:34
  Richard Murray: So they know that they need to land specific applications, which are of above and beyond just the sort of generalities of, oh, well maybe it could, you know, do this, but don't ask me exactly when you'll be able to do this or, or what exactly it can do.
• 46:48
  Richard Murray: So I do think it's not just orca, but I think in general, the whole of the quantum of market is shifting to be much more specific and needing to answer much more specific questions.
• 47:00
  Richard Murray: Industry to customers on what exact fine I get.
• 47:03
  Richard Murray: They can do lots of stuff, but what specifically can it do?
• 47:05
  Richard Murray: Yeah.
• 47:05
  Richard Murray: And, and when and how much.
• 47:07
  Richard Murray: Yeah.
• 47:08
  Richard Murray: And so I, I think that in general the quantum industry is about, is you going through a pretty exciting shift.
• 47:14
  Richard Murray: And obviously there are lots of publicly traded quantum stocks at the moment and they're having to answer this question as well.
• 47:20
  Richard Murray: And I, so I think it's a sort of exciting transition from it as a exciting new era of science to Well great.
• 47:27
  Richard Murray: I get that.
• 47:27
  Richard Murray: But what are the products,
• 47:29
  Chris Reichhelm: and one question on that.
• 47:30
  Chris Reichhelm: You mentioned Goldman and BP starting to, uh, you know, hire their own quantum scientists and engineers.
• 47:36
  Chris Reichhelm: Are they doing it to enable further, enable their AI capability?
• 47:43
  Chris Reichhelm: Or are they doing it kind of as a standalone operation to see where that goes?
• 47:49
  Richard Murray: Um, hard to tell.
• 47:51
  Richard Murray: I mean, I think, um, historically they've done it as a general capability that they knew they needed to build up.
• 47:57
  Richard Murray: Um.
• 47:58
  Richard Murray: Mostly looking in general at Quantum and what can it do, I think now almost.
• 48:03
  Richard Murray: And so, and I've worked in the world of sort of industry, uh, innovation and, you know, research divisions that are quite separate from the main body of the business.
• 48:12
  Richard Murray: I think now that those, those quantum teams that have been set up maybe on the peripheries of large companies doing their innovation piece, but, you know, let us know and we'll work out where it applies to the business.
• 48:23
  Richard Murray: I think that is starting to shift to be research teams that are much more centrally located within the business.
• 48:31
  Richard Murray: Um, so I think historically it's been less linked to say AI or the specific applications, but I think that's changed.
• 48:38
  Richard Murray: Yeah.
• 48:39
  Richard Murray: Um, actually one, one great example is I spoke to some, someone who I won't name, but it's a great quote that they said that said that they, in, within their team, they labeled, um, quantum Advantage to mean when a big company invests in quantum computing straight off their balance sheet.
• 48:56
  Richard Murray: They, because they're able to define, you know, the business return that can be derived, that, that is the definition of quantum Advantage.
• 49:03
  Richard Murray: Not some more academic label.
• 49:05
  Richard Murray: But, uh, you know, there's a lot, lot of debate in the industry about what Quantum Advantage means, and this person apparently stepped forward and said, well, it means someone investing in a off their balance sheet.
• 49:13
  Richard Murray: Um, of course, we all know what that means.
• 49:16
  Richard Murray: Absolutely.
• 49:16
  Richard Murray: And how significant that would be.
• 49:17
  Chris Reichhelm: Absolutely.
• 49:18
  Chris Reichhelm: Absolutely.
• 49:19
  Chris Reichhelm: That's very good.
• 49:20
  Chris Reichhelm: Um, let me, uh, and let's talk about something else.
• 49:24
  Chris Reichhelm: Um, the journey you make as an organization requires obviously, certain skills, experience, and talent, and which, and those skills, experience, and talents change, uh, over time.
• 49:40
  Chris Reichhelm: Um, how, and yet talent is, you know, such talent, the type of talent we're talking about, it's at a premium, uh, it's scarce and what is available is at a premium.
• 49:50
  Chris Reichhelm: So how do you think about.
• 49:55
  Chris Reichhelm: Team, team, uh, that recruitment of skills, experience, and talent, the way that's organized, um, what's your, you know, what works for you guys?
• 50:07
  Richard Murray: Yeah, I mean, this is really at the heart of, I think being successful in Quantum is, is the team.
