The Quantum Leap in Health and Medicine

In this week’s episode of Time to Talk Quantum [TTQ], Firgun Ventures co-founder Dr Kris Naudts is joined by Dr Lara Jehi & Professor John Morton to discuss all things quantum and health. 

The conversation examines how quantum could unlock new approaches to drug discovery, diagnostics, and complex problem-solving. As data outgrows classical systems, quantum offers a path to simulate biology, accelerate innovation, and rethink what’s possible. 

The episode also looks at what it will take to scale: from hardware and algorithms to talent, infrastructure, and early demand. With growing global momentum and new UK investment; the question is no longer if, but how quickly these technologies will start to deliver impact.

Available to watch on YouTube

Episode Themes: 

• Quantum & Drug Discovery 
• How Far Can Quantum Take Healthcare?
•  New Investment 

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About the guests: Dr Lara Jehi is the Chief Research Information Officer at Cleveland Clinic and Professor of Neurology at Cleveland Clinic Lerner College of Medicine. She is also the Executive Program Lead for the Discovery Accelerator, which spans more than 50 projects focused on AI, quantum computing, data science education, and international initiatives.

Dr John Morton is the Professor of Nanoelectronics and Nanophotonics at University College London and Director of the UCL Quantum Science and Technology Institute and co-founder of  Quantum Motion, developing quantum processors based on silicon transistor technology, and Phasecraft, the quantum algorithms company

Music: NGN2 by ILĀ from their album Quantum Computer Music. 

Content advisory: Time to Talk Quantum explores the intersections between quantum technology and AI, health, finance, defence and art. Some discussions may cover sensitive or technical subjects related to cybersecurity, global security, geopolitics or emerging technologies. This podcast is for informational purposes only and does not constitute investment, scientific, or medical advice.  

This podcast is brought to you by Firgun Ventures.

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Content advisory: Time to Talk Quantum explores the intersections between quantum technology and AI, health, finance, defence and art. Some discussions may cover sensitive or technical subjects related to cybersecurity, global security, geopolitics or emerging technologies. The podcast is for informational purposes only and does not constitute investment, scientific, or medical advice. 

Transcript

SPEAKER_05 0:05

Welcome to Time to Talk Quantum. I'm Dr. Chris Knolls, neuroscientist and psychiatrist, founder of Culture Trip and co-founder of Firgun Ventures. Today I'm joined by Dr. Lara Jehai, Chief Research Information Officer at Cleveland Clinic and Professor of Neurology at Cleveland Clinic Learner College of Medicine. Dr. Jehai is the executive program lead for the Discovery Accelerator, which spans more than 50 projects focused on AI, quantum computing, data science education, and international initiatives. She is also a steering committee member of both the Healthcare and Life Sciences Quantum Working Group and the AI Alliance formed by IBM and Meta. Dr. Jehai also serves in advisory roles for industry and federal agencies. I'm also joined by John Morton, Professor of Nanoelectronics and Nanophotonics at University College London, and director of the UCL Quantum Science and Technology Institute. His research involves the coherent control of electron and nuclear spins in solid-state materials and devices, with a focus on quantum technologies. Professor Morton is a co-director of the Quantum Biomedical Sensing Research Hub, the UK's first quantum hub focused on health. He is also a co-founder of not one, but two international quantum computing companies: Quantum Motion, developing quantum processors based on silicon transistor technology, and Facecraft, the quantum algorithms company. It's time to talk quantum. So the Cleveland Clinic, Lara, I mean, can you tell us a bit about its history and its status in the US and the world? I heard it's so big, it has its own postcode, its own prison, its own police, or is that an urban myth?

SPEAKER_02 1:41

All of the above, except the prison.

SPEAKER_01 1:43

We're working on that. No, you did your homework. So Cleveland Clinic is an organization that's a hundred years old. We over a hundred years old. We started in 1921. Back then, uh it was with four physicians who had just come back from World War I and thought that there should be a better way of practicing medicine than just each clinician opening up shop on their own. You know, the tradition then was these clinics, the small private practices. So their innovation back then was that medicine should be practiced as a team. And we were one of the first group practices in the US. And from the very beginning, it was a tripartite mission with research, clinical care, and education. And uh since then, we have grown now to be 83,000 people strong that are distributed across the US in many states. Ohio is our mothership in Cleveland, but we have facilities in Florida and in Nevada, uh, and outside of the US too. In Canada, in Toronto, um, a place that's focused there on more executive health and sports medicine. In Abu Dhabi, uh, that's a full-fledged surgical medical program now with a new cancer center added to it. And in London, uh the Cleveland Clinic London is our latest addition uh to the health system, uh, now just over two years old.

SPEAKER_05 3:28

Okay. I associate it strongly with uh cardiology and cardiac surgery. Is that is that right?

SPEAKER_01 3:33

Correct, correct. The very first uh cardiac catheterization happened at the clinic, many first, actually, not just in heart. Uh the first um uh face transplant happened in Cleveland Clinic. Uh the first uh uterus transplant uh in the US happened in Cleveland Clinic. Uh some uh fundamentals of medicine now, uh, like for example, sterotomin, you know, the chemical that drives our understanding of mood disorders and all therapies uh for depression, anxiety, that compound, that chemical was discovered in Cleveland Clinic. Uh so was the angiotensin, the main uh chemical that drives uh hypertension. Hypertension as a disease was defined in Cleveland Clinic uh back in the time. So biomedical research is really core to what we do. And because our research facility is right across from our hospital, then it always carries that clinical translation and that emphasis on team science, with working with our clinicians who are innovating in how care is delivered and how clinical practice is happening. Um, led uh, of course, with, you know, through innovations in many disciplines. You mentioned heart, that's what we were uh we were number one in the nation and in the world for almost three decades.

SPEAKER_05 5:10

I see. Oh wonderful. Congratulations. Yeah. You are also, aside from being a professor of neurology, you're also the chief research information officer. Can you tell us a bit more about that role and why you took that on?

