Macroscopic Quantum Tunneling with Nobel Laureate John Martinis

Episode overview
John Martinis, Nobel laureate and former head of Google’s quantum hardware effort, joins Sebastian Hassinger on The New Quantum Era to trace the arc of superconducting quantum circuits—from the first demonstrations of macroscopic quantum tunneling in the 1980s to today’s push for wafer-scale, manufacturable qubit processors. The episode weaves together the physics of “synthetic atoms” built from Josephson junctions, the engineering mindset needed to turn them into reliable computers, and what it will take for fabrication to unlock true large-scale quantum systems.

Guest bio
John M. Martinis is a physicist whose experiments on superconducting circuits with John Clarke and Michel Devoret at UC Berkeley established that a macroscopic electrical circuit can exhibit quantum tunneling and discrete energy levels, work recognized by the 2025 Nobel Prize in Physics “for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit.” He went on to lead the superconducting quantum computing effort at Google, where his team demonstrated large-scale, programmable transmon-based processors, and now heads Qolab (also referred to in the episode as CoLab), a startup focused on advanced fabrication and wafer-scale integration of superconducting qubits.

Martinis’s career sits at the intersection of precision instrumentation and systems engineering, drawing on a scientific “family tree” that runs from Cambridge through John Clarke’s group at Berkeley, with strong theoretical influence from Michel Devoret and deep exposure to ion-trap work by Dave Wineland and Chris Monroe at NIST. Today his work emphasizes solving the hardest fabrication and wiring challenges—pursuing high-yield, monolithic, wafer-scale quantum processors that can ultimately host tens of thousands of reproducible qubits on a single 300 mm wafer.

Key topics

• Macroscopic quantum tunneling on a chip: How Clarke, Devoret, and Martinis used a current-biased Josephson junction to show that a macroscopic circuit variable obeys quantum mechanics, with microwave control revealing discrete energy levels and tunneling between states—laying the groundwork for superconducting qubits. The episode connects this early work directly to the Nobel committee’s citation and to today’s use of Josephson circuits as “synthetic atoms” for quantum computing.
• From DC devices to microwave qubits: Why early Josephson devices were treated as low-frequency, DC elements, and how failed experiments pushed Martinis and collaborators to re-engineer their setups with careful microwave filtering, impedance control, and dilution refrigerators—turning noisy circuits into clean, quantized systems suitable for qubits. This shift to microwave control and readout becomes the through-line from macroscopic tunneling experiments to modern transmon qubits and multi-qubit gates.
• Synthetic atoms vs natural atoms: The contrast between macroscopic “synthetic atoms” built from capacitors, inductors, and Josephson junctions and natural atomic systems used in ion-trap and neutral-atom experiments by groups such as Wineland and Monroe at NIST, where single-atom control made the quantum nature more obvious. The conversation highlights how both approaches converged on single-particle control, but with very different technological paths and community cultures.
• Ten-year learning curve for devices: How roughly a decade of experiments on quantum noise, energy levels, and escape rates in superconducting devices built confidence that these circuits were “clean enough” to support serious qubit experiments, just as early demonstrations such as Yasunobu Nakamura’s single-Cooper-pair box showed clear two-level behavior. This foundational work set the stage for the modern era of superconducting quantum computing across academia and industry.
• Surface code and systems thinking: Why Martinis immersed himself in the surface code, co-authoring a widely cited tutorial-style paper “Surface codes: Towards practical large-scale quantum computation” (Austin G. Fowler, Matteo Mariantoni, John M. Martinis, Andrew N. Cleland, Phys. Rev. A 86, 032324, 2012; arXiv:1208.0928), to translate error-correction theory into something experimentalists could build. He describes this as a turning point that reframed his work at UC Santa Barbara and Google around full-system design rather than isolated device physics.
• Fabrication as the new frontier: Martinis argues that the physics of decent transmon-style qubits is now well understood and that the real bottleneck is industrial-grade fabrication and wiring, not inventing ever more qubit variants. His company’s roadmap targets wafer-scale integration—e.g., ~100-qubit test chips scaling toward ~20,000 qubits on a 300 mm wafer—with a focus on yield, junction reproducibility, and integrated escape wiring rather than current approaches that tile many 100-qubit dies into larger systems.
• From lab racks of cables to true integrated circuits: The episode contrasts today’s dilution-refrigerator setups—dominated by bulky wiring and discrete microwave components—with the vision of a highly integrated superconducting “IC” where most of that wiring is brought on-chip. Martinis likens the current state to pre-IC TTL logic full of hand-wired boards and sees monolithic quantum chips as the necessary analog of CMOS integration for classical computing.
• Venture timelines vs physics timelines: A candid discussion of the mismatch between typical three-to-five-year venture capital expectations and the multi-decade arc of foundational technologies like CMOS and, now, quantum computing. Martinis suggests that the most transformative work—such as radically improved junction fabrication—looks slow and uncompetitive in the short term but can yield step-change advantages once it matures.
• Physics vs systems-engineering mindsets: How Martinis’s “instrumentation family tree” and exposure to both American “build first, then understand” and French “analyze first, then build” traditions shaped his approach, and how system engineering often pushes him to challenge ideas that don’t scale. He frames this dual mindset as both a superpower and a source of tension when working in large organizations used to more incremental science-driven projects.
• Collaboration, competition, and pre-competitive science: Reflections on the early years when groups at Berkeley, Saclay, UCSB, NIST, and elsewhere shared results openly, pushing the field forward without cut-throat scooping, before activity moved into more corporate settings around 2010. Martinis emphasizes that many of the hardest scaling problems—especially in materials and fabrication—would benefit from deeper cross-organization collaboration, even as current business constraints limit what can be shared.
  
