Kevin Rowney 0:30
Welcome back to the new quantum era. Today it's an interview with Dana Anderson, he has a really interesting perspective, this is going to be on the sub segment of the overall quantum ecosystem devoted to sensing technologies. And I think there's just a lot of room for optimism that this segment of the ecosystem has got, I think some wild new possibilities.
Sebastian Hassinger 0:52
Yeah, Kevin. Absolutely. I've been looking forward to this conversation is Dr. Anderson has been involved in cold and ultra cold atom systems from from really From their inception in his work at JILA, in Colorado, where the first sort of scientific breakthroughs happened for cooling atoms down using lasers, and then developing the applications for that that type of device. It's really, really interesting and neutral atoms have become a very hot topic in quantum computing over the last year or two, ColdQuanta was the name of the company that Dr. Anderson founded, has been joined by QuEra by Pasqal as having neutral atom based devices and there's more companies that are developing them as well. So it'll be really interesting opportunity to learn more about the science and the technology underlying that modality.
Kevin Rowney 1:46
Good stuff let's dive in.
Hello, welcome back. And today we have the privilege of an interview together with Dr. Dana Anderson, who is co founder and CSO of infleqtion formerly known as ColdQuanta. He currently serves as this chief strategy officer, and is also fellow at JILA affiliated with NIST as a professor in the department of physics and electrical and computer engineering at the University of Colorado. And since 1993, Dana has been extensively involved in guiding and manipulating that the field of cold and ultra cold atoms. Welcome, Dana.
Dana Anderson 3:00
Thank you, Kevin.
Sebastian Hassinger 3:02
Yeah, thank you very much, Dana. We're really happy to have you on I think, you know, we've been really interested in the rise of of neutral atom platforms as a as a modality new modality of qubits. This seemingly sort of came out of nowhere, even though called quanta was founded way back in 2015. And you've been involved in the development of the science and the technology right from the beginning. Can you sort of tell us how you? Well, first of all, how you know, how you sort of found yourself in that realm of physics itself, and how your path to Quantum Information Science generally?
Dana Anderson 3:37
Absolutely Well, I was trained in quantum optics specifically, which is about lasers and optical information processing in the quantum domain many years ago. And I'm not applied because I'd like to do useful things. So as a professor of the University of Colorado, in the JILA Institute, my colleagues, were focused on atomic physics, and in fact, one Nobel Prize for what's called the Bose Einstein condensate demonstration, which at the time was acknowledged as the atom analog of the laser. And being an applied physicist, it was very clear to me that this stuff was going to be useful. We wanted to get it out into the world. And so we got a fair amount of funding from DARPA, and from various other DOD and NSF agencies and NSF to develop the technology, and we wanted to get out to the world. And that's what gave rise to the founding of then called quanto, which was actually in 2007. Sorry, so you were working there with my colleagues, Eric and Carl, one couldn't help but sort of be pulled in to using neutral atoms on the one hand with quantum technology, which was what I was trained in. Excellent.
Sebastian Hassinger 4:59
That's really good. thought, and so a Bose Einstein condensate. That's a form of matter that occurs at these very, very low temperatures. Can you explain, first of all, how lasers make atoms cold? And secondly, what happens at that, you know, what are the properties of that that form matter?
Dana Anderson 5:18
Yeah, very quickly, lasers make things cold in the following way, you know, possibly that atoms like to absorb a very specific color of light. And so roughly speaking, you can think of it this way, temperature is motion, vibration, and so on. And if you shine lasers such that the atom absorbs it, they pick up the recoil of the photons, and they slow down, just like throwing maybe ping pong balls at cars, ping pong balls don't weigh a lot. But if you throw enough and fast enough, you can slow down a car. And so the laser beam shine from all directions can actually sensually stop atoms, once you stop them, that means they're cold. Once they're cold and close enough, together, they form all together a single quantum state, all the atoms become as one, almost as one, but one quantum state. So there's a whole new world at those low temperatures. And the beauty is only the lasers are used to cool the atoms, and only the atoms are cold. So everything else can be at room temperature. While the atoms are in a quantum state. And that's why as we discuss probably a little later, today, this technology is so useful for so many technologies, not just the quantum computer, but everything from clocks to quantum networks to navigation systems.