SPEAKER_01 5:21

Correct. Uh yes, I'm a I'm a clinician scientist specialized in epilepsy. So my clinical role and my personal research have always been in that space. Um, but uh I've you know, by virtue of doing research, it just uh I had to learn more about the infrastructure uh at the QRID clinic when it comes to data, biological samples, um uh regulatory frameworks. So uh over the years, beyond my personal research, I did things like build an enterprise biorepository where we automated uh finding patients, consenting them, and collecting samples for biological research. We enrolled over 40,000 patients, collected over a million samples in the span of three years uh with one and a half coordinator, which is a fraction, like a small fraction of the manpower that's typically required, because we automated all of it. So uh I was doing this kind, you know, this is one example, but I was doing many initiatives like that. And then this position for chief research information officer opened up. It was a new position for Cleveland Clinic, because as an organization, we recognized that uh computation in biomedical research is no longer something that can be done on the side, it had to be intentional, uh structural. We had to build capacity. So it was uh it was fortunate for me because I matured in my personal journey at the same time as the organization acknowledging the need for such a role. And I remember looking at the you know the posting and thinking to myself, well, I'm doing this now, I might as well have the job. And I took that on in uh early 2020, and little did I know, you know, what came afterwards, but yeah, that's how that came to be.

SPEAKER_05 7:34

Oh, excellent. So when when and and why did the Cleveland Clinic embrace quantum and what is the idea? Is is it envisaged to give you a financial return, or is this just on the frontier of technology that you want to operate? What was the driving force?

SPEAKER_01 7:50

Sure, great question. I get that quite often. It's really more of the second uh point of what you mentioned. Um as I said, I was new in my role, uh, and I was uh having to deal with urgent needs for biomedical research here. Our data, for example, was ballooning. We were sitting back then at about 15 petabytes of uh data for research, and we were growing at the pace of three petabytes every year. So we had these big data needs. Everybody wanted to do AI and still wants to do AI, but that was, you know, in a way, the bread and butter, the now of how biomedical research happens. And we uh entered into an agreement with our local government, the state of Ohio, uh, and Jobs Ohio, that's a public-private entity focused on economic development, to co-invest uh with Cleveland Clinic uh about$500 some million dollars uh over 10 years to create an innovation district that's anchored on biotechnology. So I knew that all of those needs that I mentioned were going to just explode with this new partnership with the state. So I had to think of what do we need from a computational strategy perspective that would fit both what we needed in the present, you know, back then, which was the AI uh work, but that would also set us apart five, 10 years down the road, uh, so that we are relevant in the future, you know, not just keeping up with the present. And it is with that mindset that uh I thought it's important that we invest in quantum because that is the next frontier. And the the way we did that was with our partnership with IBM Research at the time, in this calculated risk approach, where we get what we need as far as AI and infrastructure, compute, talent goes. But at the same time, this will allow us financially to take the risk with quantum to explore what that future would look like.

SPEAKER_05 10:14

Okay, okay, got it. So you you guys work with uh IBM, also with Meta, I see. Your main competitor, for lack of a better word, would be the Mayo Clinic, I assume, in the US. They work with Sandbox, uh, indeed Google. Um, what's the main difference you would say between what you guys try to do at the Cleveland Clinic and what uh what what the Mayo Clinic focuses on?

SPEAKER_01 10:34

Yes, uh Mayo, they're I mean they're a great group. So for us, the one difference, not just with them, but with any other group, is that we are the only group in the world that has a quantum computer on site.

SPEAKER_05 10:46

Uh in the canteen.

SPEAKER_01 10:48

In the canteen.

SPEAKER_05 10:49

Yes.

SPEAKER_02 10:50

We would love to host you so you get here.

SPEAKER_05 10:53

You're welcome and have a look and see it in real time.

SPEAKER_02 10:56

That's what everybody gets excited about.

SPEAKER_01 10:59

It is actually in the middle of our cafeteria. Uh, that was because we had to retrofit it in existing space. And it's a finicky machine, as you know. You know, we beat it uh certain limits with vibration and heights, and uh where can we put the machinery that needs to go with it? And there were uh the choice at the end was either the canteen in the middle of the uh the research cafeteria or in our data center, uh, which would be in another city.

SPEAKER_02 11:34

And we went with the canteen because as I told our finance officer, you're like all the committees back then, I said, Well, we're not buying a diamond ring and sticking it in a drawer. It has to be you know, somewhere that's visible.

SPEAKER_01 11:54

But you know, I have to say that was uh that was great because it was a change, right? You know, you know, for these researchers. They we're a biomedical research group, so people haven't grown up with uh, you know, quantum computing and what it is and what it means. So having the machine be there uh was a great magnet for external partnerships and to build internal interest and uh activity around it. That really sets us apart from any other group because we're not doing quantum on the side. You know, we have we uh we have uh gone all in, you know, in a way, uh by having the hardware here um and all the activity that comes with that.

SPEAKER_05 12:49

And and and what what does the Mayo Clinic focus on versus you guys at the Cleveland?

SPEAKER_01 12:53

Is there a difference or I think they're trying to figure out what they want to do uh because they uh for uh that would be a great question to ask them actually, but for I can tell you from our side, we are um our journey has evolved since we've uh put the machine here. At the beginning, it was more exploratory with let's define what could be promising, you know, applications uh for it. And we've come to classify those in buckets, you know. So we have our quantum chemistry work, and that is all the activity related to drug discovery. We have our quantum machine learning work, uh, which is more along biomarker discovery, I would say. Um disease, uh, you know, uh some diagnostic type work. Uh and then uh there is the track that started a bit more recently in the journey where we realized that it's not really about quantum versus AI or you know, quantum versus machine learning. It's about how all of these computational methods and approaches can work together. And for that, there is the uh you know the theoretical, you know, computational research side, but then there is also the hardware side, where we have uh teams that worked on uh connecting, say, our QPUs, our quantum processing units with our GPUs, with our classical uh clusters. So there is now work that's going on with purely on the engineering side, with how do you connect uh you know and enable this type of computation? Um but uh what's uh driving all of this is the applications uh which we're doing with um you know within our teams.

SPEAKER_05 14:54

Okay, okay. Are there any other hospitals that you know in the US or in the world? I mean, I know the Suraski Hospital in Tel Aviv, together with Nvidia, that is doing some work. Are there other hospitals who have embraced quantum at this part, at this point of the journey?

SPEAKER_01 15:08

Not to this extent. Uh uh well, definitely none that has uh, you know, the hardware on site uh like we do.

SPEAKER_05 15:16

And uh you're the only ones, yeah.

