Welcome to TCS Life Sensor Advisory Podcast on leveraging quantum computing for licenses. I'm Sanjeev Sasiva, CTO and Head Advisory for Life Sciences unit in TCS. The destructive computational power of quantum computing can provide licenses industry just the path it needs and bring a paradigm shift in the approach, especially in areas such as drug discovery. And to discuss on this interesting topic today I'm joined with Jamie Gracia from IBM Research. Is making the world a better place through quantum chemistry? From revolutionizing plastic recycling to shifting chemistry modeling. Our work has crossed industry and global impact. Today she served as a senior manager for quantum applications, algorithms and theory at IBM Research. Thank you, Jamie, for joining us today and let me start with my first question. Today, technology is making a transformation shift. And quantum computing seems to be one of the transformation technology. Can you please share and explain quantum computing as a technology from your view? Yeah, Thank you, and thank you for having me. I'm really excited to be able to talk about quantum computers and how they might apply to healthcare and life sciences research. Quantum computers are fundamentally different forms of computation than what you can typically think of when you think of classical types of computation. So we can use them in similar ways, like towards similar ends and use cases. So for example, in applications and use cases regarding biology, chemistry and just nature in general, however, they fundamentally act and behave differently. So instead of computing with ones and zeros and programming and in languages that leverage that, what we actually do is we leverage different quantum states on a quantum computer due to the materials that they're made out of. And this enables us to have all sorts of new. Handles. For the programming such as superposition and entanglement. So fundamentally very different technology, they handle different problems or different pieces of the problems in different ways. So very complex problems or problems that are very inter correlated are good ones to look at. And so for that reason chemistry is one of our primary. Interests for use cases. Sure. I think it's very exciting and could be inflection point the way the computing will be done in the future. But from a maturity perspective that means how much change is quantum computing is now and how the Roadmax looks like? How do you see the quantum computing ecosystems are evolving? So I've been involved in quantum computing for a few years now and I must say that I am constantly surprised by how quickly this field is evolving and how quickly the devices are maturing. So when, you know, I first started and the quantum experience went online for people to try for the first time and actually try their hand at a quantum computer, we had A at that point in time we had a 5 cubic device. Today we are on the orders of dozens of qubits, and by 2023 we're expecting to have a device that has over 1000 qubits. And in addition to that, we're also improving things like error rates and noise and learning how to protect the qubits from external factors that cause them to lose their state. They're very fragile quantum state that we put them in when we program them. And so all of that is continuing to evolve at once. So what we're seeing is lower error rates, higher number of qubits, we call this quantum volume and quantum volume we set out to double every year. And then in 2020, we actually doubled it twice. So that also gives you an indication of just how quickly we are improving the devices and improving their fidelity to be able to start looking at complex simulations and calculations on them. So as you're saying right there is a maturity, there is a improvement in the accuracies. But if you look this segment from a life senses industry point of view, there are many problems which needs a high compute and very quantum computing is a promising technology for application and license industry. And then which are the problems you think will be the prime candidate? Where quantum computing can make him major significant impact. So alongside the hardware that's that we discussed is continuing to evolve, we're also continuing to push forward the software capabilities, our processing capabilities, how quickly we can run the calculations because right now we often we always have a classical piece to it, right. So there's the classical computer and the quantum processors work together for different parts of the problem. So we have those going on in the backdrop and in addition to that, the theory, the fundamental theory, the fundamental math, the algorithms that are being developed for evaluating specific problems with relation to chemistry and also healthcare, life sciences, biology topics and things of this nature. So we think that we can leverage all of these things coming together to be able to do some really interesting work. Also, the fact that we do kind of that we oftentimes pair up classical with quantum means that we can distill the problems and very large problems, very large complex problems that would be intractable today on a classical computer. You can think about distilling them in ways that make sense that are very clever to look at different pieces of it with a quantum computer for example, and then different pieces of it with a classical approach, so something that's more traditional. So this is 1 possibility. The thing that I'm probably the most excited about personally and having a background in chemistry myself is being able to leverage quantum computers for full configuration, interaction, type of calculations done on molecules. So in other words, you know the hope for the grand hope for the future. And the grand challenge is that we can eliminate the need to incorporate approximations into our calculations