Sebastian Hassinger 6:54
I was gonna say that I mean, the the involvement of JILA immediately brings to mind you know, the the atomic clock work and other work that's happened in that institute and out of NIST, and University of Colorado Boulder. You know, do you think that the what we learned building those atomic clocks, building those other quantum devices has sort of accelerated the development and the the maturation of the technologies behind the neutral atom arrays?
Speaker 4 7:26
Oh, absolutely. You have to appreciate that the clock in the JILA building is a million times more precise than the world timekeeper, the one that keeps us time and everywhere else. And so that factor of a million can be, you know, utilized in other technologies. Everything that Infleqtion does involves lasers, and atoms, shooting laser beams at atoms, to make them behave in useful ways. Clocks on the one hand, quantum computing, another.
Kevin Rowney 8:05
so fascinating. And so much of our audience, I think, is already familiar with many of the other publicized architectures in quantum computing, I mean, transmons and superconductor architectures and so forth. How do we describe at a high level to our audience the, I don't know, advantages and disadvantages and contrast between the two separate architectures.
Dana Anderson 8:27
Very quickly, of course, you're you're speaking to an advocate of one modality and each of us are in favor. Okay. So
Kevin Rowney 8:34
it's a fun debate. Yeah, there's
Dana Anderson 8:37
the advertisement for cold atoms, what we what we'd like to convey is that, first, there's no fab. Every atom is a qubit. And every atom is just like every other atom. And they're as perfect as can be to begin with a, b, they are neutral. And we can use laser beams to trap and hold atoms and address them. Everything is done with lasers. And we can have a whole lot of atoms, say 10,000, made of a square of 100 by 100, is not much larger than the diameter of your hair. The Truth in Lending Disclosure is you have to get very good control over these atoms, and beautiful, you know, just exquisite control. But there are many other aspects of neutral atoms that make them very attractive for us. And then finally, if you turn the chapter, you know, we haven't finished reading the what, you know why we like neutral atoms for computing, but turn the pages. We today have ultra cold atom systems operating on the International Space Station. That's how practical the technology is. It's hard to imagine the cryostat the liquid helium, the big, the big chandelier The air being launched on a rocket. So on the one hand, are the arguments that are very fundamental and core and that we'll fight over. Nobody argues about getting cold out in space. And therefore we can operate at the edge and tie together compute with sensors and so on, or deploy them in data centers, helicopters, you know, any application arena, you're like, it's reasonable to talk about putting with cold atoms, they're quite apart from the discussion about computation.
Kevin Rowney 10:33
Yes, I so interested in quantum edge computing. That's
Sebastian Hassinger 10:37
cool. You've talked about sort of the end, you've done quite a bit of even recent work, the robustness of that, that lattice the the atoms held that lattice is, I think you said something like 1000s of of G compared to gravitational forces, what holds that atom in that optical lattice? Is that right?
Dana Anderson 10:56
Yeah, we slow them down the force of those ping pong balls against the car, or the of the light against the atoms, that's the 1000 G's in the lattice, you can tune it anywhere from 10s to hundreds of G's, where g is the force due to gravity. What that means is that if you want to think about putting this technology on a vehicle, an autonomous car, a UAV, that the nasty environment, the vibrations and so on, can be addressed, because we're holding on the atoms so tightly, and you think of lasers, and atoms as being kind of ethereal. But think of them as bricks and mortar. They're just held there, like against all things in harsh environments, very protectively. So that's why we do that.
Sebastian Hassinger 11:51
That's really interesting. I love the broad applicability of the technology. Because if you think about the earliest days of the electronics industry, before it became the information technology industry, right, there were, it wasn't the focus wasn't necessarily on universal computing, it was on these purpose built devices that guidance computers and other single application electronic devices. And we learned so much about fabrication and engineering, and all the reading of maintaining reliability in a difficult environment. And all of that fed into the birth of IT itself, the microprocessor and universal computing at scale.
Kevin Rowney 12:31
That's good, Sebastian. I mean, you're right, because it's control systems for likely to very rigorous environments, right? Yeah, it was the early dawn of the transistor, right?
Speaker 4 12:38
Indeed, by the way, and, roughly speaking, the way to think about everything that Infeqltion does, can boil down to atomtronics, where atomtronics, in fact, has perfect parallels to electronics, with an additional richness that the atoms provide because they have internal state. But otherwise, in terms of currents, and voltages and waves. It's all the same, but instead of wires, you have light beams, to guide the atoms and to control the atoms and so on. So atom tronics is is how the company refers to its platform, as hold. And that's the way you can think we hope of the evolution in the same way that happened to electronics, you know, in the late 40s, on up to today.
