A quantum leap into the future of drug development

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Speaker: The BioWorld Insider podcast.

Lynn Yoffee: This is the BioWorld Insider podcast. I’m Lynn Yoffee, BioWorld’s publisher. Every year, a group of trailblazers and forward-thinking executives converge at the BioFuture conference to evaluate the future of health care. This year, there will be more than 100 speakers who will focus on the future of biopharma, digital medicine, big data, AI, health care systems, insurance providers, and beyond. The BioFuture conference is in midtown Manhattan from Oct. 28th through the 30th. If you attend, you’ll have the chance to hear panels and join workshops and fireside chats with key opinion leaders.

Our guest today on the podcast is one of those BioFuture panel members. Victoria Lipinska is America’s lead for Quantum Innovation Centers at IBM Quantum.

Welcome, Victoria.

Victoria Lipinska: Thank you so much. I’m happy to be here.

Lynn Yoffee: She’s talking today about the future of big data and drug development with Lee Landenberger, a BioWorld staff writer and the BioWorld Insider podcast host.

Lee?

Lee Landenberger: Thanks, Lynn. Victoria, I really appreciate you taking the time to chat with us today. I’m looking forward to hearing your panel presentation at BioFuture. I want to step back real quickly. BioWorld covered IBM’s collaboration with the Cleveland Clinic with the launch of the Quantum System One, which was the first time this type of system was established specifically for health care and life science research. It’s nice to talk to you for a follow-up and to get ready for the next couple of weeks.
I want to start with a really basic question, and this may be one where I may be the only one who doesn’t know the answer, but quantum computing strikes me as an interesting phrase and I want to know if you just give me a basic on what it is and why is it different than AI or machine learning, or is it different?

Victoria Lipinska: Yes. Thank you, Lee. That’s actually a very good question. I think that we hear this phrase quite a lot, right? Quantum computing in the context of AI and high-performance compute. I think it’s really good to take a moment to think about what it actually is and what it means. Quantum computing is a completely different branch of computing as opposed to what we know right now, and it’s meant to complement what we know, not to really replace it.

When we’re talking about regular computing, so in the context of our laptops or our phones or high-performance compute, we’re talking about bits, and bits are the basic unit of information. In the context of AI, we’re typically talking about neurons, but in the context of quantum computing, we’re talking about quantum bits, or as we like to call them, qubits. Qubits use principles of quantum physics, or more specifically, quantum mechanics. In order to produce them, we need to do some special tricks because they’re quite fragile.

In order to make them survive, we need to bring the temperatures really, really low. Actually, those are ultra-cold temperatures of 15 millikelvins. If you think about it, this is really colder than the outer space. Currently, we’re on a second generation of quantum computers using those quantum bits. At IBM, we have IBM Quantum System Two with the largest chip of 156 qubits, or quantum bits.

Lee Landenberger: Looking into the future, I’m really curious what it is that you see quantum computing becoming capable of.

Victoria Lipinska: Because this is a completely new way of computing, we also want to be solving new problems. We hope to unlock problems that are either classically difficult or completely intractable. We’re getting closer to applications that are relevant to industries. Quantum computing doesn’t have a practical advantage over the regular or classical compute quite yet. Nevertheless, it’s still important to ask the question, where are we right now and where do we want to go?

In June 2023, we achieved a so-called quantum utility. This was a demonstration that a quantum computer can run certain quantum circuits or quantum computations beyond what is possible classically to simulate. That was Step 1. Step 2 is so-called error-correcting code. This is just another step to get us into the quantum computing of the future to have hardware resistant to errors. Normally, this is very costly in qubits, but we have a recent result where we show that this is possible to reduce the number of qubits by 90%, so quite significant.

While error correction is a big goal that we hope to achieve in 2029, we do think that the industry will be able to find that advantage in quantum computing before that happens, before error correction.

Nevertheless, error correction will open that door to quantum advantage even wider.

Lee Landenberger: Error correction, that’s an interesting phrase. Can you talk to me a little bit about that? I assume it’s just reducing the number of mistakes, but I’m curious about what you mean.