• 50:14
  Richard Murray: Um, the, the funny thing about Quantum, which I think a lot of people miss from the outside, is because of the fact that quantum has never been an industry, there has never been an existing industry for the development of quantum computers.
• 50:27
  Richard Murray: That means that almost certainly you cannot recruit people with a lot of industry experience in, in Quantum because it hasn't existed before.
• 50:37
  Richard Murray: So it is not the so easiest to say, oh, I'm gonna go for the top 10 quantum companies and pick up someone with 10 years experience working on a quantum computer.
• 50:45
  Richard Murray: Import you, I mean, you might find one person, but in, in general, that's not a talent sourcing.
• 50:52
  Richard Murray: Scheme strategy that works.
• 50:54
  Richard Murray: So that then means that either you go for the people who know a lot about quantum computing, but typically they'll be coming from a more research background.
• 51:03
  Richard Murray: So they'll have to learn and adapt to learn how to operate in a more commercial environment.
• 51:08
  Richard Murray: Or you have to go for people with a lot of industry experience, but then find the people who are capable enough of translating that knowledge into quantum.
• 51:19
  Richard Murray: And the question of, well, those people, how, how much, how can, how deep can they go into quantum if they, if they haven't studied it before?
• 51:27
  Richard Murray: And, and maybe there are outliers, people who've studied quantum before, but, you know, and then steps into industry bit, bit like myself.
• 51:34
  Richard Murray: But there is a, a talent gap, uh, and a pretty big one, or people who know quantum, who also know how a company operates, especially at the more senior levels.
• 51:44
  Richard Murray: Really, really challenging.
• 51:46
  Richard Murray: That gets only even more hard for more product related skills.
• 51:50
  Richard Murray: The more, you know, when we really look at the frontiers of product development or quantum computers, you're really talking about, you know, like probably a Venn diagram of one, well, a very small number of people.
• 52:04
  Richard Murray: So I think, um, the answer is twofold, really finding great people who are versatile enough, um, dare I say, it's sort of creative and intelligent enough, but versatile enough to take on a new role that's different from what they did before.
• 52:21
  Richard Murray: Step into a new way of doing it.
• 52:23
  Richard Murray: Embracing new technology or, or, or, or embrace or if they're coming from research background, a bit of a weird, much more, uh, constrained way of, of working than they've been used to.
• 52:34
  Richard Murray: So you've gotta find the right sort of feedstock, if you will.
• 52:37
  Richard Murray: And then in inside the company, I think there's a lot of, you know, um, I think training is too shallow a word, but, um.
• 52:47
  Richard Murray: Up all these words aren't significant enough to really describe the job of really working hard to make, make sure that your team members understand culturally what you're trying to do.
• 52:57
  Richard Murray: That's maybe the hardest piece.
• 52:58
  Richard Murray: You know, making a researcher understand the world of, uh, products and revenue is, is, is a big, is a big old cultural shift and a big, um, thing I think sometimes for them to understand and also just in terms of the, the actual skills that they have to be able to step into this and continue to evolve and develop as people, um, into this completely different world of working.
• 53:20
  Richard Murray: Yeah.
• 53:20
  Richard Murray: Often.
• 53:22
  Richard Murray: Yeah.
• 53:22
  Richard Murray: Um, but, but it's, it's hard.
• 53:23
  Richard Murray: It's really hard.
• 53:26
  Chris Reichhelm: Your, your point around.
• 53:29
  Chris Reichhelm: So I wanna touch on a few things you mentioned there, but your, uh, your comment that training or development is too light a word.
• 53:40
  Chris Reichhelm: I forget how you phrased it, but you, it, it's too, almost pejorative a term.
• 53:45
  Chris Reichhelm: To use for what has to go on it.
• 53:47
  Chris Reichhelm: It's funny how over the, I guess last few decades we've come to see training and development has almost a weak, weak terms for what has to happen.
• 54:01
  Chris Reichhelm: Hmm.
• 54:02
  Chris Reichhelm: And when the whole point of, especially of these kinds of environments, for anyone, quite frankly, for anyone, when you're operating at the limit of what's possible scientifically and technically so on, uh, um, is, is to develop.
• 54:19
  Chris Reichhelm: We have to develop, we're always developing, but we see that as almost a, some kind of, you know, it's got weakness around the term and around the connotation.
• 54:28
  Chris Reichhelm: Mm-hmm.
• 54:28
  Chris Reichhelm: Especially in an area like quantum.
• 54:30
  Chris Reichhelm: And you outlined it beautifully there in just the, you know, the conflict.