  

Papers and research discussed

• “Energy-Level Quantization in the Zero-Voltage State of a Current-Biased Josephson Junction” – John M. Martinis, Michel H. Devoret, John Clarke, Physical Review Letters 55, 1543 (1985). First clear observation of quantized energy levels and macroscopic quantum tunneling in a Josephson circuit, forming a core part of the work recognized by the 2025 Nobel Prize in Physics. Link: https://link.aps.org/doi/10.1103/PhysRevLett.55.1543
• “Quantum Mechanics of a Macroscopic Variable: The Phase Difference of a Josephson Junction” – J. Clarke et al., Science 239, 992 (1988). Further development of macroscopic quantum tunneling and wave-packet dynamics in current-biased Josephson junctions, demonstrating that a circuit-scale degree of freedom behaves as a quantum variable. Link (PDF via Cleland group): https://clelandlab.uchicago.edu/pdf/clarke%20science%2088.pdf
• Nobel Prize in Physics 2025 – Scientific Background: “for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit.” Official background document summarizing the Berkeley experiments and their impact on superconducting quantum technologies. Link (PDF): https://www.nobelprize.org/uploads/2025/10/advanced-physicsprize2025.pdf
• “Surface codes: Towards practical large-scale quantum computation” – Austin G. Fowler, Matteo Mariantoni, John M. Martinis, Andrew N. Cleland, Physical Review A 86, 032324 (2012); arXiv:1208.0928. Tutorial-style introduction to the surface code and its resource requirements, written to make error correction accessible to experimentalists and guide hardware design for scalable architectures. Journal link: https://link.aps.org/doi/10.1103/PhysRevA.86.032324  ArXiv link: https://arxiv.org/abs/1208.0928
• “Measuring logical error scaling with the surface code” – representative talks by John Martinis explaining surface-code concepts and experimental data, for example his lecture at the Spring School on Quantum Error Correction (CIQC/UCLA/IPAM). Video link: https://www.youtube.com/watch?v=ed9CfJLto1I

Why it matters
This episode shows how a line of experiments that began as an attempt to see quantum tunneling in a macroscopic circuit has grown into a full-blown technology stack for superconducting quantum computers. By connecting Nobel-recognized physics to today’s engineering bottlenecks, Martinis makes clear that the next breakthroughs will come less from inventing entirely new qubit types and more from mastering the fabrication and integration needed to build reliable, wafer-scale hardware.

For listeners, the conversation is a rare look at how an experimentalist navigates between curiosity-driven physics and hard-nosed system engineering, and why that dual perspective is essential for taking quantum computing from beautiful demos to economically meaningful machines. It also underscores the importance of community culture—sharing techniques, building common error-correction frameworks like the surface code, and tackling pre-competitive problems together—in making quantum technology a durable, global platform.

Episode highlights

• How the 1980s experiments by Clarke, Devoret, and Martinis at UC Berkeley demonstrated macroscopic quantum tunneling and energy quantization in Josephson circuits, ultimately earning the 2025 Nobel Prize in Physics and redefining what counts as a “quantum system.”
• Why a failed early experiment on thermal escape rates forced Martinis to rethink his entire setup and embrace microwave engineering—filtering, impedance control, and dilution refrigerators—as the key to turning Josephson devices into clean, quantized systems.
• The role of Yasunobu Nakamura’s late-1990s single-Cooper-pair box experiments and related work in showing clear two-level behavior in superconducting circuits, marking a transition from fundamental demonstrations to qubit-oriented devices.
• How writing and teaching the surface code—particularly via “Surface codes: Towards practical large-scale quantum computation”—gave Martinis a concrete architectural target and reshaped his work at UC Santa Barbara and Google around building error-corrected systems, not just better individual qubits.
• Why Martinis believes the field now faces a “CMOS moment,” where the limiting factor is no longer basic qubit physics but the ability to fabricate high-yield, reproducible junctions and integrate thousands to tens of thousands of qubits on a single wafer with minimal off-chip wiring.
• A candid discussion of system-engineering thinking inside big organizations, including how viewing every idea through a scalability lens can create friction with more exploratory physics cultures—and how this tension ultimately motivated Martinis to focus his startup squarely on fabrication and integration challenges.
• Reflections on the early, highly collaborative phase of superconducting-qubit research, when groups at Berkeley, Saclay, UCSB, NIST, and elsewhere shared results openly, and how that pre-competitive culture helped the field progress rapidly before corporate efforts necessarily made some work more proprietary.

Creators and Guests

Host

Sebastian Hassinger

Business development #QuantumComputing @AWScloud Opinions mine, he/him.

Producer

Ayann Ettienne McGuire

Guest

John Martinis

John Martinis is a distinguished physicist who was awarded the 2025 Nobel Prize in Physics for his groundbreaking contributions to quantum computing. With a focus on superconducting qubits, Martinis has been at the forefront of developing high-fidelity qubits essential for scalable quantum processors.

What is The New Quantum Era - innovation in quantum computing, science and technology?

Your host, Sebastian Hassinger, interviews brilliant research scientists, software developers, engineers and others actively exploring the possibilities of our new quantum era. We will cover topics in quantum computing, networking and sensing, focusing on hardware, algorithms and general theory. The show aims for accessibility - Sebastian is not a physicist - and we'll try to provide context for the terminology and glimpses at the fascinating history of this new field as it evolves in real time.

Sebastian Hassinger: 00:31

Welcome back to the New Quantum Era. I'm your host, Sebastian Hassinger. I've got a very special guest for this episode. I've interviewed him before. In fact, just a few weeks ago, I repeated our episode from last spring where I talked to him at the APS summit.

Sebastian Hassinger: 00:47

But whereas in that conversation, we were focusing on the work that he's currently undertaking, I wanted to revisit the conversation in light of recent developments. The Nobel Prize for Physics in 2025 was awarded for the discovery of macroscopic quantum mechanical tunneling and energy quantization in an electric circuit. This is work that was undertaken by John Clark, Michel Devoret, and my guest today, John Martinis at UC Berkeley in the eighties. By introducing microwave control and readout of these devices, they laid the foundation for the use of superconducting circuits with Josephson junctions as the basis for superconducting qubits, which have been crucial in the development of quantum computing to date and connected directly to John Martinis more recent work with Google and currently with his startup QoLab. We did encounter a number of technical difficulties with this interview.

Sebastian Hassinger: 01:54

We had some network issues that we had to work through. So I have edited together this episode from all of these bits and pieces. I hope I've done justice to the conversation and to the subject matter. I really thank John for his patience in working through those technical issues with me, and I hope you enjoy our conversation. Alright.