and be start being able to look at actual, you know, calculations on molecules. In a highly accurate way, without having to invoke approximations. But as we're starting out, we're trying everything. We're trying all these different methods. And applying them to problems that are of high interest to these different fields. And just exploring what it is, What can we do today using the devices of today? How can we prepare for the devices of tomorrow? And everything in between. And Jamie, as you touched upon chemistry, right and. In pharma, that is the fundamental existence of pharma, right. How the chemistry research happens, he turns about briefly. Would you like to share? Because coming from your domestic background, some throw some more light, right? From a formal perspective, how to impact on drug design, synthesis, protecting drug. Like properties and so on. So how would you wanna make a very significant impact for a farmer? Yeah, so. In my previous life I was a benchtop chemist, so I did a lot of synthesis and catalysis in the lab with my with my own two hands and worked very closely with all of my computational colleagues. And oftentimes what I found is that, you know, we would go in the lab, we would see some unexpected result, and we want to be able to explain it so that we could control the reactive environment. Even better to get the desired outcome that we were seeking. And this process oftentimes meant that we made the observation, we went down and talked to our computational colleagues and started discussing like how would we model this? What are the important things to, you know, put into the model and the important features to put into the model in order to have the model match what we were seeing experimentally and then be able to control it in the future. The process itself was very time consuming as a result, so there was a lot of back and forth, a lot of seeing a model and then saying like this looks good, except this one part isn't quite right. Like can we tweak it? And then sometimes we get a model and we'd go and test it in the lab and validate it to make sure that it was consistent with what we thought would happen if we changed some variable X, right? So there were certain limitations to this. It's in general it would be a time consuming process, and a lot of that had to do with these approximations that we needed to put into the calculations themselves. One very good example of that is how we deal with electrons or fermions in the system. So in a lot of models what they do is they they'll look at an electron and put it in sort of a field of electrons that kind of mimic the electron electron interactions. But they didn't have the distinct capability because it just took too much compute to be able to model every single one of those interactions. And So what I'm hoping we can get to in a place we can get to with quantum computers in the future is because we can leverage things like super position and entanglement. We'll be able to mathematically describe these systems in a much simpler and less resource intensive way without losing that accuracy, so that you no longer have to approximate and it can minimize that back and forth time between the experimentalists and the theorist. And that feedback loop will cut down further and further if you think about this in terms of making a new drug, for example, or new material that time. Costs a lot of money as you have to distill down maybe thousands of potential like a library of 1000 potential hits down to a few that you want to synthesize. Anything you could do to speed that up will be hugely time you know conserving and addition to that it would be money conserving as well. So those are those are the pie in the sky goals as well as being able to evaluate systems that we haven't been able to before that have just been too difficult. Of calculations, for whatever reason, to perform even on very small molecules. These don't have to be large systems, but it using quantum computers and as an additional tool to classical and an additional handle will give us insights to nature. I think that we wouldn't otherwise have insights too. Absolutely. And technology like quantum computing could be very, very strong enabler, alright to escalate the journey. So we spoke about how it can help, right. But if we just move to the other side of the table and say what are the challenges you see in adoption of quantum computing right in this context of life senses industry we just spoke about? Yeah. So some of the challenges just have to do with the fact that it is early days. And quite frankly, we have a lot of people coming from all sorts of different backgrounds who need to be able to talk together and to be able to speak the same language in order to make a technology like this work. So if you think about a person who is a domain expert in chemistry, is no longer. Completely removed from the computer scientists who created the computer that they're using. It's now a hand in hand process and it's now that there's this feedback loop that has to occur. So I'd say one of the biggest challenges and one that we've experienced in our teams is just being able to like understand the person across the aisle in a different field and what they're saying and how to translate it and how to learn from each other. So that has been a really interesting and I would say unexpected challenge that has come out of this. When I say electron you say for me on what does that mean and how do you think about energy differences? What? How do you wrap your head around something like that and get intuition of another person's field, basically? It's a really interesting challenge and I think it's one that's been probably one of the most exciting to watch people learn from each other and evolve into this basically completely new area. So that's one. Certainly there are challenges in figuring out which