Victoria Lipinska: Yes, that’s exactly what it is. Currently, on the hardware, we do experience noise. This is due to the physics of the computation because we do have to remember, like we said at the beginning, that the physics of computation is completely different for the first time since we invented transistors. Because of that, and because we’re building this technology, we’re inevitably experiencing noise. However, we can use those error-correcting codes or error correction in general to reduce the errors that we have on the hardware, but this comes at the cost of a higher number of qubits.

In other words, we need to introduce redundancy in additional qubits in order to be able to tackle errors better. This is where we want to go with error correction.

Lee Landenberger: Give me an idea of why the health care and life science industries are so interested in quantum computing.

Victoria Lipinska: Ah, yes. That’s a great question. Typically in quantum computing, at least at this point, we’re talking about three main areas of applications. That means simulating nature. Here we can think about quantum nature of quantum materials, drugs, so on, being simulated with quantum computers. In other words, quantum simulating quantum. This is a very big simplification, but I think it gives the gist pretty well. That’s No.1.

No. 2 is data with structure. You can think of images with patterns and so on.

The third one is data without structure. Here we’re talking about databases and searching them, optimizing them, so on. In health care life sciences specifically, we can use those three main areas of applications of algorithms to tackle problems related to health care life sciences. Those problems can be, like I mentioned before, imaging. Really here, searching for features in images, drug discovery. This is that quantum simulating quantum part. Clinical trial optimization or [unintelligible 00:08:00] drug interactions, protein structure prediction. Really problems that are fundamentally important to health care and life sciences but have a computational bottleneck that can be tackled with those three main areas of applications for quantum computers.

Health care life sciences is not the only area where quantum computers can be used. Some other applications include materials, optimization, sustainability, and high-energy physics.

Lee Landenberger: I want to ask you about drug development. That seems like a front-runner in quantum computing. If you’re going to speculate, and I know you probably — that’s not always the easiest thing to do, and not you’re not always comfortable to do it, but I’m curious if you were to speculate about how quantum computing might change drug development, what do you see in the future?

Victoria Lipinska: I really like that question because we actually worked very closely at IBM with Cleveland Clinic, as I mentioned before. There is a thing that Lara Jehi always says, Dr Lara Jehi, who is one of the leads at Cleveland Clinic for quantum computing. She always mentions that the overall process of drug discovery can take on average up to 17 years. That’s a really long time. That means from the idea all the way to taking drug to the market. Even thinking about that, if we had the potential to reduce that number, that already would be very good.

To be a little bit more specific, in quantum simulations, in chemistry simulations, we typically have pretty good classical or regular compute models. Some problems can encounter bottlenecks in this classical computing. For example, that’s ground state energy problems to determine specific properties, those are quite expensive to compute. Drug discovery involves complex molecular interactions that are challenging. to model using those classical compute models. Quantum computing could simulate these interactions at the quantum level, like we said. This can lead to more efficient drug discovery by identifying those promising compounds faster, understanding their effects at the molecular level, and then reducing the need for costly or time-consuming lab experiments.

If we circle back to these 17 years, if we accelerate the overall process and potentially lower the cost of bringing new treatments to market, it’s worth a shot.

Lee Landenberger: It’s underestimating it to say, “It’ll just save a lot of time.”

Victoria Lipinska: We hope to think so, but we still have to wait for the results.

Lee Landenberger: Who, in health care and the life sciences is exploring the use of quantum computing right now?

Victoria Lipinska: I always like to bring up Cleveland Clinic in this, and that’s a little bit for a personal reason because I’ve been working with Cleveland Clinic for two and a half years now. This is a 10-year partnership that we have at IBM that’s not just focused on quantum computing, but it’s also focused on AI and high-performance computing.

The whole partnership is called Discovery Accelerator and it’s meant to do exactly that. It’s meant to accelerate discovery in health care and life sciences. Cleveland Clinic specifically in quantum computing has the first quantum system in the world that’s dedicated fully to health care and life sciences research, and it’s on their campus. First one as well ever installed outside of an IBM facility, and it’s located in the Lerner Institute.