SPEAKER_01 15:18

And none uh there is definitely great work. I mean, of course, there's academic work around quantum with uh quantum computing, with biology and chemistry and life sciences uh in many great uh places. Uh we have uh started this quantum and healthcare and life sciences working group uh with uh with IBM and and many other groups, and that team meets quite regularly, and it has representatives from life sciences uh from uh industry and from academia uh in the US and uh elsewhere. Um and you know, that has been a tremendous group. We've put out some of the our our initial publications were all white papers and you know perspectives. It was necessary to get the lay of the land before we dive in with our original research uh since then. Uh but and you could see in those you know names like the Broad Institute and MIT and um you know Purdue University and University of Chicago and uh uh you know, so we're working with with all of those uh groups, uh, and we're also uh bringing in uh partnerships with uh startups that are interested in uh looking at quantum for healthcare and life sciences. Uh we have a whole program around startups, we call it a catalyzer program. We launched that uh uh last year, and initially it was an experiment for us. Um, what we awarded the startups was time on the machine.

SPEAKER_05 17:06

Yes.

SPEAKER_01 17:08

And the three that we awarded, the one of them is algorithmic, uh, which is this Finnish uh yeah, they do great work with uh photodynamic therapy and looking at quantum applications there. Uh we partnered with them uh around uh that work, which is now in the finalist group for the Welcome uh Leap, where you know the Welcome Trust uh initiative around quantum for biology. So that was one of the three that we supported. The second one, uh Crador was a local um, you know, US-based uh startup uh focused on Alzheimer's disease, you know, with uh drug uh research. And they just published some work wherein there were specific examples where they surpassed Alpha Ford III. Um and then the third company is still trying to find its way. But you know, I would say two out of three, that was a good return, you know, for that. And then now this year, our second iteration of this, we're doing this in a partnership with a uh venture capital group, uh Q5. Uh and we just selected our four winners, and we will be announcing them soon. And I'm so excited about the new crop coming up.

SPEAKER_05 18:36

Yeah, brilliant. Very much looking forward to that. You also collaborate with Novo Nordisk in Denmark, who in their own way are pioneering, of course, in quantum, really pushing an ecosystem out of the ground there. Um, what is the nature of that collaboration?

SPEAKER_01 18:49

Yes, we are very happy about uh our relationship with the Novo Nordisk group. They're really uh very forward thinking. Uh, and their investments in quantum um uh go all the way from they want to define what is the hardware that's going you know to work best. So they have that whole uh initiative. And of course, they had their big investment in Nvidia on the uh AI side. Our relationship with them is purely on the quantum and AI applications end. So Cleveland Clinic as an organization has a long history with Novo Nordisk Foundation. You mentioned our you know, you mentioned our history in heart disease, metabolic disease. So we have a lot of work already with the NovoNordisk, you know, company and foundation over time. So when we got interested in quantum, it was a natural fit with their interest in that field. What we do with them is this uh fellowship uh program, which they fund, a postdoctoral fellowship exchange, uh where we send our trainees for um to Denmark to work there uh with labs uh around AI and you know quantum projects. The fellowship funds each postdoc for up to four years. Uh so you have time to do meaningful work and it's not just like a you know a quick rotation here and there. And uh vice versa. You know, so they could send also their postdocs here uh for collaborative work. So um uh we're um that has been they've been a great group to work with. So we're looking forward to see how that pans out.

SPEAKER_05 20:42

Okay, fantastic. I'm gonna bring John in here a bit more as well. So, John, you founded two quantum companies. They're on your way to be the Elon Musk of Quantum, people told me. I mean, perhaps not temperamentally, but in terms of number one. Um and you're also the director of the Quantum Biomet Hub. Could you maybe tell us a bit about the two companies you co-founded and then also, of course, what you're doing at the Quantum Biomet Hub?

SPEAKER_04 21:04

Yeah, sure. So I mean I would say in in quantum computing, uh, you know, the the big challenge is how do we bridge this gap between the um early uh quantum computing prototypes that we have today uh and these really exciting transformative applications such as the one that Laura's been talking about. And we can address this from two different sides. One on the one hand, we need scalable quantum computing technology, we need ways to build quantum computers that can uh reach the level of um hundreds of thousands, even millions of qubits. Um and that's what we're trying to do in quantum motion using the same uh silicon transistor technology that powers smartphones and laptops today, and use those uh elements as the building blocks of the quantum computer. Um but there's also big gains to be made by making the algorithms more efficient. Um and uh within Facecraft, that's the challenge looking at ways in which um quantum. Quantum algorithms can be made more efficient so that you can get more out of quantum hardware today and bridge that gap uh between the two. Okay.

SPEAKER_05 22:09

And then at the at the sensing hub, can you tell us a bit more?

SPEAKER_04 22:12

Yeah. So quantum sensing is this uh it's really exciting opportunity that comes out of this big drive that we've had in developing quantum technologies like quantum computing. And we've we've figured out new ways to harness um nature at the quantum level, uh at you know, at single photons or single atoms, single particles. And we've found ways in which that can be used to develop new types of sensors that can be applied in biomedical and health applications. And in some cases, that can uh just radically advance the limits of sensitivity. And in other cases, it means that we can get the same level of sensitivity for which you need a specialized laboratory test, but you can now do that in a low resource setting. What does that mean? It could be um uh in a GP surgery in a shopping mall or in a rural area. So quantum sensors really have the capacity to impact a wide range of different healthcare challenges.

SPEAKER_05 23:03

Okay. And and and where does this fit within the wider UK quantum strategy? Why why was it chosen to solely focus on sensing?

SPEAKER_04 23:11

Well, the the UK has had this um uh quantum technologies program going back uh almost uh 10 years now and uh funding these research hubs in different areas, computing, communications, sensing, and timing. And we felt the time uh was right when the hub was was uh proposed, when we proposed the hub last year, to do something that was really focused around the healthcare and biomedical space. Because I think to uh to get the most out of these technologies and make sure that you're addressing the most pressing questions, you need to be a sector focused so that you're working with clinicians and and finding out where can quantum sensors really help. So we we took what at the time was a radical approach. We weren't just going to cover all of quantum sensing. We were specifically focused in this healthcare space. And we brought a large number of clinicians working alongside the quantum physicists to find that common language and fine-tune the quantum sensors to these different applications, whether that's medical imaging, uh in vitro diagnostics, new kinds of surgical tools enhanced by quantum sensors, or the kind of quantum sensing technologies that can be used in fundamental life sciences to understand disease mechanisms.