• 54:33
  Chris Reichhelm: There aren't a lot of people who've scaled quantum compute.
• 54:36
  Chris Reichhelm: No one has done it.
• 54:37
  Chris Reichhelm: Okay.
• 54:37
  Chris Reichhelm: Yeah.
• 54:38
  Chris Reichhelm: And, uh, inside big companies or small companies, everyone's doing it for the first time.
• 54:43
  Chris Reichhelm: If that's your kind of starting point, then what are you going for?
• 54:47
  Chris Reichhelm: Yeah.
• 54:47
  Chris Reichhelm: And uh, and you know, all right, so, okay, we're all doing this for the first time, so then what are the skills we need?
• 54:52
  Chris Reichhelm: Yeah.
• 54:52
  Chris Reichhelm: Give us a better chance of being able to do it.
• 54:55
  Chris Reichhelm: And we're all gonna have to learn, we're all gonna have to develop.
• 54:59
  Chris Reichhelm: Um, you know, do, do you think that young scientists, especially coming, you know, with, with outstanding Providence, those have emerged from really first class, uh, institutions, universities, and so on.
• 55:14
  Chris Reichhelm: Do you think they get that?
• 55:16
  Chris Reichhelm: Do you think they get that the journey a It has, it's gonna be tough.
• 55:20
  Chris Reichhelm: It has to be tough.
• 55:22
  Chris Reichhelm: You're gonna feel like, I don't know, you're back in school again because there's so much to learn.
• 55:26
  Chris Reichhelm: Your learning hasn't stopped or peaked because you got a PhD in whatever, from wherever.
• 55:32
  Chris Reichhelm: It's actually, you know, you're gonna carry on this journey and you're gonna feel done again.
• 55:36
  Chris Reichhelm: Do you think they get that,
• 55:37
  Richard Murray: um.
• 55:39
  Richard Murray: I think my honest answer would probably be no.
• 55:42
  Richard Murray: I, I could be more charitable in saying it depends.
• 55:45
  Richard Murray: Um, it depends on a few characteristics, but I think it, unfortunately, I think sort of university teaches you that the physics is the hardest thing and the most important thing and everything else is just easy.
• 55:57
  Richard Murray: And you, you can do it and what's the big deal?
• 55:59
  Richard Murray: I could do that if I really wanted to, but it's not, it's not, it's not something that's really that hard.
• 56:03
  Richard Murray: So I don't really need to try, try very much.
• 56:06
  Richard Murray: Um, I think, I think I important to say that I think there are exceptions.
• 56:10
  Richard Murray: I think that you have to sometimes try and spot the people who are less technically brilliant, who maybe have it.
• 56:19
  Richard Murray: That's not the, the, the most important criteria.
• 56:22
  Richard Murray: It is people who, and I could use a, a bunch of vague words that, um, that we use, which defy detailed description, but things like they're comfortable with ambiguity, um, maybe even more of a stretch.
• 56:35
  Richard Murray: Their personality is one that is.
• 56:39
  Richard Murray: Learning and putting themselves in difficult positions outside their comfort zone in order to learn.
• 56:45
  Richard Murray: I think those types of characteristics, um, to be honest, unfortunately, are sort of sometimes trained outta people as they do a PhD. As a PhD, you, you are supposed to be the expert.
• 56:56
  Richard Murray: You are not supposed to be uncomfortable if you don't know something or explain that you don't know something.
• 57:01
  Richard Murray: There's a lot of, um, sort of maybe arrogance or expectation that you just don't sign up.
• 57:07
  Richard Murray: Don't recognize those things as important.
• 57:09
  Richard Murray: But I think if you can spot people like that, and we've got, you know, some of our best people are ones who, who really step into that.
• 57:16
  Richard Murray: Okay.
• 57:16
  Richard Murray: I, I've seen, and I've heard a bunch of stuff that I don't understand.
• 57:20
  Richard Murray: I'm not gonna dismiss it and just be like, oh, I'm gonna go home and have, but I'm gonna really step into that and really try and understand and pick apart what happened today, why these events played out, why I didn't, you know, why, why there were so many iterations on this proposal that I was trying to write and things like that.
• 57:35
  Richard Murray: Um, in order to.
• 57:37
  Richard Murray: Build themselves into better people.
• 57:40
  Richard Murray: Um, maybe coming back to one of the comments you made earlier, and I think it's important, the, the training bit isn't the hard part.
