Catalyst with Shayle Kann - Quantum computing could be a critical climate solution

Episode Date: December 16, 2021

What exactly counts as “climate tech”? Basically all human activity is responsible for emissions, directly or indirectly. So nearly every new technology trend or capability has at least some role to play in curbing those emissions. Robotics? Sure. Artificial intelligence and machine learning, of course. Synthetic biology? Definitely.  But here's a really interesting one: quantum computing.  Mark Cupta is convinced it may actually be one of the most important technologies we'll invent to mitigate climate change. Mark is a partner at Prelude Ventures, a climate-focused venture capital firm, and he’s made multiple investments in quantum-computing companies.  Shayle and Mark talk about how it might unlock climate-tech breakthroughs that would otherwise take decades of brute-force PhD power. They talk about applications for new materials, battery and fuel chemistries, and synthetic biology. It could also help to solve optimization problems to improve the efficiency of logistics and operations.  Although quantum computing may not itself reduce carbon emissions in a huge way, it could essentially enable other critical technologies that we need to fight climate change.  Catalyst is a co-production of Post Script Media and Canary Media. Catalyst is supported by Atmos Financial. Atmos offers FDIC-insured checking and savings accounts that only invest in climate-positive assets like renewables, green construction and regenerative agriculture. Modern banking for climate-conscious people. Get an account in minutes at joinatmos.com.

from the studios of PostScript Media and Canary Media. I'm Shail Khan, and this is Catalyst. I always tell people the bold statement is, if I had a magic wand that can make one thing true to help solve climate, I'm making a fully fault-tolerant, powerful, massive quantum computer because it is the equivalent of, if I have one wish for a genie, I'm wishing for infinite wishes. Here's a fun game to play.

You name an exciting new area of technology development, and I will tell you why that technology is, in fact, climate tech. It's surprisingly easy. Basically everything has a case. But just because something might have applications in climate does not necessarily mean it's going to be a big decarbonization driver. To know whether it might be, you need to dig in deeper. And that's what we do here.

So this week in the hot seat, quantum computing. When utilities need flexible capacity they can count on, they turn to Energy Hub. Energy Hub works with more than 170 utilities, coordinating over 2.5 million devices to manage 3.4 gigawatts of flexibility, built for the moments when utilities can't afford uncertainty. Energy Hub builds and operates virtual power plants

that utilities actually stake their grid planning on, coordinating EVs, batteries, thermostats, and more through a single platform built for utility scale. Predictive, verifiable, and designed to perform when it counts. Learn more at energyhub.com. I'm Shail Khan. I'm a partner at the venture capital firm Energy Impact Partners. Welcome. So here's the thing about climate tech. Because greenhouse gas emissions are unfortunately so pervasive. Basically all human activity is responsible for them, either directly or indirectly.

Nearly every new technology trend or tech capability has at least some role to play in curbing those emissions. Robotics? Sure. AI machine learning, of course. Synthetic biology, definitely. Definitely. Computer vision, Web 3, pick your poison. But here's a really interesting one. Quantum computing. I think if you're not already spending a bunch of time trying to understand quantum computing, you basically think it's some future computing paradigm that will let us do much faster, much more complex calculations than we do today. And essentially, I think that's right. But from a climate tech perspective, it's really intriguing because our job is basically to imagine what if this existed? What if we had quantum computers? And to be clear we kind of do, but only kind of. You'll learn more about this. Anyway, if we had them, what would we do with them? And how much might that matter from a greenhouse gas perspective? This has been on my mind lately because my job is to invest at the intersection of climate and deep tech. Well, quantum computing is undeniably deep tech, but is it climate tech? And more importantly, will it significantly impact our mission's trajectory? My friend Mark Kupta thinks yes.

He's a partner at Prelude Ventures, which is a climate-focused fund and a frequent collaborator of ours at EIP. Mark has made multiple investments in quantum computing companies after having convinced himself that it may actually be one of, if not the most important, technology that we will invent to mitigate climate change. So, is quantum computing really climate tech? Let's find out. Mark, welcome. Thanks, Shell. Really appreciate it. Glad to be here.

I'm really excited for this one because I feel like I have a 5% understanding of the topic we're about to discuss. I don't know what percentage you think you're at, but it's substantially higher than mine. You know, it's hard to judge. Let's go 10, maybe 15 compared to the quantum physicists out there. But it's been a fun space to research and try to get smarter on as an investor in the startup landscape. Yeah.

All right. Well, maybe between the two of us, we add that up, we can get to 20% or something like that. I love it. Let's start by attempting to give a layperson's definition of quantum computing. You sent me over a video, actually, in preparation for this, that was incredibly valuable of like an explanation of quantum computing. I think Wired magazine or somebody like that did that was like at five levels.