Sebastian Hassinger 13:30
Right? That's really interesting. And so do you think the, the, I mean, you've sort of shrunk a lot of the components on the on the atom side of things. Lasers are sort of still like the the optical table and optical devices are still fairly bulky. Do you think that there'll be a similar sort of curve like progression curve, where the components on the optical side are going to continue to shrink?
Dana Anderson 13:57
Yeah, the existence proof. So first of all, you have exactly right. If we were on video, your audience would see a little package like this that shows how small the the technology is for holding the atoms.
Sebastian Hassinger 14:14
It's about smartphone size.
Speaker 4 14:16
Yes, exactly. Right. So So you're right, the challenge is in the lasers, and the existence proof is telecom where JDS Uniphase Other companies well before them, but JDS Uniphase may be the best known. Put, became powerful, by strongly advancing the integration of the photonic technology needed to drive optical telecom. The same thing has to happen here, and very much Infleqtion leads on that forefront and you'll see us push more and more are on what we refer to as quantum photonic meaning specifically, photonics designed to enable the quantum systems based on atoms.
Sebastian Hassinger 15:11
Interesting. And last thing on the sensing side of things is I thought this is really fascinating to you're using machine learning to train an interferometer, right. So you've got atoms in an optical lattice. Oh, no, sorry. Sorry, you tell, because I forget what the terminology was?
Speaker 4 15:31
Well, actually, you said it beautifully. You put atoms in an optical lattice, what is that an optical lattice is formed when you take two laser beams, for example, going in opposite directions, they create a perfect periodicity of light and dark regions, the atoms like to be in the bright regions, then you can take altogether three pairs of those and make a sugar cube of atoms, for example. And the it's it's absolutely gorgeous, absolutely beautiful application of artificial intelligence techniques and machine learning. And you are right to say, we learn how to shake that lattice that sugar cube to make an interferometer to make an inertial inertial sensor to make a guidance system. Or you can also use machine learning not specifically to make an interferometer, which is the way we experts think about it, but instead make a very good inertial guidance system. So so the the combination of machine learning, sensing the neutral atoms and the lasers, is really an incredible marriage, which has a lot of headroom, and a lot of green field for advancing not just the technology for inertial sensing, which is my bread and butter. But many other applications, addressing problems like climate change, which is one of the things we are focused on medical applications, and so on. So we see this as a tremendous forefront for the company.
Sebastian Hassinger 17:17
I was going say the first thing I thought of I mean, other than just the wow factor to begin with, about that machine learning trained model was, I wonder if that's going to give you an advantage over qubit control, right? If you add the state of the atoms into your, your, the machine learning training loop, then potentially things like error mitigation, or control in general, error correction and fault tolerance are something that you can actually train machine learning models to help improve on on the on the array.
Dana Anderson 17:51
That's That's an excellent point, I'm going to make a slight distinction. The moment you say qubits, it's almost always used to refer to quantum computing compute, like, whereas the atoms can do all kinds of things. And then and then you have it also absolutely right. Actually, machine learning techniques for getting qubits to behave is already used widely.
Kevin Rowney 18:18
Oh, we've heard that seep across the podcast, it really
Dana Anderson 18:22
it's what's under the hood, everybody, whether it's superconducting or quantum dots, or what have you, tries to get their system to behave better. with machine learning, it's very has a very directed purpose. The machine learning that we do in the sensing and the signal processing, we call it often is a very high level, I want a good accelerometer, a good RF receiver, and it can tune you know, to signals of interest that, you know, so it's sort of the outward application that the machine learning is used rather than inward of getting the system to behave itself like you wanted it to. But very, very good points and absolutely on spot.
Sebastian Hassinger 19:10
And so, when we are talking about quantum computing, what do you add to like from from Albert to Hilbert, what's the what's the added components that turn it into a computing platform? And I should say Albert is the Bose Einstein condensate device and Hilbert is your computing device right?
Speaker 4 19:31
algorithm we refer to the in several different ways and by the way, nowadays, a new name Oraqle with a cue
Sebastian Hassinger 19:41
that's required by law.