Sebastian Hassinger: 02:53

Thank you so much for joining me, John. Congratulations on the honors of the Nobel Prize this year in physics. We've talked before about your work in superconducting devices and quantum computing. I'm really interested you know, you've talked about how the Nobel has given you an opportunity to sort of have a lot of conversations and and sort of think back over, examine the sort of the history of these devices. What's been sort of the sort of new insight into the the science and the technology of superconducting devices that that sort of come to you over this through this process?

John Martinis: 03:32

Yeah. Well, of course, the basic idea here is that although we normally think of quantum mechanics as the physics of the small, you know, fundamental particles, atoms, molecules, and the like, it's actually a much more general phenomenon. So for example, you build a synthetic device, we call it with a macroscopic variable, such as an electrical circuit. And if you set up the conditions right, you can see quantum mechanics in that too. Right.

John Martinis: 04:05

And what's basically happened is from that experiment and many other experiments, people doing tremendously clever things, we people have been able to build and design and test, you know, many kind of these macroscopic atoms or synthetic atoms, if you like Right. Over the years. And it's turned into a, you know, a very nice field in itself. And clearly, it's interesting because of quantum computing. And I like to think of it as you normally think that your fundamental building blocks are for quantum as a periodic table and electrons or whatever.

John Martinis: 04:44

And now it's also capacitors, inductors, Josephson junction. Actually, massive part you know, small micromachine particles, so that's the mass that is quantum mechanical, center of mass, know, maybe nanomagnets, things like that. So there's a whole new class of quantum physics that we can study.

Sebastian Hassinger: 05:06

Yeah. Yeah. I mean, that's very interesting. As you said, it's sort of that recast them as almost the the Lego bricks of of that we have to build new new devices. You know, you've mentioned it's been a very long road for superconductor.

Sebastian Hassinger: 05:20

Right? I mean, superconducting devices. You know, Josephson hit upon the the tunneling effect in the early sixties. I remember him saying he actually was told in a a a workshop in a public setting by Bardeen that he must be totally wrong.

John Martinis: 05:39

Yes. Exactly. Yes. This was very surprising. And there's a technical reason for that.

John Martinis: 05:46

You know, I've I've written the paper that goes through the technical calculation. I don't know how how new it is, but it's something I wanted to understand deeply. And also to understand it, to know how, you know, we rely on this effect to make our qubits. I wanted to understand kind of more at the microscopic level what's going on. But, yeah, it's kind of subtle what's going on.

John Martinis: 06:11

It's a second order quantum effect. And, you know, I can I can see why someone is as, you know, brilliant as Bardeen Right? Could could

Sebastian Hassinger: 06:21

Right. Right.

John Martinis: 06:22

Could not could not get it get it at the

Sebastian Hassinger: 06:26

wrap his head around it. So the tiling effect was validated experimentally at Bell Labs a few years later. And then Ford Labs, which used to have an industrial r and d division, built the first SQUID, the the superconducting quantum Right. Magnetic Interfusion detector. Right.

Sebastian Hassinger: 06:49

Right. Thank you.

John Martinis: 06:50

Right. Right.

Sebastian Hassinger: 06:50

And and so that was kind of the first inkling that or an attempt to make something useful out of this this new device, this Josephson junction.

John Martinis: 07:02

Well, you know, people people always understood that superconductivity was useful, but then that kind of opened up the vice physics based on the Joyceann junction. And the first thing was to build, you know, these squids. But, you know, if you look at John Clark's thesis project and his work in Cambridge, he was trying to measure very small voltage. And I think in the Nobel lectures, he's gonna go into that, which is really good. Great.

John Martinis: 07:31

Okay. So talk about the early days. Yeah. And yeah. And then that, the people were trying to make classical computers with that in the, you know, late seventies, Yeah.

John Martinis: 07:41

Early

Sebastian Hassinger: 07:43

Interesting.

John Martinis: 07:44

Yeah. And people are still trying to do that today. They have single flux quantum logic, but there was a big effort at IBM at the time when I was a graduate student. So that's late seventies, eighties, and, you know, three people trying to do that.

Sebastian Hassinger: 07:59

I I guess I'm probing at the what you were saying about these, the microscopic quantum tunneling of Josephson Junction is providing a portal into to see into that that you know, the the subatomic level, see the the behavior of the subatomic level at a macroscopic scale. That that really is at the root of the interest of these devices right from the very beginning. Right? And and, I mean, would you say I mean, Tony Leggett, another Nobel Prize winner, sort of famously, like, tried to create, you know, a call for people to to demonstrate microscopic quantum behavior, and that was primarily around these superconducting devices. Right?

John Martinis: 08:38

Yeah. Well, that was the call, and then he understood superconducting devices, and he knew that that would be a way to to test it. And, you know, at the time when I first learned about that, this was actually at a low temperature 16, I think, in Los Angeles. There were, you know, some papers on it. There's a lot of discussion.

John Martinis: 09:00

There was a little bit of data, but it was not very conclusive. People really hadn't understood exactly how to measure it yet and do it properly. But, you know, as a student, it was clear that all these people were thinking about it, it was a great topic and should be investigated carefully. So, you know, even as a student, I recognize this was interesting. And actually, looking back, I don't understand why more people didn't jump on this.

John Martinis: 09:28

But, you know, the technology was a dilution refrigerator and everyone was used to just doing it in helium. There there it was some more advanced technology you had to feel comfortable with.

Sebastian Hassinger: 09:40

So one of the things that I think is really I mean, so we're getting to the point where where, you know, you take a class with with John Clark, I think, as an undergraduate, right, initially, or was that during your master's?

John Martinis: 09:53

No. No. I I was working at another group as an undergraduate, but then I moved to John Clark's group as a graduate student.

Sebastian Hassinger: 10:03

Okay. But you took

John Martinis: 10:04

mean Oh, but I took a class from him as an undergraduate. Yeah. Exactly. And and what what maybe he hasn't been in I talked about previously is that at that time, they were he was doing research where people were looking at quantum noise effects in these superconducting devices.

Sebastian Hassinger: 10:24

It's getting to exactly the thing that I one of the main things I wanted to talk about. So you had been a self described electronics tinkerer. Right. These Josephson junctions, as you you just described, were being seen as DC devices.

John Martinis: 10:40

Yeah. Low frequency device. Low frequency. That's

Sebastian Hassinger: 10:42

right. So so when at what point did did did the thinking shift to microwave signaling control and readout and and resin you know, a resonance interaction?