It starts with an elementary schooler and it ends with like a quantum physicist. I watched the whole thing and I got through the quantum physicist and I still feel like I'm at 5%. But it was better than nothing. thing for sure. It is. And that was from, I believe, a quantum physicist at IBM put that together. And it's a really great watch for anybody that wants to sort of walk through how the most simplest definition to the highest level of this space. Because it is confusing. It is complicated. And as somebody that has a background in chemistry and chemical engineering, I took quantum physics courses and physical chemistry courses. And so I understand it's weird, it's strange.

historical physicists like Einstein didn't believe it in a lot of ways because it said how could this possibly be true because the things that we see in reality don't match what happens at the atomic at the quantum level so there's some fundamental cognitive dissonance that causes people to react in a way to quantum physics it's almost disbelief in a way

which is which to me makes it even that more interesting Yeah, a lot of it is super counterintuitive. But okay, let's see if we could do this relatively succinctly. Define quantum computing. If you don't mind before defining even quantum computing, it could be good to talk about what classical computing is as a relative to quantum computing. When you think about a classical computers,

you think about the bits, the zeros, and the ones that make up literally every single calculation that goes through in our cell phones, in our laptops, in our cars, everywhere we use that. are all basically calculations on transistors of something that is either on or off. And that is a process as we've gotten smaller and smaller and smaller, the Moore's Law of scaling of transistors,

we've gotten more power. But that's a really brute force way of thinking about solving things and compute. One really interesting analogy is, for a classical computer, is if you were putting a bunch of kindergartners a room doing very simple zero one calculations. If you have them do it all in parallel, they can actually solve really hard problems that way. But it's through a very, very brute force method. A quantum computer is similar in a way that it still has a bit, but they call it a quantum bit. And the power of a quantum bit is its ability to live in two different states at once.

And so it is a thing called superposition. And so within superposition, the quantum bid is both zero and one at the same time. That's a little bit of an oversimplification of it, but that's good enough from the low level of a quantum computer. And that allows a quantum computer to do things in parallel versus doing them in series. And so you almost imagine solving a problem concurrently going through from the state of what that algorithm is you're trying to solve all the way through that end state. And it's really the power of a quantum computer is the power of exponentials.

And I could do a quick pause and tell you about how powerful exponentials are that's a really interesting visualization that I think for people, that's something tangible that lives in your everyday life, which is a deck of cards. Jell, have you ever heard of the Power 52 factorial

with the deck of cards? And do you play cards at all? I have, but yeah, Go on. Yeah, so I think it's an interesting way to think about it because most people have touched a deck of cards. Most people have shuffled the deck of cards. And when you shuffle a deck of cards,

you are with near certainty the first person who's put the deck of cards in that order in the history of the world. And the power of this number is so great. The visualization I tell people is, say you're standing at the equator of the earth and you take a step every billion years and you walk around the earth.

And when you complete that, you take a drop out of the Pacific O'Reck. ocean and you put it into an infinite bucket. And then you repeat that process, taking a step every billion years. And when the Pacific Ocean is empty, you put a piece of paper on the ground. And then you refill the Pacific Ocean and you do that again. When the pieces of paper hit the sun, you only have to repeat that 3,000 more times to go through all of the different permutations shuffling one time a second a deck of carts. It is that powerful. And that is the power of a near exponential, which is a factorial.

And this is the challenge that a quantum computer can solve in an ultimate state because it's able to solve all those things in parallel versus doing something in series. And so problems get really, really challenging when you have permutations that grow at an exponential rate. Right. So to try to read it back, I mean, that was the simplest version of the definition or explanation of what a quantum computing is. we could go into substantially more detail

or someone could anyway. But for our purposes here, as I understand it, the promise of quantum computing, as you described it as doing things in parallel instead of series, which basically leads to being able to do theoretically far, far more complex calculations much quicker than classical computing can do. Is that basically right?

I think that's the best way to describe it, yes. Okay. And so the promise of quantum computing is to be able to do that. I don't think anybody is saying that quantum computers are necessarily going to be good at everything. And so it's not like we end up some number of years from now with exclusively quantum computers. There are things that they will be particularly good at, potentially, and things that they may not be as good at. Can you kind of outline at the high level? What defines something that we think a quantum computer might be particularly well suited to solving?