Dana Anderson 19:44
We're trying to bring down Scrabble.
Sebastian Hassinger 19:49
I'm worried that we're going to run out on a global scale of queues. It's not an accessible it's.
Dana Anderson 19:55
So they are two complementary platforms you should think of Albert/Oraqle, not as a BEC machine, but an atom tronics platform where you can develop think of it as the FPGA field programmable board from Xilinx, right that you get you want to prototype, you know does certain things you want to prototype your application. That's the way to think of Oraqle, it gives you the opportunity to simulate and solve sensing problems and signal processing. It's an analog, it's basically analog system. And it has many, many atoms, you know, 10s, of 1000s is a typical number, that Hilbert our quantum computer, by contrast, has a very specific number, each atom is very precisely addressed and controlled as an individual qubit. So they're there in they operate in completely different complementary spaces. In the old days, they used to be something called analog computing, for example, but this is Albert/Oraqle's much more general and makes the development of sensors and signal processing accessible. Just like when IBM put their computer on the cloud, Albert/Oraqle makes quantum sensing and signal processing accessible to the broader public.
Sebastian Hassinger 21:25
So it for the purposes of prototyping applications, is that right? So you're sort of you're using the ensemble in the in the Bose Einstein condensate as a proxy for for a single, or I mean, or for a smaller group of, of atoms, or,
Dana Anderson 21:40
well, typically, in sensing applications, the more atoms the better first, so And second, just in a very different way, the atoms can still be entangled. But you're right, it's ensembles rather than individuals, which have advantages in the context of what is meant for sensing and signal processing. Whereas the quantum computer is meant to be a very precise system that can solve any problem. As per general purpose quantum computing. Albert/Oraqle is meant to be targeted purpose sensing and signal processing.
Kevin Rowney 22:23
So do we do we interpret it correctly that many of the applications from from your venture that are just focused more powerfully on sensing? Or do you feel like you're trying to, I don't know, greet both markets with with equal emphasis
Dana Anderson 22:38
Ah, very good. So here is Dana's view, which is not very distant from the infleqtion view is, think of the internet world, you and starting with the Internet of Things, you have a sensor, right behind the sensor, you always do signal processing, then you send it up to the big boys, you know, the processors. The so we can start at the edge with a sensor. And it's especially important in quantum, to let the signal be quantum as long as possible before you turn it to classical and send it down wires. And or you put some simple quantum computation even behind the signal processing. So do you understand the distinction of there's a sensor, keeping the signal quantum, you can do quantum signal processing, followed possibly by the quantum computation.
Kevin Rowney 23:36
So it sounds like your platform is almost set up for a pairing between an Oraqle based sensor and a Hilbert based computing platform together.
Dana Anderson 23:47
That's correct. And as I said, we can start from the edge and work out, you know, towards the data center, not necessarily targeting, say big data center, like you think of IBM. Q,
Sebastian Hassinger 24:00
that's interesting. So in a sense, I mean, you just said, you know, sort of sensing signal processing, and then potentially some computation. So presumably, in that sort of an application, you could actually I mean, you know, what your use cases very specifically, so you can make, maybe not even a universal quantum computer, but a more narrowly scoped quantum computation for that particular purpose.
Dana Anderson 24:24
And there you have pinned part of our business plan and the difference where you don't, once you move away from having to be general purpose, you know, apply any algorithm, you have a lot more tolerance over certain classes of errors, and so on. And again, machine learning comes in there now to match what you have in your computation to the problem at hand, and targeted purpose is the way to get some power early on out of the quantum computation.
Kevin Rowney 24:54
It's so cool. So I mean, it just for the benefit of us and our audience, I mean, just to help us I feel it about what what kind of new sensing technologies, what kind of new capacities will emerge into the market based on these platforms. I mean, inertial sensing sounds very powerful for guidance control, but it must be much, much more right?