SPEAKER_05 24:19

And it's research focused, or it's also meant to seed startups, or how do we think about that?

SPEAKER_04 24:24

The hub ultimately is uh focused on on research, but a key part of that, if it's success, is making sure that that research gets successfully translated. Um so it begins with a dialogue with clinicians to make sure that we're addressing the right challenges and we have the right target specifications. The uh, you know, the the typical um physicist just carries on making the sensor more and more sensitive and and doesn't necessarily know when to stop. You know, at what point is it good enough to actually start being applied? So our first mission is that clinical engagement that allows us to define the needs right. And then there's the research element, which is of course the sort of major part of where we're putting um our uh our efforts. But but then there's the translation side and the regulation side. And so we have um a significant portion of our funding focused on um those turning that research into proof of concepts that can go into um preclinical trials um and and then uh and then also to seed startups um through uh some of the uh seed funding that we have in the hub before it can be taken over by uh the traditional investment. And the hub or the funding for the hub is there open-endedly, or is it a 10-year project or what it's it's a five-year uh uh funding in the in in this uh initial phase. It's a 24 million uh pound project. It's something that could potentially be renewed, and it's also part of a uh much larger uh mission that the UK has set in quantum technologies. And one of these three missions is focused around quantum sensors for health. Um so we see ourselves as a key part of delivering that mission. Okay. And that sets this really challenging objective of by 2030 getting these quantum sensors into NHS trusts um around the UK.

SPEAKER_05 26:07

Oh, that would be amazing. That could be delivered indeed. Um what does it compare to? Is it similar to QB in Chicago, for example?

SPEAKER_04 26:15

Yeah, this there's it's actually uh there's a number of different centers around the world that have recognized this uh potential for quantum sensors. Definitely Chicago is a big uh area. Also Ulm in Germany and in Australia. Okay. Um I think each of these brings a different angle. Um, probably where we see as our uh one of our strengths is that level of clinical engagement that we have um that is well balanced between the quantum physicists and engineers and clinicians uh and our focus around these different the breadth of different medical applications where we see these quantum centers being deployed. The hub also collaborates internationally. You have partnerships with some of these obviously. I mean, of course, uh we've had uh very nice discussions with Cleveland Clinic. And we hosted uh a Cleveland Clinic at at uh at UCL to explore potential applic uh applications and collaborations, but we're certainly um looking internationally about where we can see complementary strengths and and and start developing these technologies together.

SPEAKER_05 27:12

If you look indeed a bit to bring it briefly briefly back to startups, I mean, according to the Tony Blair Institute, they published a report fairly recently. There's about 500 quantum startups in the world, and about 150 sit in the US, unsurprisingly. But second in the world comes the UK with about 65. What proportion of of that of these numbers are sensing startups, really? Or are they mostly on the computing side or the cryptography side?

SPEAKER_04 27:36

Yeah, I mean, I I I say from my personal experience, the UK has been a fantastic place to uh uh to develop uh the quantum computing startups, the national program, uh the investment landscape has been has been brilliant. And also plugging into the wider European um landscape has been critical. But it's certainly true that um uh perhaps counterintuitively, uh the the I would say the majority, or my feeling is that there's a majority of uh quantum startups within the computing space. Um, and yet sensors are now finding this um uh uh these early applications for quantum technologies. And so I think we're going to see um many more uh quantum startups coming out in the quantum space. The thing, the thing to sort of uh balance that against is is that of course, um, from a uh from a commercialization or investment perspective, um uh the diagnostics uh uh uh are not always the most attractive uh quantum uh startup proposition. You know, it's all very well to tell someone that you've got something, but uh really, you know, potentially the biggest returns are in providing uh then the uh the remedy.

SPEAKER_05 28:38

Yeah, true, very true. Um could you tell our audience also a little bit more about the actual technology that is quantum sensing in general and and perhaps with with also some um details on uh diamond sensors in particular?

SPEAKER_04 28:51

Yeah. I think this is one of the challenges that we have with quantum sensors because it's so broad, right? With quantum computing, we have a fairly well-defined definition of the machine. Quantum sensors describe a really wide range of different ways in which we're using quantum systems to sense uh properties uh more accurately. Um, but two of the big front runners are uh nitrogen vacancy centers in diamond and uh optically pumped magnetometers. I'll focus mostly on the uh in the diamond to start with. And here, uh if you take if you take diamond, um naturally occurring diamond, and it has a bit of color, that's because it's not pure diamond, it's not pure carbon. There is a few uh atomic uh defects. Okay. And um, through the development of quantum technology research, we found a way to um isolate the signal from individual atomic defects within the diamond. Now, why is that uh powerful? Because nano uh diamond can also be um uh uh produced in this uh nanoparticle form, in these nano diamonds, um, at relatively low cost. And this means that they can be functionalized to bind to particular biomarkers, and they can therefore be used in diagnostics. Um, we're all unfortunately very familiar with lateral flow tests and the red line from COVID. Um that red line um maybe uh, you know, is is is made out of gold nanoparticles. Um, and that's what that comes up as red. But you can also use uh nanodiamond, um, uh, and that can be more sensitive. And by manipulating these uh atomic defects within the nanodiamond, um, we can go uh one level uh further in sensitivity.

SPEAKER_05 30:27

Okay.

SPEAKER_04 30:27

And actually, we showed a few years ago that you can improve sensitivity by over 100,000 fold and therefore detect a single copy of an HIV virus. Okay. And this is a platform technology. Um you can then apply it to many different types of biomarkers. Um, we've shown it since the hub started um for COVID using clinical samples um from the uh UCLH. Um and we're we're just beginning to explore um the different types of biomarkers and diseases that we can use um for this kind of sensing technology. And it's a great example of something that then could be deployed in a GP surgery, as I said, in a in a shopping mall or in other kinds of um uh uh uh settings um that allow for faster point-of-care testing? Yeah.

SPEAKER_05 31:13

Yeah, as a former clinician, this this strongly speaks to me. And I'm sure the same is true for you, Lara. When you see these things that we could replace ECGs and all the mess that comes with it with a simple sensor, of course, it's amazing. I mean, I also saw and that speaks more to your field. I think some detection or early detection of Parkinson's, that there's been some TPC or tuberculosis testing. What are some of those that you are excited about with your particular clinical hat on?