There's a couple of different classifications of things that quantum computer is really good at solving, but most problems that is going to solve are problems you can boil down to an algorithm or an equation that has massive numbers of permutations that if you try to go through that brute force method, you won't ever get there within any meaningful human timescales. And so these problems are typically problems of the physical world, of chemistry, of biology, of pharmacy. They are problems of optimization. They are problems of solving really complicated mathematics around factorization,

other things that we only have algorithms to solve that through a brute force method of trial and error. And so if you can take something that historically has been a trial and error type brute force method and do things in parallel, that's a really great problem for a quantum computer. Do I need it to use on my cell phone to play whatever game I'm going to do or in an Xbox or PlayStation? No, I don't need it for that type of thing. And you may not use it for that type of application. And if you look at the structures of quantum computers,

they may not fit into a personal device or into a home, at least in the near term. But we said that about computers back in the day, famously, about that they were just going to be supercomputers, and now we have them sitting in our pockets. And so I can't predict the future for that, but you don't necessarily need them for day-to-day problems versus the really, really hard to solve computationally challenging problems.

I like the frame. This isn't true of every problem that a quantum computer is good at solving, but I like the explanation of replacing trial and error, anywhere where there's trial and error, and there are a million, million, zillion configurations of that trial and error, then it's a good fit for quantum computing. All right, so we'll come back to you alluded to, I think, or you prefaced some of the applications

that might be applicable in a climate context. We'll come back to those in a minute, but first let's just quickly talk about where we're at in the trajectory of quantum computing. What is the current state of the technology today and sort of who are the big players? So we are in an era called the NISC era for quantum computing, the noisy intermediate scale quantum computers.

that is we have quantum computers that work, but they're very, very small in the number of bits that they have. And so we are no longer in a realm of theory, whether we can make a quantum computer or whether or not a quantum computer can do something that a classical computer cannot do or they can do it faster than what a classical computer can do. So we have crossed that threshold. Actually, that's only been crossed in the last couple of years. So we are in a really, really interesting time where we have working quantum computers, but they're early, they're small, and they lack a lot of power and usability and functionality.

And so people are just starting to be able to play with these and test these types of computers. And the way we measure the power of a quantum computer today, there's several different types of methods, but we really talk about the number of quantum bits or qubits in a computer. And the most recent announcement actually came from IBM

announcing the first ever three-digit, or north of three-digit cubits in their quantum computer. So they have the largest functioning quantum computer that's been announced in the space. But they are right there, sort of neck and neck

with a lot of other major players in the space that can be grouped into a couple of different categories. You've got sort of the big companies that are going after this. So this is IBM, Honeywell, Google, Microsoft, to name a few, all have major quantum computing efforts that they're going down. And they're all somewhat in lockstep with one another and how they're pushing forward into this noisy, intermediate,

early stage quantum computing landscape. You then have some very well-funded startups in this space. Some are now public companies, or two big public companies. you have ion Q, which went public via SPAC this year. You have Raghetti quantum computing, which is in the process of going public. You have a couple of other earlier stage things, but have raised a decent amount of money in Cy Quantum and Zanadu, which all have aspirations of leapfrogging some of these big major players in the space.

But it is still early days, but the really exciting thing is we've got tools to play with. So is there a, I'm thinking about it in the context of like nuclear fusion, right? There's a, in nuclear fusion, you know, it's sort of, it's always been this like, well, it's 10 years out, it's 10 years out. But you can hit certain milestones that you can make, that are pretty clear on the path to like getting to a commercial fusion reactor. In the case of nuclear fusion, it is Q equals one, which is energy break even.

There's like this seminal moment that. that we're gearing toward that may or may not get hit by one or a number of the players in the next few years. And that's one milestone on the path. Is there anything like that with quantum computing? Is there a clear milestone? That's a great question. We theoretically have hit that Q equals one with quantum computing already with a Google experiment that was done a couple years back, which proved that they did a calculation on a quantum computer.

faster than what you could do classically. That's sort of that Q equals one moment. Now, the detractors of that experiment said, well, this is a made-up mathematical problem that has no use whatsoever that it was designed to be successful within this space. In my opinion, I'm very excited about something like that because who cares?

It did something that nothing else could do on Earth at that time. And they proved that it was a really interesting a sort of moment for the quantum computing space. And to take your fusion analogy one step further, Q equals one is incredibly important for a fusion reactor, but if that Q equals one comes at a cost of $20 per kilowatt hour, it is still useless for us as a society because it's too expensive. And so that's the analogy of where we are right now with quantum computing.

We have that moment, but that thing that it's done, not all that helpful, right? But I will say there are use cases today, and this is another way of how this industry is going to evolve. It is not going to be on or off. It's not black and white, whether we have something that's useful or not. Quantum computing is going to roll out via a continuum of success from sort of easier problems, less impact, to harder problems, greater impact over time as the compute power gets faster and faster and faster. And relating this to something that people may know of with classical computing, think about the power going back to video games, a resolution on a video game, or the Bit Wars back with Nintendo and Sega and all those early days. You got more and more and more powerful.