Dana Anderson 25:15
Well, it's essential to have navigation in GPS denied environments , so starting with clocks. And believe it or not also combining the clocks with quantum, a radio frequency sensing. And then finally, the inertial sensing makes the PNT solution position, navigation timekeeping solution. The first thing is on the radar screen will make total sense. For example, you can send up in space, special gravity radiometers, for example, to measure climate change, early versions, classical versions were used to measure the mass change, due to the melting of the polar ice caps, things like that. environmental changes due to wildfires, the first things are terribly practical, but actually very sophisticated, and you keep on adding them. Because you would like to begin to recognize what do you not interested? What do you wait for us to then that's where machine learning comes in. Compressing the data is very crucial, where machine learning comes in. So you have all this quantum data, which can be enormous, absolutely enormous. You know, how do you deal with it to make it digestible if you like on the compute and whether it's quantum or classical computing. And so as they say, the the first applications will be quite understandable and rational just come from the bottom out of what are some of the important problems to solve, solve navigation, GPS denied environment,
Sebastian Hassinger 26:48
you were mentioning, climate change and monitoring the climate, it does make a lot of sense. Now, frankly, just personally, I get the idea of the Cold Atom Lab on the space station now with better context, because what better place to observe the Earth space. And it's such a compelling use case, because if we can't quantify the changes that are going on in the climate, it's going to be very difficult to get a handle on how to slow changes that are dangerous or reverse things are really dangerous. So that's really fascinating is that? What phase is that development? Is that still in sort of experimental? Is there any actual applications you're working on?
Dana Anderson 27:31
Oh, actually, NASA, NASA is supporting a fairly large effort to develop them at NASA missions are very long term. So when we look at 10 years down the line, so the answer is yes. And I also wanted to add in the future, you know, wouldn't it be nice to see the impact of various events on climate change much sooner by having much greater sensitivity? For example, what is the impact of a forest fire, or, you know, a hurricane over there? How does it impact other places, that's just to say that the sensitivity improvement, you can get with quantum can be applied in so many places. Space, we're talking about climate change, but also in medical applications for doing brain scans, for example, you might see as replaced the squids and MRI machines, quantum has been used for a long time, and those that's a little bit aspirational, but we shouldn't be shocked at those things. But that's, that's the real key is that the where we will see quantum, I often say in talks to popular audiences, quantum is going to be in the kitchen, sitting next to the toaster, it's going to be there in 25 or 30 years. It really will. That's where all the technology lasers, you know, are in your kitchen. excetera quantum will be there as well.
Kevin Rowney 29:04
I'm not sure if you recall this bit of history, but I mean, at the onset of the PC era, you'll back in the 80s we so many people were skeptical about the claim that there'll be a computer in every home and they just wildly scoffed at it. But you're you're making a similar bold claim of any of the visionaries back from the or the early onset right of compute technology that there will be home use applications for quantum computing
Dana Anderson 29:27
there will be and it will be boring instead of saying "oo! quantum!" you're gonna say Oh, well you only have a you know, you know 2000 bit computer there, you know.
Kevin Rowney 29:41
So, but did that help help us ground that that I love that daring claim, it helps credit what would be the home application and in two decades for, you know, friends and family for a home QC?
Dana Anderson 29:54
Well, as I said, it'll in the end be probably a little bit chip when it first comes thought would be shiny and exciting. But you don't. You may remember when your CD player finally made it into the house, you probably don't remember when the transistor radio made it house, there are a lot of problems that can be solved with, for example, with quantum computation, or timekeeping, precise timekeeping, for example, secure comm. So it just might be in the back of your WiFi that whatever we call it, then Wi Fi modem, you know, it'd be, it'd be boring, but it'll be very, it'll be very important,
Sebastian Hassinger 30:31
best kitchen scale ever. One one grain of flour at a time.
Dana Anderson 30:40
So when it gets there, it's not going to seem flashy. It'll seem every day, just like, now computers are today.
Sebastian Hassinger 30:48
It's so interesting. You mentioned MRI, you know, Ike Chuang and others used NMR. As the first I think the first sort of algorithmic computation on qubits back in the 90s, until they sort of maxed out the number of qubits, they could actually manage on an NMR rig and said, Okay, that's seven qubits is as much as it would be really interesting to go full circle and ended up with, with much, much more sensitive sensing technology in an MRI machine. And, I mean, I can't imagine if, if you're at a quantum, you know, a quantum scale, potentially, you could be imaging individual cells, you could spot you know, a, a first cancer cell, if you had machine learning sifting through the data,
Dana Anderson 31:34
the field of machine learning, as you as you know, AI and just going gangbusters. The interesting aspects is indeed marrying quantum with machine learning, not just for usual machine learning applications, but even to discover things about quantum systems, because quantum systems become incredibly complicated, very soon. And fortunately, the technology for doing machine learning has become every day and accessible. But I think you'll see an explosion, which you might not quite have expected, of the of the marriage between quantum and machine learning beyond the computation that, you know, the quantum computing advertisement and so on of it. It's, it's really moving unbelievably fast, those two together.