John Martinis: 10:56

Yeah. Well, you know, we we did an experiment, and we kinda wired up this as we would wire up a no normal Josephson junction experiment, and it completely failed. And, you know, we looked at the thermal escape rate from one to four Kelvin. It just didn't make any sense at all. And we had an idea how to analyze it at the time.

John Martinis: 11:19

And, you know, as soon as it didn't work, it's like, okay. Let's rethink this. And then we sat down and thought about it very carefully. And I think it was pretty soon that we understood we had to filter it, especially at microwave frequencies Mhmm. And that we wanted to make sure that the junction looked into a very well defined microwave impedance, microwave circuit, if you like.

John Martinis: 11:42

Right. And, you know, we did microwave experiments and like to check that all out. So it it it's just we did an experiment, didn't work, and then we just figured out what it had to do. And once we did all the careful things, especially the filtering, but it was really basically everything we have to change, then the data made perfect sense. Okay?

John Martinis: 12:03

And at that point, you know, we had to improve things and think about it, but it was a matter of getting dilution refrigerator working, getting to the low temperature, then making everything compatible. But it just kinda came out, you know, logically from that point.

Sebastian Hassinger: 12:19

Right.

John Martinis: 12:19

But, again, I think, you know, people knew that it was oscillating at the Joseph, you can do whatever, but if that was not it was not the fundamental concept we had to understand.

Sebastian Hassinger: 12:30

Right. Right. Well and, I mean, at least in my mind, that's such a pivotal moment because the development of Joseph junction Joseph junctions into qubits is really a story about developing the the microwave control and readout. And and Right. You know?

Sebastian Hassinger: 12:50

I mean, that's that is still to this day.

John Martinis: 12:52

That's the challenge. People are working on that. And that happened for a long time to figure out the various various tricks and, you know, what materials to use, how to isolate the junction in the proper way. And, you know, we were just doing very rudimentary experiments at that point, but it got us in the right direction. So that

Sebastian Hassinger: 13:14

was that was what was key. And and most importantly, you you experimentally proved that this macroscopic quantum Yes. Effect was happening. Right. Exactly.

Sebastian Hassinger: 13:26

That's the and, you know, so

John Martinis: 13:28

Which, of course, showed that it was interesting effect. Exactly. The other thing that that happened there, and I would say in the next few years, is as we did more and more sophisticated experiments, we could do a clean experiment, know what's going on, you know, get it to work exactly as you would expect from theory. So, you know, we we gained confidence that was a clean enough system to do more and more sophisticated experiments. Each experiment led to the the next experiment.

John Martinis: 14:03

The way I like to think about this is sometimes you do an experiment and you kind of fall over the finish line. And then okay. Then it's gonna be hard to do the next thing. And I would say that's not what happened. You know, every experiment, we learned a little bit more for the technology and the physics, and then we could build on it.

John Martinis: 14:22

And other people did too, of course. Right. So Right. Right. That's what made it so interesting.

Sebastian Hassinger: 14:27

Right. Yeah. And in fact, I think Yasunabu Nakamura in 97 - 96 Right. Demonstrated sort of the the two level system. Right?

Sebastian Hassinger: 14:39

Right. Which is really that's the the next sort of giant step towards this being a component in a quantum computer. You talked about that that sort of thinking about these devices being potentially interesting for quantum computing sort of being in the air before then too. Right?

John Martinis: 14:59

Well, you know, when I when I was writing up my thesis, there was a nice conference at UC Santa Barbara where they, Freidman talked about building a quantum computer. So it was kinda conceptually out there. Right. But some other theory work was developed, other things developed to the point where it's like, okay. Now we have to make qubits and make sure that we know how that work and the like.

John Martinis: 15:26

But, yeah, that that was the the the next, you know, big step is showing that. But I'm gonna say in the ten years that happened, we and Saclay Group and other groups were doing a lot of experiments kinda looking at the energy levels

John Martinis: 15:44

and looking at the quantum physics. And it I'm gonna say it took about ten years to understand quantum devices and learn how to measure and understand so that by the time, you you know, that more of the qubit experiments came along, you know, the technology had developed far enough to to, you know, start doing those experiments properly. But I would say the big moment, you know, that was important experiment very clearly. And then there was US government funding to look at these.

John Martinis: 16:17

And then, you know, people really started looking carefully about it. You know, that's that that's the that's what happens. Okay?

Sebastian Hassinger: 16:24

Yeah. So a certain algorithm caught the attention of the US government and

John Martinis: 16:28

the the

Sebastian Hassinger: 16:29

national intelligence community. But but that that also brings another point in here, which you and I have talked about briefly before, which is is, you know, the the the atomic clockwork, the molecular beam, and then the the laser cooling, all all that work, Dave Wineland and others, Chris Monroe at at NIST.

John Martinis: 16:52

Oh, by the way, they were down the hall from me. Yeah. Right. So

Sebastian Hassinger: 16:57

Right. And but, I mean, in a sense, they had they had a lower bar for proving that this was quantum quantum phenomena that they were dealing with because they were interacting directly with an atom at a atomic scale. And so no one was questioning it. Right? I mean, in a sense

John Martinis: 17:12

So yeah. But what was interesting at the time is we started doing experiments on single atom systems. Right. And up until then, people had always been doing, you know, ensemble measurements. Right.

John Martinis: 17:27

Okay? And you kinda take the average of the signal. But doing single single measurements now we were doing single measurements at the time, but I would say the ion traps and what David Island, especially with the quantum jumps, was really much more spectacular and showed directly showed you that there were some of the strange things going on that you can now understand. Right. And that was definitely a big thing.

John Martinis: 17:55

But, yeah, they were looking at natural atomic systems Right. And then cleanly looking at the the atomic physics in those systems. We we had a we had a while to catch up to that.

Sebastian Hassinger: 18:07

Of course. But you were you this is my point. You're building up this foundational set of of techniques and knowledge and understanding and turning it into a technology, a synthetic atom as a technology that was as you said, you were doing you were interrogating a single quantum system, but in this synthetic setting. So it makes sense that there was a lot of of a lot more work to be done to get to sort of the acceptance that that was that was what you were actually looking at.