You could do more and more things with a video game. And Madden 2020 looks far different than TechMobile back in the day. And a quantum computer is going to have that same sort of trajectory over time where it's able to do more powerful and have more impact. on our problems. All right. So the salient question at hand for us is not just will we have quantum computers

and will they be able to do things that classical computers will not. It is will they affect climate change, assuming that we actually get them that they can do things. And so I want to talk about the potential applications of quantum computing and climate. I think they fall into two high-level categories,

then there's some sub-categories. But the two high-level categories are impacts of quantum computing on power consumption of compute. And then, and that's one, and then the second is using quantum computers to solve problems that will have impacts on climate change. So let's talk about those two individually,

I think spend a little bit of time on the first and then a bunch of time on the second, which tell me if I'm wrong, but that feels like the right allocation of our time. I think that is exactly the right allocation of our time and acknowledging the fact that data is eating the world and the compute power behind that uses more and more and more of the Earth's energy

that we need to run our daily lives, which are all interconnected with the massive amount of data centers and compute power that we've grown so accustomed to. And if you think about that serial versus parallel approach to computing, if I'm doing something in serial, I'm working far, far harder to get to the endpoint versus doing something in parallel.

And so it's really going to depend on what the quantum computer looks like and the power of that to get to an actual number. So I can't sit here and tell you that a quantum computer is going to be X times or X percent more efficient than a classical computer because I don't know what that embodiment looks like. But if you think about how do you solve some of the problems that we are facing today and all the different supercomputing assets that we use to solve those problems, if I can do something orders of magnitude faster with a similar footprint and a similar energy infrastructure, I lower the energy of that compute by that same amount because the data centers are scoped in megawatts, right? In gigawatts.

They are not scoped by the amount of transistors that they have because power is so closely correlated with our ability to compute things. So if I can compute faster, I reduce the energy input for what I need to do all of that different work. And you can imagine a future landscape where you have classical data centers and quantum computing data centers working in concert, solving the problems they need to,

and you have an energy optimization that you can do to that to be able to lower total energy requirements of our global compute power. So I will say this one for me feels important, but sort of incremental, to be honest, from a climate perspective,

which is, you know, we do consume over what data centers represent like over 1% of global power consumption now, and growing, but not growing that much because energy efficiency of classical compute and of data centers has been improving pretty rapidly. And so, you know, Bitcoin aside, let's set crypto off to the side for a second, you know, I don't see this apocalyptic future in which our ever-growing hunger for data and computation turns out to be like a fundamental problem for the power system and for climate change. Certainly if you can

cut total power consumption from data centers by an order of magnitude, that has a meaningful impact on climate. But that feels to me to be, it's an improvement over what is already a pretty rapidly improving trajectory. I couldn't agree more. In fact, it's not only the fact that it is still a small percentage,

growing but small. But some of the companies at the, companies at the forefront of trying to get to zero carbon energy are some of the big companies that have data centers. It is the Amazon's, it's the Microsofts, it's the Googles. They are pushing far faster than other folks to be able to reduce the energy footprint. So there is a future, in my opinion, for those companies to get to not just net zero on a basis of using offsets or other things, but actually truly having zero carbon power. which would make that argument somewhat moot.

Now, it is still helpful to have lower energy for that because you have to build out less infrastructure and you can focus in other places. But I'm not nearly as excited about that impact on climate change as I am about the second one that we are about to talk about with enabling technologies. Yeah, agreed. To the extent that there is, I think,

a really strong climate argument for quantum computing, it comes in this next category that we should now transition to. Before we do, I will give one comparison, as I was doing some reading, I saw a comparison between, you could tell me whether this is actually apples to apples, but a comparison between a one particular quantum computer, the D-Wave 2000Q, and a summit supercomputer,

imagining that they could do fairly similar, you know, sort of degrees of complexity of computation, the D-Wave, the quantum computer consumes about 25 kilowatts of power, the summit supercomputer 13 megawatts. So that's four orders of magnitude, roughly, difference between the two. And from what I read and talked to about the D-Wave team, they're actually even better as far as what they're able to output. Their output from some of those problems are superior to the classical computer, which is really interesting. Now, it's incremental, it's small, but that could be really important for some of these things.