• 57:46
  Richard Murray: Like we recruit the most intelligent people you can possibly imagine.
• 57:50
  Richard Murray: They get stuff immediately.
• 57:52
  Richard Murray: They need to know, they need to understand why they need to learn that stuff.
• 57:56
  Richard Murray: That that's the harder part, the, the sort of the step into the training as if, if you like the explanation as to why you need to know this.
• 58:05
  Richard Murray: Not because as soon as you, as soon as they know, they need to know it, they can learn it.
• 58:09
  Richard Murray: You know, they can, they can sort of find, you know, they can do a training course like the rest of us, but it's, it's sort of, it's the wrapper around the training, the cultural piece, if you will, that I find even more important to, to explain to them.
• 58:23
  Chris Reichhelm: But is some of that training or development, you know, you are a very commercial organization and we're gonna build products for customers that they can use.
• 58:36
  Chris Reichhelm: So this is less about, again, you know, getting back to the original point, this is less about going for the end goal.
• 58:43
  Chris Reichhelm: Now it's a journey to get there.
• 58:45
  Chris Reichhelm: We may turn off at some point, we may find something even more interesting as we go, but now we're gonna build products for customers that they can use.
• 58:53
  Chris Reichhelm: And so we need you, depending on the role, of course, we need you to get out and get in front of customers, start having conversations and uh, and hear what they have to say.
• 59:05
  Chris Reichhelm: And, uh, and, you know, not take it 100% as as gospel.
• 59:12
  Chris Reichhelm: It never is.
• 59:13
  Chris Reichhelm: You've gotta, you know, thread the needle or in between the lines, however you wanna describe it.
• 59:18
  Chris Reichhelm: But, but there's all this other work out in the market that where, you know, and where, and identifying or kind of figuring out where that market is going, what that customer is gonna need now and likely to need in the future.
• 59:32
  Chris Reichhelm: How are, how what we're doing might align with that, how it could align that, and then getting to that point and being wrong and then coming back and doing it again and being wrong and, you know, until we get it right.
• 59:43
  Chris Reichhelm: Um, that's, that can be very difficult.
• 59:47
  Richard Murray: Yes.
• 59:48
  Richard Murray: Yeah.
• 59:48
  Richard Murray: For,
• 59:48
  Chris Reichhelm: you know, for individuals,
• 59:49
  Richard Murray: you know, for all of us, I, I definitely agree and really the essence of this is really something that's hard to learn.
• 59:56
  Richard Murray: You reading people, reading situations, so for example, your customers saying one thing, they really need this one thing.
• 1:00:04
  Richard Murray: You may might think that they're wrong, but working through that, in a world that's full of ambiguity, there are very few certainties.
• 1:00:12
  Richard Murray: Yeah, there are very few wrong answers, but there are probably answers that are more right than wrong.
• 1:00:17
  Richard Murray: Um, this is a world that's really, I mean, let's be honest, it's really hard to navigate.
• 1:00:22
  Richard Murray: All of us spend our whole careers learning how to do this, how to read situations, how to adapt to c to accommodate certain scenarios and, and feedback and things like this.
• 1:00:31
  Richard Murray: And I think what's bring, what's the most challenging thing about quantum computing is it's, it's so much more, it has to be so much more accelerated because, you know, the need for those people is now, you can't wait for people to sort of spend their whole career to figure out how to, how to read a customer signal or how to interpret something that someone said, which is a bit ambiguous.
• 1:00:52
  Richard Murray: Yeah.
• 1:00:52
  Richard Murray: Um, yeah.
• 1:00:53
  Chris Reichhelm: So what are the key characteristics or features?
• 1:00:55
  Chris Reichhelm: Can you describe, you know, what are the key characteristics or features of talent when you're, you know, you know, getting away from the technical for a minute, you know, because I appreciate that could change depending on the role, but can, you know, what does an arc of computing person look like?
• 1:01:09
  Richard Murray: Um, we often, by the way, I'll say a bunch of stuff, which my recruitment team always push back.
• 1:01:15
  Richard Murray: We can't say that it's too ambiguous.
• 1:01:16
  Richard Murray: It's like, what do you really mean by this?
• 1:01:18
  Richard Murray: But, uh, we quite often use the phrase comfortable with ambiguity.
• 1:01:22
  Richard Murray: Uh, as, and again, I'm always, there's always pushback on what, how do you test for that?
• 1:01:26
  Richard Murray: What does that really mean?