SPEAKER_01 31:37

You know, as a neurologist, I have to be excited about the applications that allow us to diagnose different types of brain conditions early. Uh-huh. There is uh a transformative step in uh in epilepsy, for example, was the development of MEG, magnetoencinography, as a you know, as a tool that allows us to both to uh merge both electrophysiology and structure, right? We have an MRI, a detailed picture of the brain, and then we overlay on top of it where the abnormal electrical activity is happening. Um this is uh this is a unique capability, but now only a few hospitals have it, and it's a very complex thing to set up with magnetic shielding, special rooms, it's costly, uh, it's difficult. So the the potential that we have with quantum sensing to make all of this portable and available in the community, say for sports teams after head trauma while you know playing football or in uh in the uh defense setting, you know, for head trauma on the battlefield, or uh, you know, those are things that have always uh been very difficult to ascertain on the spot and situations where time is of the essence, right? You have a window to intervene before the damage is irreversible. So these types of applications where uh treatment is time-sensitive, uh diagnosis is difficult because of the location where the injury happens, the potential of portability that we have with the quantum sensing approaches is tremendous.

SPEAKER_05 33:37

Yes, it's very easy to get excited about it. Also, it's such a demonstration of quantum for good, and it's not even extremely long term, it's right in front of us. Now, yesterday I did a recording of an episode here on quantum and defense, which indeed is the other use for uh sensing. I don't know how how you feel about that as a doctor, as a clinician, about uh defense applications. Do you think about that in this line of work? Do you, John? I mean, I might ask you first, Laura, seeing that I opened it to you.

SPEAKER_01 34:05

Uh you know, we have um not just in medicine, I think we're living now in an era where in a technology advance in one field ends up, whether it wants it or not, it's never intentional, I believe. But you know, at the end of the day, it ends up benefiting other fields in ways that nobody imagined, right? Like I don't think whenever the laser was first invented, that people thought its biggest application would be scanning groceries in the grocery store. But yet, you know, that's how it happened. And uh a lot of the wearable technology that we have now in medicine uh initially started on uh with defense type applications uh because uh vital signs and you know function had to be monitored when people are when soldiers are deployed, for example. And it is that same technology that now we have in our Apple watches and people tracking their heart rate to see if they're exercising enough, you know? So um that is just the way I look at it is there's no limit either good or bad, right? So to any technology as it's as it develops, and we cannot fight it to stop it. What we can, nor should we, what we should do is look at what good can come out of it and then see how we can apply it in our own discipline. So I have no doubt in my mind that whatever is being worked on in defense could have some aspects that would benefit medicine and humanity.

SPEAKER_05 35:46

Okay, great. What do you think about that, John?

SPEAKER_04 35:48

Yeah, I I I agree. I think that many of these are platform technologies and uh a sensor that is developed for um some kind of uh a health application, you know. Of course, there are potentially ways in which that technology can be applied in other ways. And a quantum computer is another great example of something, which is a general purpose technology. So, but I think for me, the tricky aspect comes when um that potential security or defense application then begins to limit international cooperation, which I think is really critical for the development of these technologies. Um and uh and and so where we need to be careful is is uh maybe not overemphasizing these kinds of security applications. And we and we remember that there are still big challenges in developing these technologies. And to do that, we need to be working with the best people around the world and collaborating um to make these a reality.

SPEAKER_05 36:37

Yes, yes, I agree with a lot of the coverage in the media is also alarmist and talks about decryption and military and and often the side for good is not highlighted enough. Yeah, yeah.

SPEAKER_04 36:46

But but it was, you know, it was one of the ver first motivations for quantum computing. It was when it were the the field really shot up when uh Shaw's algorithm came along and realized that this RSA uh 2048 encryption could be broken. And uh and so you know it's a good example of where a kind of security or defense um application then ceded this uh you know hugely powerful platform technology that we're expecting to have a big impact uh across many sectors.

SPEAKER_05 37:16

Very true. Changing topic a bit before we go to drug discovery, one of the things that I'm also very interested in as a doctor and former clinician, and I'm sure the same is true for you, Lara, uh our education as doctors is not software oriented at all, let alone quantum. How are you finding that when you're bringing quantum in the workforce there in the Cleveland Clinic? Or I mean I'm sure people are receptive, but but but but where's the skills and and how do you go about that?

SPEAKER_01 37:43

Yeah. Critical question, and I have to say, and that was one uh that we um you know, you can't really address it in just one approach. So we had many initially workshops, seminars, uh open houses, just you know, that's why I say having the machine was good, because like we would organize some of our postdocs even organized a weekly lunch, it was on Thursdays, around the machine, and they called it Cubites, you know, as a play on the word with qubits. So we had to be, I mean, like bent over backwards with coming up with different approaches to to educate and communicate. We worked with our medical school here in Cleveland Clinic. Um we have our college of medicine, and it's one of the few medical schools in the US where research has its own year. You know, so there is the four-year medical school curriculum. There is an additional year uh in CCRCM, which is purely research.

SPEAKER_00 38:52

Okay.

SPEAKER_01 38:52

So we worked uh with the curriculum development team to add a data science education course right before that research year. And we embedded uh some self-based quantum education in it to start there. Uh we worked with our local school systems in uh in Cleveland so that we started middle school and high school with presenting what you know quantum is and what it can do. Our latest education exercise was partnering with one of our public school, state-funded schools in Ohio called Miami University. Uh there they did developing a bachelor's degree, a master's degree, and a PhD uh purely in quantum science.

SPEAKER_03 39:42

Okay.

SPEAKER_01 39:43

Um, there's of course many uh PhD and master's programs, but this will be the first bachelor that I'm aware of, where you just get trained in quantum technologies. And uh all of their students, uh part of their curriculum will spend a summer plus the semester that follows, so a total of about nine months uh in our labs applying this quantum education and biomedical uh research. So it's uh in all of this is laying, you know, the ground uh work for uh instilling this education um uh throughout our system, you know, from kids all the way to PhDs, and then offering them uh all opportunities to practice what they're learning, uh working at the Cleveland Clinic uh in our research labs and with our clinicians. It's uh it's a lot of work, but it's essential because if there's this technology and nobody knows how to use it, I don't know how useful is that.

SPEAKER_05 40:47

Yeah, exactly. Exactly. How are you experiencing us as clinicians to talk with uh John? You hinted at you guys not always knowing where sensitive, but tell us more.