John Martinis: 18:38

But but all that said, that was happening also in the atom physics community too. They were learning how to trap and manipulate single atoms or few atoms. The neutral atoms were learning how to condense them. Right. So there was a real renaissance of building these systems.

John Martinis: 18:57

Mhmm. And, you know, we were just building them in totally different ways, of course. But just kind of the ideas were just in the air and everyone was really working and everyone was understanding this was exciting and new and important. Okay? You know, of course, that attracts the best people to that Yeah.

Sebastian Hassinger: 19:18

Yeah. And it's, I mean, important and new and exciting and and in two sort of mirror images of each other. Right? I mean, there's there's there's exploration for in the into the fundamental physics of, the universe that these you know, being able to probe single subatomic, behaviors and, and phenomena is is driving our overall knowledge, our our, you know, our scientific knowledge. But it's also it's so so interesting to me to see this march towards, as you said at the beginning, these these building up of these LEGO blocks, these building blocks of of techniques that can be turned into the the transistors and the capacitors of this quantum sort of con technological context.

Sebastian Hassinger: 20:07

And and that seems to be almost I mean, I wouldn't force you to pick one or the other, but it it feels to me like you're motivated by the the technological advances almost

John Martinis: 20:19

I I enjoy doing all the fundamental physics demonstrations. But, yeah, I've I I was always motivated, you know, once the there was funding for doing that. It was it went to me for the science in the beginning. And then when the you saw the quantum computing application, it's like, well, I wanna build a quantum computer. But to get there, we have to have fundamental understanding, so we have to do the technical advance.

John Martinis: 20:44

And I'm Right. I'm a little bit more on the technical advance side. Mhmm.

Sebastian Hassinger: 20:48

Yeah. But the two are are hand in glove. Right? Yeah. That's so I mean, I find that so fascinating.

Sebastian Hassinger: 20:54

And and in fact, these devices that we're building, you know, the the the other than Shores, the other, you know, sort of in fact, Feynman's point in 1981 is is if you wanna simulate nature, you're going to need a quantum computer to do that efficiently. So we're actually building these machines, which their their clear value in driving our fundamental understanding of the universe is is well understood. Right? If you can you can build a large scale many body quantum system and control it and observe it, we probably are gonna learn a lot, I'm I'm imagining.

John Martinis: 21:28

Well and and that's what was I was able to do in my career. You know, we did with these simple experiments. We learned about quantum devices. We then we started working on qubits and learning how to do that, how to couple them together. A big event for me was writing the paper on how the surface code worked, more translating it so that us mere mortals could understand it.

John Martinis: 21:54

Okay. And then and then with that, it's like, okay. I wanna build a chip to do this. We were doing that at UC Santa Barbara, then moved on to Google. And now, you know, that has progressed to a a a very good, you know, level.

John Martinis: 22:10

And it's like, well, okay. Now we have to learn how to manufacture it really reliably, really well, you know, get rid of some of the problems that we see. It's just another evolution of the technology.

Sebastian Hassinger: 22:23

Absolutely. Absolutely. And and do you so in my mind, this again, going back to the evolution of these these devices from from, you know, simple demonstration of of quantum tunneling to a two level system to, you know, gates and more and more fine grained, you know, readout and air applying error correction, it feels like there's also cross pollination of the techniques that are being developed in all the various modalities. There's, you know, now, you know, bosonic modes within superconducting qubits are being used for error correction, GKP designs, for example. Or

John Martinis: 23:07

Yeah. There's many, many ideas out there now and many things people are working on and perfecting, which is makes it feel very interesting.

Sebastian Hassinger: 23:17

Yes. But I just I guess I wonder if if do you see the increased sort of sophistication of the individual device of the of the qubit and the QPU, the quantum processing unit, as, you know, evolving into it's it's incorporating all the best learnings of all these techniques that come out of these experimental validations of theory or or vice versa? You know?

John Martinis: 23:41

Yeah. I could say that obvious is is happening and people aren't doing that. I might and that's what most physicists are doing, and that's really great. Okay? However, my idea right now is that we pretty much know, you know, how to make decent qubits, and there there for me, there's advantages to the transmogs.

John Martinis: 24:03

People are building other things, and that's great, but I see a real advantage to doing it right now. And what we have to do is just fabricate it better and fabricate in a way to, you know, improve the wiring and other stuff like that. So it it's more for me, a lot of other people are working on the physics and that's great. I love it. But for me, we're trying to solve the hard part, which is the fabrication part.

John Martinis: 24:30

And specifically, you can use kind of academic level fabrication to make these devices, and that's been very powerful. And we just wanna move beyond that.

Sebastian Hassinger: 24:41

Right.

John Martinis: 24:42

Because we think we have to be much more sophisticated to get the yield up and do it right. So it's it's it's and that and that's hard for most people to do that. And and and, also, it's it's scary because you're doing something totally new, and everyone says, well, you know, your your q our qubits aren't better than other qubits. What are you doing? And it's like, well, yeah.

John Martinis: 25:02

It takes a long time to, like, fabricate things properly.

Sebastian Hassinger: 25:06

Yeah.

John Martinis: 25:06

And And in a sense that That's the risk I'm willing to take.

Sebastian Hassinger: 25:10

And and I I I in a sense, that's a that's a putting the weight on the technological advancement side of the balance as opposed to the scientific advancement.

John Martinis: 25:20

Right? Yeah. Well well, okay.

Sebastian Hassinger: 25:22

Yeah. It it systems to transform

John Martinis: 25:24

But look at CMOS.

Sebastian Hassinger: 25:26

Right. Okay. Exactly.

John Martinis: 25:27

Why why are computers so good? I mean, this was kind of fundamental ideas invented, you know, many, many decades ago, but it's only the technological advances, which is which are very hard and involves, you know, very complex science. Yeah. Okay. So I I enjoy this kind of science.

John Martinis: 25:46

Yeah. And and in fact, I I I have a funny way to look at it is that a lot of times, you know, people are inventing new cubits. Like and that's hard and that's it. But you're you're basically putting the design and integrating the Schrodinger equation, which we know works. Okay?

John Martinis: 26:03

You know? And and it it's hard and it's great. Okay? However, when you try to fix materials, that's just really hard. And you have to do trial and error.