On top of that, there are many other institutions that are interested in health care life sciences, and they’re gathered in the so-called health care life sciences working group. This is a group that focuses on developing applications around health care life sciences in quantum computing specifically. If anybody is interested to read more about that, I recommend searching for a paper that’s called Towards quantum-enabled cell-centric therapeutics. It’s a mouthful, but that’s a perspective paper on what can be done with quantum computing in particular in health care and life sciences.

Lee Landenberger: What are IBM’s longer-term roadmap plans for quantum computing?

Victoria Lipinska: For several years now, we’ve been publishing a roadmap every year that’s not just about hardware, so not just about those qubits and devices, but it’s also about software enabling researchers to use quantum computers. We’re focusing on building scalable, fully error-corrected quantum computers, like we said. We want to deliver quantum systems with increasing qubit counts, aiming for that error-corrected system in 2029. That one will be executing 100 million gates. That’s a measure of what kind of operations we can do on that system, over 200 qubits.

By 2033, an improved quantum error-corrected system is expected, capable of executing 1 billion gates, so significant improvement, across 2,000 qubits. Like I said, it’s not just about hardware, it’s also about software stack that comes on top of that. We’re focused on developing that to address really real-world problems, not just theoretical problems, like here, drug discovery or logistics. We can’t do it alone. We also want to emphasize that as much as we can provide hardware and software, it’s really important to look into those hybrid quantum-classical algorithms, where we collaborate with our partners in the so-called IBM Quantum Network to develop new applications for many areas.

Lastly, what I would like to emphasize is that we stress the importance of quantum education to build the future workforce for quantum computing. This will only grow in the future. It’s really important that we invest right now into the workforce. The way we support that is through open access to our quantum systems.

Lee Landenberger: Fascinating. Lynn, do you have any questions you’d like to ask Victoria?

Lynn Yoffee: I do. Victoria, this is all really fascinating indeed. I was just thinking about it from a patient perspective, the patient advocacy groups, somebody with a disease. What does the timeline look like in terms of how this technology will help patients with real diseases today? Are we talking about an impact that’s still quite down the road, another decade, until there’s a practical application, or just a few years? Can you look into your crystal ball a bit and predict when the practical applications might come?

Victoria Lipinska: Thank you, Lynn. The progress in computing in general, not just quantum computing, happens extremely fast sometimes. Sometimes it takes quite a while to put a specific algorithm from an idea to production. Right now what we are doing is specifically focusing on creating new algorithms and applying those that we know in specific areas like drug discovery or imaging, searching, and so on. With dates on our roadmap, we can really make a statement about hardware and about software. Applications are quite a different story. I don’t want to make a specific prediction right here.

Lynn Yoffee: Absolutely understood but I had to ask.

Victoria Lipinska: [chuckles]

Lynn Yoffee: We’ll certainly look forward to tracking the development along the way. BioWorld will stay in touch with any new updates. Victoria, thank you for your time and insight today. We really appreciate you joining us.

Victoria Lipinska: Thank you so much. It was a pleasure to be here.

Lynn Yoffee: Just a quick reminder about the BioFuture conference. BioWorld is a sponsor because we think it’s a great opportunity for our readers to meet investors and potential partners during networking receptions, one-to-one meetings. It’s not too late to sign up. For more details, you can find it at biofuture.com. That’s our show for today.

As always, BioWorld will continue to keep you informed of all the most important scientific, clinical, regulatory, technological, and business updates. We’re a daily news service covering the development of the most innovative human therapeutics designed to improve the human condition. If you need to track the development of drugs, turn to bioworld.com. You can follow us on LinkedIn or X. If you want to share news with us, drop us an email to newsdesk@bioworld.com.

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Thank you for joining us today.

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Speaker: BioWorld, published by Clarivate, is a subscription-based news service that delivers actionable intelligence on the most innovative therapeutics and medical technologies in development.