SPEAKER_04 40:55

Oh well, I mean, yeah, the first step is really finding the the the um the right language because yeah, I I was actually I remember being around uh a coffee table when we were first cooking up the Q Biomed uh project, and we were all talking about sensitivity, and uh, we were all using three completely different definitions. You know, so a physicist was using we were talking talking about these nano diamond uh sensors, and uh as a physicist, uh I was talking about a sensitivity in femato Tesla per root hertz, you know, some magnetic, some physical quantity. And the biochemists that I uh we were with were talking about um how many nanomolar or picomolars, so some concentration of uh of a particle. And the uh clinician from Great Ormond Street was basically saying, you know, how much blood do I need to extract um from this child to get a measurable signal? And um, you know, so the first level is just, as you say, understanding what uh what what is the sensitivity that matters? What, you know, where do um what's the current bottleneck and where can quantum sensors help? But I think that, you know, since launching the hub, we're now uh uh well into uh well over a year into the hub. Um we're getting much more sort of inbound interest. Um the the uh profile of the hub and and quantum sensors has just um been shooting up in the UK. Um, more and more funding schemes to bring on new projects. And so now I think our big challenge is prioritization. Um, you know, we we develop these platform technologies, we end up growing long lists of different ways in which they can be applied. Some cases it could be transformative, but for sort of very specialized disease that just affects a handful of people. In some cases, it maybe offers a small improvement, but over something um uh very broad, like dementia. Um, and so how are we going to prioritize all of these things? Work it where are going to be the earliest wins? That's uh, I would say the chat kind of challenge that we're grappling with at the moment. Yeah.

SPEAKER_05 42:48

Very interesting one, Dorian. Is there is there any learnings? Are there any disciplines that are more receptive to this whole development?

SPEAKER_04 42:54

Well, one of the benefits that we've had is we've had clinicians um within the hub from the beginning. So they understand the right way to engage. They understand the huge time pressures that clinicians are under. Um and so knowing what those entry points are, right? Yeah, uh trying to create new workshops and getting people to come is extremely challenging. Finding out where they're already meeting uh and going into those opportunities and using that to showcase. I think that's that's been a key learning that has helped us um uh make with the early engagement. But I think once we've been able to start those conversations, um, we found uh clinicians extremely receptive and excited about this kind of um uh new technology.

SPEAKER_05 43:30

Yeah.

SPEAKER_04 43:30

Um and yeah, I I I guess for me, I'm I I'm learning a huge amount every day in these discussions, which makes it a lot of fun.

SPEAKER_05 43:36

And we learned from you guys too, obviously. I mean, for you, you shake shore's algorithm. We don't know what you're talking about at first. We say cytochrome P450. You don't know what we are talking about. So there's every day that sort of dialogue, and I'm sure Laura, you have it as well plenty of times.

SPEAKER_01 43:49

Yeah, absolutely. I really relate to um uh the John's point about uh learning the language. An example I I always use is I I should people who've heard me speak before heard it already. The when we started with putting those research teams together, you know, the biomedical researcher or the clinician together with the physicist or computational scientist, whomever is on the other side, it was like I had a role where the one half say speaks only German, the other half speaks only Chinese, and I'm asking for a document in English, you know. So it was people really they know their field really well, but then it's really hard for them to open up and you know get that other uh expertise. Yes, it's always uh for me, it's the most interesting and challenging part of the whole thing, actually. It's building the right team.

SPEAKER_05 44:49

Yes, no, and I I personally love these intersections very much. I mean, and they are everywhere with quantum, it intersects with AI, with finance, with the healthcare world. It's super interesting to develop these languages that then both halves indeed end up speaking. Um now, one thing that everybody always gets excited about lay people, doctors, physicists is drug discovery. As you say, it's a bit more popular perhaps than diagnosis uh purely. Um, when we look at that world, though, Lara, again, talking to you as a fellow medic, I mean, in in in in pharma, there is Beringer Ingelheim in Germany that is very active in quantum, probably probably pioneering, if you like. Uh, there's Nova Nordisk, there's Eli Lilly. Um but then to our point that we just made, um, drug discovery is also very much now uh a software and technology undertaking. Some people would say that Nvidia is the biggest health company in the world. Um, how do you look at that? Uh how where do you see that go? Pharma, tech, and who extracts the value of these companies that are being built?

SPEAKER_01 45:48

Yeah, it's uh it's the million-dollar question, isn't it? But you know, the the whole field is well, so pharma companies are also moving in the same direction, where the whole the exercise of drug discovery is a lot more computational now than it ever was. You know, the the days when, say, I went to medical school or was doing residency 20 years ago, where most discovery in general was happening with bench research uh at the lab and you know, trying different compounds to see what each one of them will do. Uh nobody does work this way now. You know, the the potential the computation, this is not now a hypothetical, this is the way drug discovery happens. Uh it is heavily computational at the beginning in its initial phases, whether it is, you know, identifying the leads, optimizing the leads, finding the targets, all of it is really simulation at this point. Um and that is uh great for our patients, actually, because instead of wasting years building compounds and then trying them out only to see that they don't work, at least what we can do now with computer simulation, you know, molecular dynamics, molecular modeling, however, uh, you know, all these different uh aspects, um whether it's happening in a biomedical research facility like the clinic, you know, or in a pharma company, all what it's doing, it's skipping steps, you know, it's it's simplifying the process where we are uh prioritizing what the compounds that need to be prioritized so that we can take them further down. So I think the biggest value from a societal benefit, hopefully, is going to be accelerated drug discovery and safer drugs. You mentioned the cytochrome P450, right? If we can simulate uh the metabolism of our drugs more accurately, uh then you know, better preempt toxicity, efficacy, safety, uh then that would be tremendous. Now, from a financial revenue perspective, who is going to recover most of that, that is very uh uh variable, I would say, across economies, across healthcare system structures, um and uh you know, financial constructs. It will be you know, it's the same as what's happening now in gene therapy, for example, right? Where we have these uh, you know, the CRISPR technology and other technologies that can potentially cure a disease with just one uh you know application. So that is wonderful in theory, but they're so expensive that uh patients cannot afford them, right? So what did you end up doing at the end? Are we helping society or not? Over time, all of these technologies become cheaper, so I have no doubt in my mind that over time it will all become affordable, but the challenge is this uh interval period where uh it's difficult, where how do you um deploy these innovations and maintain some equity uh where the people who need it will get it, not just the people who can afford it will get it.