John Martinis: 26:13

You have to, you know, use your intuition and the experience of our collaborators and whatever. And it's a much more, let's say, of a discovery mode to do it that way because you have to figure out what the real problems are. And it's hard to do the experiment. So I like that challenge, but mostly it's not, other people aren't doing that. So Right.

John Martinis: 26:37

You know, I I if if if we're successful, I think we're gonna be successful. We're gonna do something really unique.

Sebastian Hassinger: 26:43

Yeah. I mean, in a sense, it's it's seeing how far you can go from, you know, MQT demonstration, two level system demonstration, you know, gates, etcetera, to, like, the Transmon. Like, how far is the Transmon going to end up being that fundamental building block that unlocks the type of scaling that you get with CMOS

John Martinis: 27:03

and transistors. Reasons why I think that's so. But on the other hand, if I can fabricate really well, then let's say we can get the reproducibility of the junctions really good, then all these other qubit modalities now become possible because we can make the qubits better. So, you know, in some sense, I'm trying to help everyone Yeah. You know, by by solving solving the problem.

Sebastian Hassinger: 27:30

I think anyone working on any hard problem in a technology at this early stage is working for everybody because it's the problems are so hard. The motivation has to be sort of very broad in scale, I think. And the and the impact.

John Martinis: 27:46

I understand most people wouldn't wanna try this because it's it's just a lot harder. Okay? It's it's very simple. But, you know, we already know from some experiments that we can do it this way, this potential to improve some of the problems everyone sees. Right.

John Martinis: 28:03

And, you know, that's that's something, you know, that I think would be really great. But, you know but it's actually very hard for our company because everyone compares us to all the other super connected companies. It's like, well, you're way far behind. And it's like, yeah. We're trying to do something totally new.

John Martinis: 28:21

Yeah. We expect to be behind. We're making progress. You know? And, of course, we don't talk about it much.

John Martinis: 28:27

But then if we get this to work, we're gonna just jump ahead in terms of, you know, how reliably we can make things and scale things. So, you know, so it's only too. People don't understand that or, you know, obviously, we don't have the data yet, but that's okay.

Sebastian Hassinger: 28:44

Yeah. No. And I like the idea of, you know, your success in this company's sort of goals will potentially provide fabrication techniques that will have extremely broad impact. Right? As you said, if you can if you can fabricate just some junctions at a at a, you know, another couple of orders of magnitude in terms of quality, overall quality, then that's Yeah.

John Martinis: 29:12

But and then I'm just gonna give you an example of how, like, insane this is for me. Okay. That you know, people, you know, wonder what we're doing, whatever. If you look at a road map of a leading company with superconducting qubits, their road map is a 120 chips, 120 qubit chips, and then you use those dies and piece them together to make bigger and bigger things. So if you wanna make, you know, a million qubit quantum computer and you have to piece, you know, 10 thou you know, 10,000 of those, that's kinda hard.

John Martinis: 29:51

Okay? And, you know you know so, you know, the fact that we're trying to wafer scale up to 20,000 qubits and do it in a way where we can yield you know, problem they're doing a 120, my guess, is that it they just it's hard to yield. Yeah. You know, it's hard to get everything to work. And, you know, that wouldn't surprise me, and that's actually what I've seen from other people with the technology.

John Martinis: 30:15

And it's kind of funny. No one wants to talk about it because it's a negative of what they're doing. You can kinda tease it out from the road maps if they talk about it. And and, you know, it's like, well, we're trying to fix that.

Sebastian Hassinger: 30:28

Yeah. In that sense, what you're what you're trying to work on is the quantum equivalent of the monolithic solution. Right? The the the need for the monolithic

John Martinis: 30:36

solution was So there's driven by all

Sebastian Hassinger: 30:38

of those hand soldered joints, and you get to a, like, a thousand components in electronic device, and it would definitely fail because it'd be one joint that was cracked.

John Martinis: 30:46

Yeah. So so there's a nice way to visualize that. If you look at the superconducting qubit devices now, you know, in the dilution refrigerator and you look at it, there's just a mess of wires, a mess of microwave components, whatever. Okay? Really complicated.

John Martinis: 31:04

We know how to scale to put it onto a chip, you know, this size. And that's a true integrated circuit. Right now, it's a must bunch of wires. It's probably 95, 99% of the volume, and then it's it chip in the center. And in fact, if you go back to the nineteen sixties and you think about TTL and whatever, you have the little transistor gates, and they had all this wiring going around connected up.

John Martinis: 31:31

And and but there, it was actually better because you could separate out the transistor gates with the wires. For quantum computers, it's all in one chip, so you have this escape wiring. So ours is even worse. And now we're we're just trying to fix that problem. So, you know, we wrote a paper.

John Martinis: 31:47

We have a proposal. We wrote a paper. We're trying to do all that. And it's just strange because, you know, some people, like, understand that, but other people, it's like, well, you know, if you can build us something to solve all our problems in six months or a year, then, you know, we're interested in it. And it's like, come on.

John Martinis: 32:06

This is not a trivial

Sebastian Hassinger: 32:09

How many cubits are on that chip you just held up, John?

John Martinis: 32:11

Yeah. Okay. We're gonna start

Sebastian Hassinger: 32:13

with the The you the one you just held up, is that is actually one of the

John Martinis: 32:18

Well, the this would be a 100 cubits. Okay? Okay. But, you know, then you you the 300 millimeter wafer would be 20,000 cubits. Okay?

John Martinis: 32:26

You you know, you you could do that do that, but you have to start small to find out what all the problems are.

Sebastian Hassinger: 32:32

Yeah.

John Martinis: 32:32

Yeah. Yeah. You know, what we're finding is, you know, we're trying to do something radically different. It's not a trivial thing at all to do this. And, you know, people have tried it and other things, it's a small part of it, and they don't work very well.

John Martinis: 32:47

So we we thought about doing it that way. You know, we don't think it's gonna work. We're trying it our way. We actually have a a second new way to do it, which is really, really interesting. We'll talk maybe we'll talk about it in a couple months.

Sebastian Hassinger: 33:01

Cool.

John Martinis: 33:02

And, you know, so, yeah, we're really excited about, you know, the possibilities. It just takes longer.