Kevin Rowney 32:26
I mean, it's in many ways, I agree. We're living through miraculous times, right? I mean, the onset of the machine learning algorithm, so powerful, you know, quantum information science is just blossoming in so many ways. But also, it feels like we're living through an era of rapid advances in essentially quantum level engineering, right, have the capacity to build these these systems in brand new ways. Do I have that right?
Dana Anderson 32:48
You, you do. And I think part of the part of what you're seeing is the following. Putting AI for the moment aside, the UK in 2013. Institute in 2014, was the first government to recognize the economic and national security impact of quantum technology. And Europe followed us followed, China was already well invested Australia followed, India's followed. You know, the there, there is a race, to establish leadership. And especially here in the US, having given up leadership in various kinds of manufacturing technology. There's a recognition that quantum is in some sense, the final frontier, the way I articulate it is, quantum physics tells you how well you can form any task given a set of resources. And modern technology today allows us to get to the quantum limit, that how well you can do any task in a broader and broader array of applications. And in the near future, if you're not operating at the quantum limit, you won't be competitive. So it's not just that, as in the let's say, the.com, boom, or the telecom boom, it's not a matter of just technology or just the financial world. It's really a matter of survival of of being at the forefront and not not being behind. And so there's geopolitical aspects to quantum technology and AI, of course, as well as pure technology. And there's an anxiousness to accelerate the technology drive, which is why there's a substantial quantum market, we'll call it where governments, including the US government are putting in billions to secure leadership.
Kevin Rowney 35:06
So these are these are almost nation state level concerns. This is not just economic competitiveness.
Dana Anderson 35:12
Oh, absolutely. Operating in GPS denied environment, timekeeping has civil but also defense applications. is most of these technologies to Absolutely.
Sebastian Hassinger 35:26
It's true. I mean, when you think of it in those terms, as you said, it really is the final frontier. Because once you get to the, the quantum limits of nature, I mean, how are you going to improve your performance beyond that, right? It's really just going to be how well do you use those, those, those, those phenomena that borrow from the way the universe operates? So that's your competition.
Dana Anderson 35:51
That's exactly right. So it'll be a matter of driving down costs, price performance, being able to scale.
Sebastian Hassinger 35:57
I noticed also you I looked at Google Scholar, you have a listing there for Psi as a service, which I thought was amazing.
Dana Anderson 36:08
Yes, quantum as a service? Exactly right.
Sebastian Hassinger 36:11
Quantum mechanics as a service.
Dana Anderson 36:14
Yeah, and Oraqle somewhat does that.
Kevin Rowney 36:16
Yes, right. Now. So we've been really fascinated by this, this thread. Thank you so much on this quantum sensing all these applications really opened our eyes. But we're also curious to hear more about, you know, what kind of quantum computation and qubit level architectures and approaches that your venture supports. I mean, there's a lot of algorithms and play out there, there's a lot of ways that could go help us understand in more detail how the inflection platform and quantum computation could emerge as a set of practical outcomes for for the market.
Dana Anderson 36:51
Okay. First, let me add something on to the previous conversation, which it from a venture point of view, is that not only does Infleqtion push on the computational technology itself, it very much works to enable others to push on theirs, we, we provide tools and components to competitors. Super.tech, you saw, we acquired their software applies across the board. So keeping in mind that Infleqtion as a whole is very, very broad in its approach to enabling this the evolution of quantum technology. So there's the core stuff that we do on the inside. But there's also much broader enabling across the spectrum, as I say, from software to components. And, for example, we have a program about to commence in Japan called moonshot to help them build their quantum computer. So when you ask about our strategy, it's enabling very broadly other people's quantum computing, then you can say, What about our internal quantum computing effort? So the objective here is to target that, let's call it the sweet spot that, you know, moves towards the edge of solving targeted problems with a modest number of qubits, and modest fidelity, modest meaning, not that far away, in some sense, from what the let's say Quantinuum, and IonQ have. But in a smaller qubit form factor addressing very specific problems.