And so I think that's a great example of how this, and that's an early stage. We're still in this fuzzy, you know, early-states quantum computer. it's going to get better as these things get more efficient. Virtual power plants are becoming a reliable way for utilities to manage capacity, but enrolling devices is just the start. What really matters is confidence, knowing those resources will perform when dispatched and being able to prove it from the control room to the living room. Energy Hub's platform handles the full picture,

from near-real-time forecasting, locational dispatch, and the kind of rigorous verification that holds up when regulators, grid operators, or leadership ask, did it deliver? Easy enrollment creates momentum, proven performance builds trust. That's why more than 170 utilities rely on Energy Hub to manage over 2.5 million devices delivering 3.4 gigawatts of flexible capacity. See what that looks like at energy hub.com. Okay, so let's talk about the bigger stuff, at least from a climate perspective.

How you could use quantum computing to solve problems that will have a big impact. in climate theoretically. I saw a, there's an organization nonprofit that is sort of specifically at this, the intersection of quantum computing and climate called Q4 Climate. And they have a good sort of seminal paper on like all the ways quantum computing

could be applied to climate. They categorize it in four different areas. The fourth of which is the one that we've just been talking about energy efficiency. But the first three, they categorize as simulation, optimization, and sensing. and we've talked a little bit about sort of each of those

but you laid out the sort of categories of climate tech to me that you think could be impacted by quantum computing. So let's run through those starting with material science. Excellent. So if I think about

as a recovering material scientist, so that's what I did early part of my career for about a decade. I made new materials. I made new things that go into high performance. applications in medical and aerospace and automotive and energy. And so as an investor, I tend to look at the world through a material science lens. And material science, the things that make up everything

that we use around the planet are the core fundamental building blocks of what we have to solve to be able to address climate change. And the bold statement there is if you can't fundamentally fix the things that go into all the value chains around the planet on a carbon basis, it's going to be really hard to decarbonize downstream from that. And so one of my focuses as an investor is how do we solve things at the very, very top level of the inputs that go into everything that we make, do, and use? And what's brilliant about a quantum computer is because a quantum computer is based on quantum physics, it is able to simulate the physical world.

what it's going to be very, very good at. And so we do not have the capability currently with our classical computers to simulate anything more complicated than a two-body problem or a two-adam molecule. And we're really good at simulating hydrogen. So we can take that put into a computer and understand all the energy states and figure out how that hydrogen atom would interact with physical properties. You start getting bigger than that, maybe a lithium-hydrogen combination. We can we can do that fairly well. But as you get to more and more complicated molecules and all the different electrons that flow around those,

we have no idea how to truly simulate that perfectly to understand what that would look like. Now, we can use certain things. There's tools like density function theorem, and we have companies that are using artificial intelligence to help predict what properties can come out of materials, but it is imperfect. A quantum computer could simulate these things perfectly.

And what that means is, you know, in the future, could you imagine a world that's like, I want these properties out of a material, and I want it to have this footprint. You could put that into a quantum computer, and it could output something. These combinations of elements give you that solution. And to add to that, because we're talking about a climate context here,

you could specify your array of inputs that you'll allow, right? Because at the end of the day, what we're trying to say here is, if you're looking for the best combination of low carbon or zero carbon materials, you have a bunch of constraints on how you produce those materials and what you can use to produce those materials in order for them to be lower zero carbon. But then you also need to meet these sort of output characteristics of like this is the nature of the carbon fiber I'm trying to build or whatever it's going to be. So theoretically with the quantum computer, you can state both of those things. These are my input constraints. these are my output constraints, and then it will tell you your ideal formulation. Is that right? That is the future vision for what you could do

with a very powerful quantum computer. And what's exciting about that is if I think back to all the major true decarbonization efforts that we've had and the true successes we've had, which are solar batteries, you can put wind and composites and magnetics in that, they're all based off of fundamental material

science innovations. And unfortunately, you know, it typically takes decades, 20 to 30 years to go from a university idea of something into a full-scale commercialization because it's very, very challenging to be able to do material science research with PhD horsepower. It takes a very long time of doing that brute force parallel track. You've got some theories, but it is going through a scale up that takes a long time. And what if you could short circuit decades of that? that work by doing things and simulating what I call in silico, which is how do I figure out and do those experiments in a computer to be able to know what they might be, to be able to shrink down them the millions and billions and trillions of possibilities of combinations of things

down to something that's tractable and really supercharge material science to be a tool for climate change. So let's see if we can give a couple of relatively concrete, albeit theoretical examples of how this might work. I mean, you alluded, you talked about batteries. You know, is it true that as an example, I'm trying to design the ideal formulation for battery cathode materials at some point in the future for a specific application maybe, right, like electric aviation, say, or something like that? I currently can do that by the brute force method, which is I could take all the properties

of the cathode materials that I know about and test individual. other formulations out one by one, and that's long and slow. I can maybe go a little bit further. I can apply AI, and I can take the sort of known cathode material characteristics and known properties of other potential materials, and I can use that to predict, and that'll make me more precise in my testing. Or at some theoretical point in the future, I can plug all of the necessary parameters into a quantum computer, and it can tell me what cathode materials I should use.