SPEAKER_05 49:39

Exactly.

SPEAKER_04 49:40

John? Yeah, I mean I think this is this is a wonderful application of quantum computing, perhaps even the first one that Richard Feynman um spotted. You know, he said nature isn't classical, damn it. And if you want to build a simulation of nature, such as what you want if you want to design drugs in a computer, then you've got to make the computer quantum mechanical. Um I think that one of the challenges is in order to do this properly, we're going to need um very large quantum computers. There's some um really interesting uh near-term uh ways to solve the problem. Uh facecraft has developed a technique called quantum enhanced density functional theory, which is a a way to split the problem of modeling um uh molecules um by giving some of the hard problems to the quantum computer, but otherwise using, let's say, existing classical techniques. But still, I think to really realize this kind of potential, these are the kinds of problems um that will need uh quantum computers at the sort of uh million qubit uh uh plus level. So I think there we have exactly the challenge that uh Laura mentioned is if this is a billion-pound machine um and these problems take a long time to run, how are the economics going to work? And and it's a fascinating question. Then how is the the value of that going to be shared with with drug companies? And and I think if who knows exactly how that's going to turn out, but clearly if it's a if it's an accelerating um uh and transformative technology, quantum computing companies will find a way to uh we would extract that value. But hopefully we're going to find ways, and it indeed it will be essential that we find ways to build quantum computers in a cost-effective way, even at that scale, um, so that um you can really um use them for these kinds of applications. And and I that may require different kinds of quantum computing uh hardware than we have at the moment.

SPEAKER_01 51:30

Yeah, I totally agree with that. You know, the the way we're doing the way we're doing our part here is we have uh mobilized our uh we have a drug discovery group here in Cleveland Clinic, our center for uh it's morphing, you know, it's experimental therapeutics, and we do everything from it's mostly small molecules uh for the most part, and some biologics too, and some targeted immunotherapy, CART cell therapy for lung cancer and other types of cancer. So all of this is happening now in Cleveland Clinic, and we have a group that does develop vaccines, we just developed a vaccine for breast cancer. So all of those groups that are already doing drug discovery type work are the ones that have partnered with our computational, you know, the quantum chemistry team that uh we have. And we uh the way we what we're doing is we're basically dissecting all of these different steps on the computational side that have to go to enable these uh simulations. And we're building platforms around each one of them, right? So, like in a way, you take the work that we're doing and you see the papers coming out, each one of them is addressing a very specific computational challenge around that whole problem set, you know, that needs to be developed. And we're chipping at it step by step, uh, so that whenever the hardware is ready, you know, like John is saying, we won't be fully ready for it. And what we're realizing along the way is that there are all these opportunities to optimize where in a say, even if we have hardware that can take in, you know, that has a million qubits in it, or you know, that can take in so many uh such heavy computation, do we really need it to do that? You know, the fact that it can do it doesn't necessarily mean this is the way we should go. So uh we're doing a lot to figure out what of those steps really need quantum versus what can how far can we go with uh advanced AI approaches, right? To so that um you know we take the most advantage out of whatever hardware exists at any point in time.

SPEAKER_05 54:01

Why has AI not quite delivered though? I mean, if you look at say DeepMind, I mean, I mean, how could you not be excited when Alpha Fold came out? It was such a game-changing thing. I I still remember it, it's almost like as a medic, it's like goosebump material almost. Then the billions have gone in, isomorphic labs, uh, for example, but we're still not there. The clinical trial that was held out to be there by the end of 2025. And we all know we can have holdups, but it doesn't seem to be necessarily within reach that first AI synthesized drug. Why is that in your opinion?

SPEAKER_04 54:32

I mean, John, maybe you can I uh well, I I I uh probably Lara can say more with uh in terms of how uh yeah, in terms of the existing um efforts for uh for for computationally driven drug drug discovery. But uh maybe Feynman was right. All right. You know, may th there are elements which are extremely difficult. And if there are, you know, if if if there's um if we're limited to interpolating between what uh let's limited classical simulations are giving us, um, then maybe we miss something. And maybe that's the key ingredient that we need a quantum computer to provide in order to um support uh more uh you know more successful AI-driven drug discovery.

SPEAKER_05 55:13

Yeah, could easily be. Yeah, yeah. What do you think, Lara?

SPEAKER_01 55:16

Yeah, so there there are just inherent challenges with the way you can simulate drug with AI technologies. So uh, for example, you are uh in just the technology, you cannot simulate transitions in state really well. Uh we whenever we simulate drugs with AI, we assume a fixed structure. You know, this is the chemical compound and this is how it looks like in 3D. But that's not really how the body works. You know, proteins change and receptors change, and all of these bindings are happening in fluid. They're not happening in vacuum, right? So um it it that is only one of the issues. There's many issues like that. Uh rare diseases, that's a huge gap. That um, again, AI by definition, machine learning, it's uh the output depends on how good the input was, uh, whether it's diversity or depth of the data, right? So you train a uh model, Alpha Fold, or others on what is known. Well, so what is known is not really the uh enough about the receptors and targets in rare diseases. So you will come up with predictions that don't really fit that well what you're wanting to address. I mean, those are exactly the challenges with AI-based drug discovery that are uh fueling the interest in looking at what quantum can do.

SPEAKER_04 56:49

That's an excellent example of uh and it sort of highlights the importance of time dynamics um in in materials uh discovery, which is uh perhaps one of the earlier applications in quantum computing. There's some recent papers that have been coming out um uh claiming quantum at computational advantage. Um uh would looking exactly at this at this uh problem of time dynamics, of um modeling the the time evolution of a material. And it's exactly that kind of element that that uh Lara's referring to in terms of the uh dynamics in uh uh molecular dynamics that that uh may be challenging for existing AI models, but where quantum computers are already showing advances in materials.

SPEAKER_05 57:31

Okay. Could you clear something up, John, for our audience? Because I I feel that in the media this gets constantly confused. So on the one hand, the media talks about a million qubit computer, and it's never said whether it's logical or physical compute uh qubits and what these ratios even are. So that's one question. Second point is in many ways, drug discovery material sciences might very well be the low-hanging fruit. And we might we don't need a million logical qubits to to do some decent work there. Could you could you demystify the qubit counting a bit in this particular context of drug discovery?