Kevin Rowney 38:48
So helpful. Thank you. And could you be more concrete to me what kind of problems are economically desirable with a medium, the count of the number of qubits you could even run?
Dana Anderson 39:00
So you have such a good question about economics? And frankly, in terms, well, the fact is in compute, you can't realistically talk about good economics, you can on the sensing side, and working your way out to processing. So we are still in a mode where we demonstrate milestones, and achievements that are not actually economically viable yet. But here's the key about Infleqtion's approach. When we make a better quantum computer, we also make a better clock, and we make a better clock, we also make a better quantum computer, because the work is all directed at again, shining, pointing and shooting laser beams onto atoms precisely. And so the economics is going to be led, for example, by the clock technology. First, when you say something economically feasible, where the company can make a product, put it to market make a product of reasonable margin. That's with clock technology. And it may seem so distinct from the computation. But I will mention, by the way, that the first entanglement of gates came out of the clock group at NIST, not the computing group at NIST. So I, you know, I didn't mean to divert your question, but rather just describe the company's tactic on getting quantum into the market, getting finally to be cost effective on the compute side, and it will come, as I say, with things like clocks first, RF detection, second and other in other sensing technologies. And then the quantum signal processing we'll call that analog and very closely connected that will be the quantum computing the targeted, purposed quantum computer. So that's the economic added, it's led by timekeeping, say,
Kevin Rowney 41:16
I see it but believe their advances reinforce each other, right? Yeah.
Dana Anderson 41:20
So when we make a better vacuum for the clock, it happens with the computer and vice versa. And I so again, to your electronics analogy, the equivalent of transistors, circuit boards, and so on, you can use them in different ways to get different functionality, the computer, for sure is the hardest, but it still uses the same parts. Engineering, still has think classes of challenges as even the clock
Sebastian Hassinger 41:49
that strategy strikes me is so smart, because if you can tap into the market demand for a clock or an interferometer, or, as you said it's TNC, I think is right, that's the acronym for TNP position, then that, that starts a flywheel where you can fund the engineering work that you need to make the devices smaller, more reliable, cheaper to manufacture, and the quality goes up over time, and you're getting closer and closer to that threshold where computation becomes economically viable.
Dana Anderson 42:28
And so that's exactly right. And we have been manufacturing those parts. And if you looked at our computer versus our looked at our clock, you'd recognize the relationship between the two, even though the clock is much simpler, that technology is underlying it. There's enormous amount in common. And that's going to be that way for years to come.
Kevin Rowney 42:54
But it's also interesting, just from the standpoint me we're both Silicon Valley people are the two co hosts of this program. And so we watch all these startups, some of which have very abstract value propositions that don't seem to make any sense and have a long time before they make money. Here, though, I mean, this sub segment, right of the quantum computing, you know, ecosystem, your, your, your segment of it feels like it's got immediate, direct commercial application. It's not like we're waiting for 1000 more qubits of error free computation to come online, then we can do Shor's algorithm sometime in the next decade. It's like right now, it appears there's strong applications.
Dana Anderson 43:33
That's exactly right. We still need them, the market and the technology drive down the cost of the lasers, so they can be deployed everywhere. But it's not a question. In fact, that's why there's a whole sector of the community out there, particularly the defense community, that's in a big hurry. Because it's not a question. It's, it's a question of technology and not of you know, getting good qubits to work. And by the way, you know, in ColdQuanta, before Infleqtion was profitable, sold things was profitable for about 10 years, until we took on money to explode. So we had customers
Sebastian Hassinger 44:21
that's got to be unique in the quantum space completely. You made money Yeah. Wait a minute.
Dana Anderson 44:30
Yes. And we have to get back there. But the path to doing it say there's there's no question that what what the past needs to be get those laser integrate the lasers bring the cost down, integrate those systems, because this country actually needs it and even before integration down, fortunately, there's a clock market there's navigation. It's PNT -- Position navigation timekeeping market, already out there. Where we know exactly how well we have to do to interest to people who are in the market for these things.
Sebastian Hassinger 45:07
It's so interesting. And am I right in assuming I mean, super.Tech was founded by Fred Chong, from University of Chicago, it's a really strong software team, my exposure to what they're doing was, they looked like they were focusing on software architectures required for doing distributed computation or or orchestrated across multiple devices is that sort of how they fit into the work that ColdQuanta had already done is sort of, as you were saying, sort of constructing that syncing, processing and then computation pipeline.