is that about right for one potential application? 100%. If you know what properties that you want out of that cathode, it's not going to be a magical genie in a bottle to plug in a thing and it pops out an answer. But if you're able to define the qualities and boil down that problem into what is effectively still mathematics, mathematics is the core of all the different physics that we do,

you should be able to do that with a quantum computer to varying degrees of accuracy as the power that quantum computer increases and the quality of the question that you're asking it, which is very important. If you don't know all of the knowns going in, you're not going to get the quality answer out. So it's still limited to the capabilities of a human to define what that problem is,

but a quantum computer will be able to give you that answer, which is really, really exciting. And that works for things that are increment to say a better and better cathode, how do I remove cobalt? Or are we looking at wrong combinations of elements for what that cathode would be to improve something by 5, 10%, which can be important for lowering the cost or increasing energy density or charge times of batteries? Or it could be a fundamental new alloy that is stronger and lighter that allows us to have

better energy efficiency for cars. Or is it a room temperature superconductor that allows our transmission grid to be more effective at delivering electrons with less losses? Is it a better solar material that allows us to get something? All of these things are inherently possible to be able to explore with a quantum computer in the realm of material science. So it's fundamental new things

and improvements to the things we currently have today. But to bring it down to Earth, to your point, in order for that to happen, in order for any of those to happen, really, we, one, need dramatic improvements in quantum computing technology, but then two, we need to be able to input all the right information for it to be able to give us answers.

And that second part is a whole monumental task of its own. We couldn't just say, tell me how to build a better transmission line, right? So to your point, the question asking is key. That's right. All right. So material science is one category. Let's talk about biology, which I think is another interesting one. You said before that one of the core things quantum computers should be able to do better

is simulating reality, physical reality, as opposed to just creating, sort of complex algorithms to, I don't know, mimic portions of the characteristics of reality. So biology is obviously a place where that you would think would come to the fore. Absolutely. And it goes back to that same challenge we've been talking about, the power of exponentiation and the daunting task when you have these huge, huge number of permutations you're going through. And so when you think about biology, biology is dictated by DNA, by the genetics of whatever it is that we're working on. we talk about biology within climate change, we're talking about how do we make things with

life, either bacteria or fungus or algae, all the biofuel 1.0 movement that dominated sort of early stages of clean tech to the next generation of companies like Zymergen and Ginko that have gone public that are all trying to use the power of biology to make the things that we use around the planet. And that's important because not only the, does a barrel of oil fuel our cars and our planes, it is also what makes up almost everything that we use around the world. It's critical to going back to the materials. And as we transition away from fossil fuels,

we're going to need to find other ways to make things. And biology is also very, very slow historically in figuring out how do we make new materials. And it's also very, very expensive to do that. And so could you use a quantum computer to be able to solve problems on what material to make from life and then how to potentially alter the DNA of that microbe to be able to produce that thing? It is sort of supercharging what Zymergen and Ginkgo are doing, just like Zimmergen and Ginko supercharged the Generation 1.0 of those companies in Sola Zim and Amherst of the world.

So it is the next step in being able to provide another tool to our generation. our biology innovators to be able to solve those problems to bring new products to life. Okay, so let's give a concrete example again then. I'll let you pick one, but give an example of something we might be able to do using quantum computing in the field of biology for climate that we can't do today. I think one of the things that you could do with this, one of the biggest uses of a barrel of oil that I'd want to replace is our polymers. And so if I think about what our polymers made up of,

they're made up of monomers, the individual molecules that we chain together to make into this prolific thing that sits forever for the most part in our oceans and our landfills, but also is critical to our daily lives. And could you use a quantum computer to come up with better monomers that are more biodegradable, that have the same strength profile as a traditional polyolefin or engineered thermoplastic,

which is one of the main challenges with biomaterials is typically they're more expensive and they're not as good. That's just a bottom line of a lot of those different materials. And that comes down to the actual core thing of what it is that we're making. So could you use a quantum computer to discover a new set of materials that has sustainability into the calculation of what you want to make through biodegradability, through the footprint, it's made through biology, but it performs at a level of what we expect from some of our engineered polymer materials that we use in all those different applications

in automotive and aerospace in our daily lives. Yeah, big categories I think about in this world, what are we using? It's sort of all the things we're trying to use synthetic biology today to improve, which in the climate context, a lot of it is. petrochemicals and plastics, it's fuels, its proteins and food, like all of these things,

you know, we're sort of in the early days, even around just using synthetic biology, let alone quantum computing, accelerated synthetic biology. That's right. And to that point, the second example from this

is around enzymes and proteins. So what are the workhorses of how do we make and do things? Biology is like having built in catalysts inside of that to take one input, whether that be sugar, methane, CO2, and have an output that is usable. That's what we want life to do for us. And proteins are just sequences of amino acids. And what's really interesting is how those proteins fold onto one another. And that folding and that shape is something that we are getting better and better at predicting that.