Sebastian Hassinger: 33:11

Yeah. Yeah. Well, as you said

John Martinis: 33:13

We're we're we're we're being graded against, you know, a twenty, thirty year old technology. Sure.

Sebastian Hassinger: 33:20

And also the expectations of private capital in particular around emerging technologies over the last thirty, forty years are you know, is that three to five year the the the stereotypical three to five year time frame for for getting to some kind of, you know, technology that can build traction in the market. And it this is I keep telling people, we've reset the clock to the late forties or early fifties with quantum computing. And if you were a venture capital in 1950, how would you what would your portfolio investments look like for for investing in classical computing? I wouldn't have a three to five year horizon

John Martinis: 33:58

for you. I I think even the most rudimentary computers had uses in cryptography and ballistic tables or something.

Sebastian Hassinger: 34:08

Yeah.

John Martinis: 34:08

So there were some early use cases because you

Sebastian Hassinger: 34:11

That's true.

John Martinis: 34:12

Competition was by hand or, you know, maybe some simple punch card thing.

Sebastian Hassinger: 34:16

That's right.

John Martinis: 34:17

But yeah. And and, you know, that's that's what you I you know, the frustration for me is that if you're kind of very optimistic and maybe naive about all the real challenges, what are the challenges, then you can sell a simple, exciting, optimistic story Right. That, you know, people will go with. When you talk about, well, things are harder and you have to do all these things and develop this complex technology, well, then you can't make money so fast. And and, you know, that's harder to harder to want to fund.

John Martinis: 34:54

But, you know, our our view is there's a tremendous potential. We're solving the problem so that we can build this thing, but it's very hard for people to do. But, you know, I I'm you know, I I don't wanna sound too negative, but what I would say is my experience for doing this for forty years or so is when people don't understand and this kind of thing is happening, you have a really good idea.

Sebastian Hassinger: 35:18

That's right. That's typically true.

John Martinis: 35:20

Because, you know, it's hard to understand, you know. And and I think I can look into the future, you know, a little bit better than some people can. And and under and and then there's a realist in terms of getting things done.

Sebastian Hassinger: 35:35

I suspect that the the Nobel Prize Committee's decision will add to the you know, to validate the fact that you are on the right track, that you know what you're talking about? Well, yes. We've obviously,

John Martinis: 35:47

we've seen that. I like to put it that we now have a celebrity endorsement for our company. So and celebrity endorsement, in fact, works really well. But the other thing is even though that happens and people congratulate me and whatever, they still revert back to their old thinking. Okay?

John Martinis: 36:07

And and, you know, again, it's just it's just, in my view, validates that we're doing something really transformative. And, know, of course, we have to get it done and the like, but, you know, it's it's that's normal when you try to do something something out of the box. Most people wanna do something incremental. That's right.

Sebastian Hassinger: 36:29

That's right. Well, I mean, it's it's thank you. It's great to me to see how the Nobel Prize sort of validates again sort of your your thinking, your work all the way back from the mid eighties, but you're all also still it validates the fact that you're you're still doubling down on seeing how far you can take this technology. And there's a real chance of being that that transformative, disruptive foundational layer that really brings quantum computing to scale that it needs to have global impact. So thank you very much for your time.

Sebastian Hassinger: 37:04

This has been really, really great. I heard you say you'll have something else to talk about in a couple months, so you'll probably be back on the show at some point soon. So this has been terrific. Is a

John Martinis: 37:14

very interesting technology that exists right now, and it's been fun working with this company to view this application, and it'll be I I can talk a lot about that that. You wanna talk a few more minutes because of the problems we have, I'm open. Your questions are very interesting, so I'm willing to Thank

Sebastian Hassinger: 37:39

you. I mean, you know, it's it's the reason that I really enjoy talking with you is I think that you use you do encompass these two sides. Right? I mean, there's the there's the there's the experimental physicist view of things, and then there's the, systems engineer and and, computer engineering kind of view of things. And that's why I mean, combining those two is is no small feat because they really are two different lenses for deciding what your next experiment's gonna be, deciding what the challenge is that you're gonna try to tackle, deciding on your methodology, deciding on, you know, what what the what the end goal is.

Sebastian Hassinger: 38:23

Right? It's there's curiosity driven scientific experimentation where it's like, let's see what happens when we when we do x. And then there's engineering driven experimentation, which is how do we solve problem y. Right? It's much more practical.

Sebastian Hassinger: 38:38

And I guess what I'm wondering is do you has that has that sort of been something you've been consciously trying to resolve the tension between? Do you feel that that tension between the two, or or has it been sort of just following your natural instincts?

John Martinis: 38:53

So this is a little bit of a natural instinct because I've always thought about instrumentation. And it's actually interesting because I really I I think I've always been this way, but I learned that from John Clark, and John Clark came from Cambridge. And if you look at the, you know, my scientific family tree through John Clark, there's all these people who built instruments that, know, mass spectrometers, you know, cathode ray tube, you know, all these things that led to scientific discoveries. So instrumentation is kind of in my family blood, if you like, through So John I've always thought that way. But this was more physics instrumentation.

John Martinis: 39:41

And what happens is when you wanna build a computer, then it's a different kind of instrumentation that's really systems engineering. Yes. And what happened is I think I was always thinking this way and thinking globally about all the things we had to do. But after I left Google for a variety of reasons, I started thinking about, you know, what were some of the areas of conflict. And a big area, I think, was I was a system engineer.

John Martinis: 40:12

And, you know, people have ideas to do this or that, and it's like, oh, I'm always filtering it through a system engineering. And then I have a, you know, let's say negative things to say because it doesn't make sense from a system engineering point of view. And what happened is at that point, I I read a book and thought about it and really realized that there was a whole discipline involved in this, and I learned some of the principles of that, which completely explained how I thought. But the most important thing is I realized that the system engineering is kind of completely opposite from what you do as a physicist. Right.

John Martinis: 40:51

Okay? And and then it was like, okay. Yeah. I can understand where there's a lot of conflict, you know, coming about from this. So And for example, our company Yeah.

John Martinis: 41:02

What to do the fabrication, this is totally a system engineering thing. We have to fabricate really well. Whereas, I think people are now it's like, okay. You know, we make it a certain way. It works really great and you know, fine.