Dana Anderson 45:43
Well, they work on several layers of software. But those guys are simply outstanding, they are now undertaking the machine learning and machine learning simulators. When Oraqle goes on the cloud, there'll be associated simulator with it, they have a lot to do with that software. And then there's marrying hardcore Schrodinger equation quantum with and abstracting it. So you know, it's already been abstracted for quantum computation. But in lots of arenas, it has not been abstracted. That's what they're extraordinarily good at. They're just an awesome team is, by the way, is Fred Chong, and Pranav Gokhale, who is the CEO. And just a really, really good team I hadn't appreciated, I've been a hardware and tech guy, how much power there was not just sort of fundamentally enabling, but also enabling economically a company to move faster than its old pair of skis.
Sebastian Hassinger 46:54
Don't give the software guys too much credit, though, it'll go to their head, though. It doesn't work unless you run it on. So keep that in mind. That's great. Yeah. So I mean, I feel like we've gotten a really complete picture of the inflection story, but also just the story of this cold atom technology, this from from the initial sort of discovery through the increasingly sophisticated sort of product isolation of the technology, is there anything sort of that you see on the horizon in the next, you know, 12 to 24 months in terms of, of shrinking of components, or anything else that would sort of be a big leap forward for neutral atoms?
Dana Anderson 47:42
Well, first, I think you'll, you'll see not only the clocks come to early maturation, still kind of big, and market, also something we call quantum radio frequency detection, which is a totally different way of receiving RF signals. And so you should put out your antennas to see this technology actually rise quite quickly. In fact, as we speak, there are demos being made to agencies like DARPA, the army, other DoD agencies, because they are often the first adopters. So you'll see those technologies come to the surface. And then in terms of miniaturization, know that the Department of Defense is investing very heavily in the CHIPS act. And through the CHIPS act has a quantum portion. And while it's a bit early, to say what the outcome, I will be extremely surprised if it isn't a very good portion of those funds and that energy going into what's called photonic integrated circuit development at a very major scale. So look out for that that's coming. If you look at the recent work coming out of UC Santa Barbara in particular, for example, Dan Blumenthal's group, and at NIST, you will see the tremendous progress they are making miniature versions of the kinds of things we need to see commercialized, and to become every day. So exciting.
Sebastian Hassinger 49:33
I can't wait to have quantum device on my countertop. I'll be the first. Well, thank you so much for your time, Dana. We really, really appreciate it. We had a tremendous conversation and really, really learned a lot. So thank you.
Kevin Rowney 49:49
Thank you so much.
Dana Anderson 49:50
It's been a pleasure and all the best to you both.
Kevin Rowney 50:00
Hi. Wow, that was so cool that just, that's just just mind blowing stuff. I mean, it's just it was wonderful to hear his prediction that in one or two more decades, quantum technologies are going to be common within the home. I was just just amazed by that daring prediction.
Sebastian Hassinger 50:54
Very Yeah, absolutely. I mean, I was struck by that. And I mean, I do see the reason for his optimism is very, very broad applicability based on, as he was saying this sort of maturation, the engineering techniques that we have to operate near the quantum limit. And as he said, My My takeaway line was, if you're not operating at the quantum limit, you're not competitive. There it is. Yeah, that's, I mean, you want to know what quantum 2.0 At the quantum revolution is the new quantum era. That's it right there.
Kevin Rowney 51:25
And what was really one of those powerful moments for me was to realize that he's he's pointing directly at it is that a certain sense? These technologies are so strategic, that they're, they're really at the level of nation state level concerns, like absolutely numerous very powerful entities, all competing for supremacy in this area of innovation, just amazing well,
Sebastian Hassinger 51:46
and also that the applications for sensing and signal processing include in the near term starting to get a better sense of how our planet is changing as the climate shifts, that's just so exciting to me, because, you know, you can't fix a problem you can't measure and it's really hard to measure the planet at the moments. Anything, any advances on that front are very welcome. More power to him. Terrific. Thank you for joining us, everybody. We'll be digging deeper into the neutral atom modality and other modalities of qubits and other algorithmic topics in the future. So thanks for joining us. If you like what we're doing, please subscribe and leave a review on Apple podcasts or whatever platform you use for consuming your content and stuff.