• 1:01:27
  Richard Murray: But comfortable with ambiguity.
• 1:01:30
  Richard Murray: Also, I think, related to that very strong sort of problem solving skills.
• 1:01:35
  Richard Murray: So you notice the point of ambiguity, you know, experience, track, record of finding maybe unique, maybe resource constrained solutions to those track changing problems.
• 1:01:48
  Richard Murray: Um, our, and, and, and I mean, and then slightly different from that, within a different set of sales, sort of commercial awareness.
• 1:01:58
  Richard Murray: I mean, that, that actually comes third really, if you can have those two things first.
• 1:02:02
  Richard Murray: And then also commercial awareness.
• 1:02:04
  Richard Murray: You know, it's rare, but, you know, we'd be doing really well, but some sort of person who at least is willing or able to understand, um, the, the commercial environment, the commercial side of, for example, what a customer will be looking to go through in order to say purchase one of our systems.
• 1:02:21
  Richard Murray: Yeah, so I'd say those three probably in, yeah, various different orders, probably summarize.
• 1:02:27
  Richard Murray: Outside of just, we need a quantum PhD or someone who understands quantum optics.
• 1:02:31
  Richard Murray: Yeah.
• 1:02:31
  Richard Murray: Some of the things that we, we strive to, to get from, from our team members, um, in, in what we do.
• 1:02:39
  Richard Murray: Yeah.
• 1:02:39
  Chris Reichhelm: Yeah.
• 1:02:41
  Chris Reichhelm: Richard, this has been hugely enjoyable.
• 1:02:44
  Chris Reichhelm: Um, thank you so much for, for spending this hour with, with me and us.
• 1:02:50
  Chris Reichhelm: Um, I think the, where we, where we draw that line, uh, when we're on that lab to market journey, trying to get to that ultimate destination is, is, uh, is really important.
• 1:03:04
  Chris Reichhelm: Resources.
• 1:03:05
  Chris Reichhelm: What I've taken away is this idea that resources are going to, uh, have a huge impact on how much, on our adherence to that goal.
• 1:03:15
  Chris Reichhelm: Uh, and that's a good thing.
• 1:03:17
  Chris Reichhelm: Um, you know, these constraints are a good thing, uh, and they, and they can lead us to other destinations.
• 1:03:23
  Chris Reichhelm: I imagine that, you know, the, uh.
• 1:03:27
  Chris Reichhelm: Communication with the different stakeholders along that journey.
• 1:03:31
  Chris Reichhelm: The one investor thinks she's signing up for, because I thought we were building this.
• 1:03:35
  Chris Reichhelm: Oh.
• 1:03:35
  Chris Reichhelm: And now it looks like we're turning off and we're gonna to do this, but hold on, my portfolio's gonna be outta whack 'cause the valuation changes and, and so on.
• 1:03:44
  Chris Reichhelm: You know, those kinds of things.
• 1:03:46
  Chris Reichhelm: Um, I imagine that kind of communication is really important as part of that journey, but that's, that is the nature of a deep tech journey.
• 1:03:55
  Chris Reichhelm: I think.
• 1:03:56
  Richard Murray: I totally agree.
• 1:03:57
  Richard Murray: And by the way, it's what makes this whole journey that much more fascinating.
• 1:04:01
  Richard Murray: That's it.
• 1:04:01
  Richard Murray: I mean, personally, I get a real kick out trying to solve these problems that we talked about today.
• 1:04:06
  Richard Murray: Yeah.
• 1:04:06
  Richard Murray: It's not a technical problem.
• 1:04:07
  Richard Murray: It's a human problem and crack it and motivate your teams to work in the right way.
• 1:04:11
  Richard Murray: And, and that's the answer to being well part, a big part of the answer to be successful.
• 1:04:15
  Richard Murray: Yeah.
• 1:04:17
  Chris Reichhelm: Richard, thank you so much again.
• 1:04:18
  Chris Reichhelm: Great talking to you.
• 1:04:19
  Richard Murray: Thank you and you.
• 1:04:20
  Chris Reichhelm: You've been listening to the Lab to Market Leadership Podcast, brought to you by Deep Tech Leaders.
• 1:04:26
  Chris Reichhelm: This podcast has been produced by bowhouse.
• 1:04:29
  Chris Reichhelm: You can find out more about us on LinkedIn, Spotify, apple, or wherever you get your podcasts.
• 1:04:36
  Chris Reichhelm: Casts.