SPEAKER_04 58:00

Yeah, no, the it's not only that, I know, but yeah, yeah, yeah. I mean it's it's a really interesting uh you know the the challenge is how do you how do you take all of these different quantum computers around the world and um what people try to do is say, how can I describe their performance with a single number? Right. Uh and of course we don't do that with normal computing. Um we have clock speed and we have RAM and we have all of these other factors. So the fact is you you you need to look at a number of different aspects. Um it's tempting to look at physical qubit count. In fact, that's one of the most um uh uh the the simple and general methods. But as you say, in order to solve these deep uh quantum circuits and and lengthy quantum algorithms, you need to wrap up these physical qubits into logical qubits which are more robust. Um, of course, one of the challenges there is there is no single thing as a logical qubit, right? Um qubits, logical qubits don't suddenly become immune for the rest of eternity. You have you have fairly rubbish logical qubits that may look may be a little bit more robust than the sort of physical qubits, or you may have um you may group together large numbers of physical qubits to make much more robust logical qubits. So I'm not in general a big fan of the of the logical qubit definition because it's just it's uh it's too open-ended. You know, it's like saying, how many applications can your computer run? Well, uh, it depends. Are we talking about one really difficult one or lots of little tiny ones? Um so you know, I think that uh generally speaking, um, when I talk about a million qubits, I'm talking about physical qubits. Okay. Um and uh and and the reason it's a large number is then we need to take take these into group them up to make uh the kind of really strong logical qubits that can run a long algorithm like a like a problem in in drug discovery. Um it's certainly possible that we can uh start to to get some gains earlier using these kinds of quantum enhanced methods. Um but I you know I would say that all these kinds of applications that have people really excited, um, you know, we need to show a route towards building quantum computers at the at this at the level of a million physical qubits, and to do so um so that you know they don't uh take up the the GDP of a small country to build, you know, that the kind of that the that they that they are in a form factor and a cost that we can we can make many of them and and put many of them into hot places like the Cleveland Clinic.

SPEAKER_05 1:00:30

Okay. No, wonderful. I'm gonna have a few more quick questions because I'm aware of the time. Um one country that's also emerging or has emerged very strongly in biotech is obviously China. Uh, and it also is investing heavily in quantum NAI for drug discovery, wanted to bring the process down from years to hours, is what I last read. Uh how how do we uh how do we follow that? And how do we how do we know what is going on there? And is it safe what is going on there? Uh uh how how should we how should we try to follow it, Lara?

SPEAKER_01 1:01:01

Yes, uh well, I mean, of course we're all supportive of international collaboration and science is always a race, but uh we have to live within certain geopolitical limits uh that govern what we do. And um that's there is a limit to how much you know the quality of what you can provide if you're cutting corners, right? So um we we can only follow what we're allowed to follow and what gets published and what is made available uh to us. Um and um you know, uh see what we can about what types of guidelines and principles in you know on being diet.

SPEAKER_05 1:01:53

You follow the science as well. You try to read papers about that, they publish at least in international journals?

SPEAKER_04 1:01:58

Uh yeah, try to, but um, you know, I think uh yeah, it's always difficult. You get asked, well, what what about the stuff that you don't know about? Well, I don't know about them. You know, it's it's hard. I think it's it is certainly um uh impressive uh what's what's being done in in quantum computing. But I, you know, if I I if I look at the quantum computers that are out there that um uh we can access and run on, um they're you know, they're still mostly in the US, I would say, uh in terms of the the most advanced systems.

SPEAKER_05 1:02:31

Okay. Um prediction markets, we we talked about it before a little bit. Uh there's a couple out there. There's there's polymarket, there's calci. Polymarket, the the quantum stuff that you find there is on you can make bets on in what quantum companies the US um will take stakes. That's what that's what they uh focus on. On Calci, it was indeed the the cracking of the shores algorithm, or indeed the ability for a quantum computer to simulate cytochrome P450, or indeed uh iron molybdenum cofactor. Um what do you think of that? I mean, have you put some money in or or does this count as insider trading? I don't it does not.

SPEAKER_04 1:03:09

It does not. Um yeah, I nor does it count as investment.

SPEAKER_05 1:03:14

Yeah, okay, exactly.

SPEAKER_04 1:03:15

Um, I think it's it's interesting that um, you know, the kind of uh dates that they were looking at, um, this sort of threshold of of where uh quantum where you get this inflection in in really significant commercial uh uh uh returns and advantage, um, you know, that the there seem to be um values between the market consensus was somewhere between 2030 and 2035. Um and and then you have efforts like this uh DARPA quantum benchmark initiative, a really fascinating, you know, international uh competition. Um uh uh where DARPA's asking the question, not just can you build a quantum computer, but can you build one which is uh worth building, right? Where the where the cost addresses the uh the value. And and DARPA's timeline for that, their deadline is 2033. So it sits right in between those two uh extremes. So I don't know, some some somehow it all seems to uh to be consistent.

SPEAKER_05 1:04:12

Yeah, sure. Lara?

SPEAKER_03 1:04:14

I would say that it's safer and faster ways to make money than to bet on the country. You I'll have to wait till 2035, and then you know, maybe some return comes. But no, but but see the But it's it's interesting that it sits there.

SPEAKER_05 1:04:33

It's interesting.

SPEAKER_01 1:04:35

That's what I was going to check in in on the serious side. I I was very encouraged to see that, in that at least there is this is now so mainstream and so so so in the like realm of reality that there is like uh this catchy where you know people are betting on it, uh, versus years ago where this was science fiction uh for uh as a conversation. So just its presence, I think, uh is proof uh of how far the field has gone.

SPEAKER_05 1:05:08

Yeah, I agree. And I find talking with the two of you also and just immersing myself more in the subject matter, it is heartwarming what quantum could do and how it could help the field forward and us as humans, contrasting that indeed with some of the stuff that today comes out of the US with the Maha movement and drinking raw milk and moon juices, sex dust. It it at least this gives a bit of hope. Um, and I do really hope that the Cleveland Clinic uh continues to contribute. And I'm sure we on our side of the Atlantic will do our bit. And I look forward to the to the future we're all building. Thank you so much, Lara, for your time. And thank you so much, John, as well. Thank you so much. Thank you. Thank you. It was a pleasure.