problems are best suited for quantum computers and how to divvy up the workload. Between a quantum computer and a classical that is another challenge. Quantum computers aren't big data machines, right? So like you, if you just want something that can handle a very large amount of data, quantum computers aren't going to necessarily be better than classical for that. But if you want something that you're starting to look at very complex interactions and dynamic interactions, quantum computers could tackle that piece of a problem very well. So those are, I think some of the challenges is identifying where and when the quantum advantage occurs. So using a quantum computer has some sort of advantage over a classical device, how to pair them up, and then finally how the scientists talk to each other, because it's still so early days. That we all still need to talk to each other to figure out how to use quantum computers for use cases and things like healthcare and life sciences. So as an industry, we should be able to cross the chasm, right? Jimmy, with your experience within IBM and working with various stakeholders within the industry. What and where the IBM in this journey of point of computing, especially in the context of life sensors? So I think one of the really important things that we have done recently is leveraged different partnerships for life science and healthcare research using quantum computers. We do this in a number of different ways, whether you can think about it in terms of our own research that we do internally. Looking at different pieces for chemistry for example, and really developing out the foundation. For chemistry problems, going from things like computing the ground state energies of molecules to now kind of thinking about what is the next calculation and more complex calculation we need to be doing to build up our toolkit for chemistry calculations. So things like excited states that are important in photosynthesis as of one example, and then also being able to develop out two things like properties, molecular properties. Such as dipole moments and molecules. So we've really been working on the foundational research ourselves and in addition to that and thinking about the future and how part different partners are also thinking about how to leverage quantum computing for their own specific use cases. One really good example of this is a partner Cleveland Clinic that. Will be leveraging their own quantum computer and their own system to really try to address. Their problems of interest in an accelerated fashion, so by having this capability of the hardware in house then the feedback loop becomes much much faster. And you can also think about how this might integrate into other types of workflows that might be going on. So like for example, if you have AI that you're leveraging for drug discovery. Is there a part of that you want to be able to do with a quantum computer? And if so, how quickly can you turn that around and get to the right answer? So I think being able to leverage and have access to the devices and the hardware is a very critical component and piece of developing out any workflow for applications. And so I think for healthcare and life sciences, what that looks like is that developing out the algorithm, so to describe the molecular natural systems, the biology that you're looking at. Understanding which pieces of the problem you want to look at with a quantum computer versus other technology like AI or using classical algorithms which are constantly continuing to develop as well. And then how do you tie this all together and accelerate that feedback loop as the software hardware in theory, all continue to develop and out at the same time. So I think this is where we're at with within IBM building out the foundations. Understanding the methods. And then understanding how we can leverage different technologies and pull them together with partners to be able to realize the power of quantum computing. I think great to hear that the IB miss investment and maturity and the way that IBM is taking the Quantum converting forward. And my last question, based on your interactions within IBM and also with various stakeholders within the industry, what would you like to give to the industry to make them ready for quantum revolution if I make calls for? I would say the earlier you get involved and educate yourself on it, the better. They're not easy concepts to wrap your head around. And even just thinking about how a quantum computer works compared to what you're used to on your laptop is it takes a lot of understanding and it takes some time to really digest how they're different and what problems a quantum computer can tackle that classical computers could not or not as well. So I would say the earlier you get involved, the better. The other thing I want to mention is that as we develop out our devices right, we're taking lessons learned from the previous devices and then using them to make the next generation. And So what that means is that it's not a black and white thing. It's not like the quantum computers we're using now are going to be so fundamentally different from the ones that have the capabilities for things like error correction in the future and universal fault tolerance, right? So getting in now, learning early, figuring out like, where you might derive value. Is what I would. Recommend to anyone thinking about how they're going to leverage these because the technology is coming, it's just a matter of time. So how ready will you be for it once it's here? Absolutely, Jamie. And with that, I just want to say a thanks to you for your great insights and creating more excitement on what quantum computing can do. So thank you very much, Jamie, for your time today. So we were talking to Jamie from IBM Research and thank you for listening. Thank you very much. Have a great day.