John Martinis: 41:15

And my statement is, yeah. That's great, but you're in a box canyon. And at some point, you aren't gonna be able to get out of that. And that's a system engineering issue. And, you know, and of course, people, you know, people don't wanna see that.

John Martinis: 41:27

They're optimistic that they'll solve it even though it's been unsolved for, you know, a decade or more. So it's a I would say, I think that gives me, let's say, a superpower if you like. Okay. But it also means it's my kryptonite too because I think As always. But it's fine to think differently.

John Martinis: 41:46

Okay. Especially if you're starting your own company. Okay? This this is this is fine.

Sebastian Hassinger: 41:52

Yeah. You mentioned your physics family tree. At one point when we were talking in the past, you so after Michel Devoret joined you at Cal, he was postdoc when you were a PhD student under John Clarke, and then you finished your PhD, and you did your postdoc at Saclay.

John Martinis: 42:10

That's right. I went

Sebastian Hassinger: 42:11

and joined them. Yeah. And you had talked about how the French approach to physics affected your your sort of your overall thinking. In what way would you describe?

John Martinis: 42:24

Well, you know, in The United States, there's a a general understanding that, you know, you build something, take data, and then figure out what nature is telling you. Okay? And that's certainly John wasn't John wasn't quite so far that way, but that's just the way, you know, people think. And, of course, when Michelle came, we had a lot of discussions about what the theory was and understanding it better. And that was really the the French way is to really do a careful theoretical analysis and understand the experiment really well and then go ahead and start building something.

John Martinis: 43:00

And Mhmm. What what I always says is sometimes experiment, you should do it the American way, and sometimes you should do it the French way. But what I think is more important is when you start the project, you say, is this gonna be a French experiment or is this gonna be an American experiment? Or maybe some combination of the two. And it's always some combination of the two.

John Martinis: 43:21

Of course. K? Of course. And and I think, you know, that realization, you know, when I went there, it it was was really good to to understand. And, again, there there's there's pros and cons of every approach, and you just have to but it's nice to have that tool kit of doing it both ways.

Sebastian Hassinger: 43:42

Absolutely. Absolutely. And so the now now Michelle Devore has now joined Google at at UCSB. Is this sort of a a you know, are you interacting with him now in the in the UCSB setting at all?

John Martinis: 44:02

So, you know, with the Nobel Prize, we've been inter interacting a lot. So yesterday, I I did an event at Google in in Palm Springs, in fact. And tomorrow, we're gonna do a q and a with students. And we very much enjoy this kind of panel q and a because we we look at things in such a different way. I think it's interesting and and we learn from each other.

John Martinis: 44:27

So we're doing that. I'm so busy with my company and he's busy with Google. We try to get together. What I find really sad is there's a tremendous number of things we can talk about. But because we're in businesses, we can talk about 5% of that.

John Martinis: 44:43

And and I find that really sad because I'm sure if we could talk freely, we could solve, like, 10 problems in the next two months Yeah. To, you know, to to make the field better. But okay. You know? And we just can't do unfortunately, we we can't do that.

John Martinis: 45:00

You know, I'm trying to figure out a way to do that, but I think that's beyond

Sebastian Hassinger: 45:05

It feels like there is still you know, I mean, big parts of this are still in a precompetitive stage, an open science stage. And and certainly the role of the public sector, you know, the national quantum

John Martinis: 45:19

industry I'm talking more about the Google part. Of course, with with the UCSV stuff that we can talk about it. Right. Right. And that's fine.

John Martinis: 45:26

But in terms of the Google stuff, which is more, you know, what I'm trying to do, it's the details of how to scale up qubit that that's, you know, that that's fine. But, yeah, you know, Michel has both, you know, his UCSB research and Google research, and when I moved to Google, I just put all that more into a practical direction. Right. That's the way that's why I retired at UCSB I see. To really focus on the practical aspect.

John Martinis: 45:55

And again, if we're collaborating with big semiconductor companies, then that's less what you can do with a graduate student research.

Sebastian Hassinger: 46:06

Yeah, yeah. I think I think we've hit all of the topics I wanted to cover. Is there anything that we haven't talked about that you wanted to talk about?

John Martinis: 46:20

No. No. This has been been been quite nice. And Good. Yeah.

John Martinis: 46:25

Thanks for going through various things. Sure. But I would say that the the the interesting thing is, you know, once we got this started and other people got involved in it, there are just so many creative ideas from around the world that push forward the the the project. And I would think what was really great is that the different research groups weren't trying to outcompete with each other and do something a month ahead of another group and scoop them. We were all kinda doing our own things and exploring it and then talking about it publicly so that the field got better.

John Martinis: 47:03

Now, you know, around 2010 or so, then it started being much more corporate and we stopped doing it. But we were far enough along that, you know, it was okay at that point. And obviously, there've been a lot of open academic things that have happened since then too. Yeah. So, you know, the the evolution of the field, I think, was good and people were, you know, cooperating with each other, competing but cooperating in the proper way.

John Martinis: 47:32

And that's why I think that's why the field has progressed so well.

Sebastian Hassinger: 47:36

Yeah. I agree. I would agree. And, I mean, the the, you know, the experimental physicists and the theorists who can start testing theory on actual quantum hardware are are kind of like your first customer for a quantum hardware device. Right?

Sebastian Hassinger: 47:52

So Yeah. And there's a there's a nice collaborative link there. Happening.

John Martinis: 47:55

Yeah. Yeah.

Sebastian Hassinger: 47:56

Yeah. Excellent. Okay. Well, let's wrap up. I'm gonna see what I can edit together out of the bits and pieces.

Sebastian Hassinger: 48:03

I'm pretty sure I've got a pretty good episode here. That's it for this episode of the new Quantum Era. I think I did end up editing together a pretty good episode. Apologies for any pieces that were difficult to follow. Thanks again to John Martinez for his infinite patience and his technical advice.

John Martinis: 48:27

I think what we should do is rejoin and try to see if the connection gets better then. Now we have to make sure that you get the recording so far.

Sebastian Hassinger: 48:42

This has been a production of the New Quantum Era. You can find past episodes on newquantumera.com on the web and connect with us on blue sky at @newquantumera.com. And if you enjoyed this episode and the podcast, please tell your quantum curious friends to give it a listen.