Deep Mind actually just released some really interesting papers and how they can predict based on the sequence of amino acids, how a protein can fold, a quantum computer will be able to do that better. It just will. And so can we design the next generation of enzymes to help do all of this stuff through biology is really, really interesting to supercharge, again, what climate scientists can do.

It gives them a tool that they didn't have before to innovate at a pace that venture expects and that the world needs to be able to decarbonize faster. All right. So material science and biology, we talked about the third category, that you mentioned to me is optimization, which as I've been reading about

and talking to folks about quantum computing, in general, people talk about the traveling salesman problem a lot. Can you describe the traveling salesman problem and how it applies? So the traveling salesman problem is a problem that is currently unsolvable by classical compute in a time scale that is on the order of the universe.

So it's something very challenging to solve. But effectively, imagine I have a salesperson who needs to visit a certain number of clients and customers that are on a map. And as I want to optimize the amount of steps that my salesperson takes, I want them to take the most optimal route

through an ecosystem or through a landscape. Or if I have multiple salespeople, how do I organize that? As that problem scales, it is something that gets, so incredibly challenging that, again, because of exponentiation, we are unable to solve those problems. We can get close through our classical compute, but we can't prove whether we are at a local minimum or a global minimum for something. And that means we get an answer that we think

is right, but is there a better solution for that? There might be, and mathematically, we can't actually prove that. But because a classical computer is doing things through brute force and a quantum computer does things in parallel, it is a perfect problem for a quantum computer to potentially solve through being able to use less resources to do the same things that we do today, which is another main lever that we have in helping to solve climate change, is how do we optimize everything that we do. And so, a great example of this, which is actually being used today, is, and a D-Wave has some good papers on this, but other companies, are working on this as well, is imagine I'm a vending machine company that I have to fill up

massive amounts of vending machines with all my different trucks. And I've got to do that and recalculate every day because I have to deliver one thing to one vending machine. They need another. I literally run a simulation to figure out how do I do that most effectively. And today, a quantum computer is already better at doing that in certain subcases. Again, incrementally better, but it is better. And because some of our early tools, when we use a quantum computer in concert with a classical computer,

can solve some of these problems better. So optimization, where the biology and material science is really, really big, but it's also some of the hardest stuff, some of the latest stuff to come. Optimization is actually here today and is being used by a lot of computers, I'm sorry, by a lot of companies

to be able to help them solve some of their problems. So this is the one where I feel like it's actually easiest to picture the climate impacts if you just take what you just described the traveling salesperson problem or take the vending machine example and then apply it to the scale of Amazon. And just imagine that Amazon were able to,

you might imagine that Amazon's already doing this, but in fact, it's probably too difficult a problem for it to be perfect. If you imagine that Amazon were able to absolutely perfectly calculate the ideal way to store and deliver all of its goods across the world, right? And the fuel savings impact from that from Amazon delivery trucks and delivery vans alone would be substantial from a global climate change perspective. Amazon, UPS, FedEx, Uber and Lyft, fleet services, all of these different companies could use this tool to be able to optimize how we drive, how we move goods and services around the planet.

And that's really exciting from a standpoint of, knowing how much more energy we're going to need, anything we can do to reduce that burden is a massive benefit from a climate change perspective. All right, I think we've covered the major applications. I guess stepping back, as you think about this nexus of quantum computing and climate, it feels to me like what we're saying is that

the quantum computing could be a sort of foundational technology, almost like classical computing was, right? Pre-classical computing, we couldn't have done a ton of this stuff, or even pre-supercomputing. We couldn't have done a ton of the stuff that we do now, that has direct contribution to climate. But it's a foundational thing. It's more than it is a direct thing.

There's a time effect here, right? Which is we need to solve a lot of these big problems in climate pretty quickly, just so as not to overshoot our carbon budget. How do you think about the interplay between getting to the point where quantum computing actually really can have an impact that can be measured in gigatons, versus how long it's going to take us to actually get there? That's a great question that I think a lot of companies and investors are asking.

It is one that I'm getting increasingly confident that we have are starting to hit inflection points in our capabilities to make these computers and as well as the ecosystem around the quantum computing landscape is buzzing with activity. So it's not just, I named a bunch of the companies that are doing the hardware side of things. That is one piece of the puzzle. We need software companies. We need algorithm companies. We need use cases for this. There is a whole suite of quantum computing startup efforts as well as government support and big company dollars going into this. And it's going in it as an increasing pace because we are starting to show more and more traction on what we're

able to create. I wish I could give you a prediction on when I think all of these things will happen. but I have been surprised over the last couple of years in investing in this space of how much faster and how much better we are getting at using these computers. And so I am highly optimistic that within this decade for sure, we will have meaningful impacts from quantum computers. And I can imagine in the next three to five years,

we are going to see winners, emerge with very specific use cases that are going to shock the world and what they're able to do with a quantum computer. All right. Well, that leads me to my final question. Gun to your head, what is the first significant way in which a quantum computer is used to solve a problem that impacts climate change? I feel like I'm at least repeating myself.

I do think it is going to be those optimization problems, honestly. I really think it's going to be in transportation. I think it's going to be an optimization. because that problem is far more definable. It is easier. It takes a lower power quantum computer to solve some of those things. And you can do it along a continuum, which I think is really, really important. Where if I'm doing material science, I either have that material or I don't.

Either works or it doesn't. Where optimization, there are still levels of performance you can do. And so I'm really excited about the ability to continue to show tangible emissions reductions from transportation within a quantum computer. But I will hold out hope that somebody discovers a catalyst or something that solves a really, really interesting challenge in fertilizers, in making other different chemistries that lowers significantly the energy footprint or the output of that. But I've gone to my head, I'm going to stick with the transportation optimization examples that

that we see because there's already red crumbs for that today. Bread crumbs that must be picked up in the perfectly optimal order. That's exactly right. Yeah. That's exactly right. But what's interesting about the space right now is things have behaving fairly linearly. And I said on stage at a quantum computing conference that I got hounded afterwards a couple years ago that I'm like, I'm kind of bored here.

And the reason I said that was because things were moving along at the exact. exact cadence that people said they were. And that was, we've got a 10-kubit quantum computer, we have a 20-bit qubit quantum computer, we have a 50, and then we have 100. And those are all really, really small. We may need millions of qubits to be able to solve some of these really, really hard challenges. And if we go along at that rate, we're going to need to start getting to a Moore's Law

state pretty quickly. And what's really exciting about what's happening is there are several different architectures that are being pursued by both startups and, and large companies. And my belief is that we will have one, maybe two of those that start to emerge at a different trajectory than everyone else. There are folks to say there's not gonna be a winner

in this space as far as an architecture. I'm not one of those people. I do believe, much like we have with classical computing and a lot of other hardware, there is going to be a winning quantum computing architecture that scales faster and better than others. And then they're going to be potentially, but likely inferior technologies that are out there.

And I feel like the next couple of years, we're going to see companies emerge with capabilities that nobody else can match and are going to have a hard time catching up to. All right. Well, we'll have you back on to verify that all this stuff happened in a few years. I hope so because it's a funny thing to talk about

quantum community and climate change. Even for me, if somebody's been looking at this for a while, but I always tell people the bold statement is if I had a magic wand that can make one thing true to help solve climate, I'm making a fully fault tolerant,

powerful, massive quantum computer because it is the equivalent of, if I have one wish for a genie, I'm wishing for infinite wishes. That's the analogy I like to tell people. And so I'm really hopeful that we're able to continue and increase the speed of innovation in the space.

I think you and I first talked about quantum computing climate like a year and a half ago, and you said that to me then, and that is what has been rattling around in my brain ever since. So I'm happy we were able to hash it out. I think we got to, I feel like I'm at 15% now. I feel like I jumped. I really appreciate it. And it's always a tough topic to talk about and to address because of the complexity and the weirdness of it. But it has really, really strong, tangible analogies and implications that I think when we speak about these applications of a quantum computer, I think it brings a little bit more reality to the entire space.

So I appreciate you having me on. Thanks so much for coming. Mark Kuppa is a partner at Prelude Ventures. Catalyst is hosted by me, Shale Khan. The show is a co-production of PostScript Media and Canary Media. Find me, Canary, and PostScript, all on Twitter. Tag us if you want to provide feedback on this episode or you want to suggest future topics. We appreciate those suggestions.

You can find links for this episode's topic and guest in the show notes, or you can go to canarymedia.com. Our producers are Daniel Waldorf and Stephen Lacey. Sean Marquan composed our theme song. Mixing and scoring was done by Eber Pinyeru. I'm Shail Khan, and this is Catalyst.