Quantum computing 101: What it is and why it matters for energy

This episode dives deep into the world of quantum computing and its transformative power beyond classical computing methods. Understanding quantum properties like superposition and entanglement not only excites but also invites practical applications in various fields, especially energy.

• Exploring the foundational principles of quantum computing 
• Comparing classical computing to quantum computing 
• Discussing real-world applications, particularly in energy sectors 

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Chapters

• 0:00 Introduction
• 0:25 Guest Introduction
• 0:59 What Is Quantum Computing?
• 2:40 Classical vs. Quantum Computing
• 3:30 Key Quantum Computing Principles
• 8:34 Real-World Applications of Quantum Computing
• 9:01 Quantum Computing in the Energy & Utilities Sector
• 12:12 Quantum Simulations & Energy Innovations
• 15:22 Final Thoughts & Where to Learn More

Transcript

Maida Zahid: 0:00

Hi everybody, welcome back to another episode of the Energy Transition Talks podcast. I'm your host, maida Zahid, and I'm part of the marketing team here at CGI Canada. Today we're going to be diving into the fascinating world of quantum computing with Curtis Naibo. Curtis is our talented Canadian expert who leads quantum computing here in Canada. And over to you, curtis.

Curtis Nybo: 0:25

Hi everybody. My name is Curtis Naibel. I'm a director of AI and quantum computing at CGI and I've been spending most of my time in the AI space and quantum computing space providing solutions to clients and building these out in production.

Maida Zahid: 0:44

Thanks, curtis. Well, very excited to have you here today, and this is a really cool topic and very abstract, for sure. So before, like as we get into it, can you kick it off by telling us what quantum computing even is?

Curtis Nybo: 0:59

Yeah, absolutely. So it is quite abstract and it's a very kind of exciting field. I get really pumped up about it when I talk about it and as I go through it. Feel free to interject and ask any questions. But what we'll probably do is compare it to classical computing and go through what the difference is between quantum and classical. So quantum computing most people have probably heard of it, but it is a relatively new field that leverages the principles of quantum mechanics to process information in fundamentally new ways. So, although it's actually not really new, it's been around since, I think like 1981, where Richard Feynman and Paul Benioff, two well-renowned physicists, started thinking about how to simulate quantum phenomena in a more accurate way than using conventional computing models, and so they kind of built the framework for quantum computing, and then they shortly realized that it's actually quite good at the usual computational problems that we try and solve today in addition to simulating that quantum phenomena.

Curtis Nybo: 2:01

So to really understand the difference between classical and quantum computers, let's start with where the classical computers are and how they work. So a classical computer it processes information using bits, and these bits are in one of two states. So you got your binary digits, zeros and ones. Every operation is performed using this binary logic, and so the way these zero and ones actually occur is it's literally flipping a switch in your CPU or applying a voltage to a switch in a CPU to go between these two states to execute calculations. So you're getting your usual zero and ones when you're using your classical computers.

Curtis Nybo: 2:40

A quantum computer, on the other hand, is pretty similar, but it doesn't use classical bits. They use what's called a quantum bit or a qubit, and so quantum bits, they take advantage of several quantum mechanical phenomena that exist within the realm of quantum mechanics, and so we'll talk about three specific ones today for our understanding. But it's important to note that at a high level, qubits are just. They're not restricted to just a zero or one, like a classic bit is. Instead, they can exist in a position called superposition, which allows them to be in state simultaneously. They can they take advantage of what's called entanglement, which allows them to kind of correlate from with each other over long distances, and they also a big part of it is also the interference between those qubits. So we'll talk about all three of these really quickly, just to set the stage of what's different.

Curtis Nybo: 3:30

So we talked about how a classical computer has a bit that's in a zero or one state, no matter what, it's just a switch flipping on and off, essentially. But in a quantum system it can be in what's called the state of superposition, where they can be in a state of both zero and one simultaneously. So if you imagine flipping a coin, a classical computer, it's either going to be heads or tails, but in a quantum system it's like the coin is spinning in the air, it's existing in both heads and tails at the same time. Until you observe it, or until you knock it over and you essentially measure it. You measure the value of that bit, and so for it'll either come up as heads or tails or with a qubit, it'll either be zero or one. So its ability to exist in multiple states at once really allow it to exist compute problems in parallel. So instead of processing just one variable at a time like a classical bit, a qubit in superposition can process multiple values simultaneously. So if you have two qubits, they can represent four possible states at the same time. You're 0, 0, 0, 1, 1, 0, 1, 1. With three qubits you get eight states, because they can be in all those states at the same time. So you get n qubits with two to the n states. Just to kind of narrow this down a little bit further, if you say you're in a maze that has three decision points, a classical computer needs to check two to the three possible paths, so eight possible paths one by one, whereas a quantum computer, with the same three bits but qubits, can explore all eight paths simultaneously due to superposition. So this allows for exponential scaling in some circumstances, and it gives quantum computers the potential for solving problems that would take a classical computer potentially millions of years, and that's a really important, important aspect of it, and it also ties into we talked about superposition.

Curtis Nybo: 5:17

The second quantum phenomenon that allows quantum computers to be so powerful is called entanglement, and so it's one of the more bizarre properties. But when, essentially when two qubits become entangled, their states become intrinsically linked. So no matter how far apart they are, if you measure one qubit, if you measure the state of one qubit, the state of the other is also instantly determined. So even if it's 100 light years away, it will still be determined, and so this allows quantum computers to perform coordinated computations across multiple qubits without direct communication, which is something that classical computers really, really can't do, and so an example of that would be going using kind of a flipping a coin.

Curtis Nybo: 5:56

Again, an example of entanglement would be say, we're um, maida has a coin, I have a coin. We're separated by, you know, 800 miles. If we both start spinning the coin, each coin that's spinning exists in a superposition of heads and tails. Beforehand we entangled the coins, so now they kind of are correlated with each other. So we each have our entangled coins. When I check my coin and I see that it lands on heads, the other coin instantly also would land on heads that Mida has, no matter how far apart they are. So the key difference is that neither coin had a definite state. It was neither a zero or a one while it was spinning. Only upon measurement does that state actually lock in. But my state would correlate with the one of my does, no matter how far, no matter how big the distance is. And this is actually quite fascinating because that's what Einstein called spooky action at a distance, because when they discovered it it was quite a counterintuitive type of process. But some like to.

Curtis Nybo: 6:53

There's a common misunderstanding out there that this allows for faster than light travel, but it really doesn't. So while the effect seems instantaneous, there's no actual information being transmitted between the particles, in no way, especially that it violates relativity. So it can't travel faster than light. And so the reason is that you can't control what outcome you get. When you measure the first particle it's still a 50-50 result. But what happens is that once you compare the results using regular communication if I was to phone MITA and say what did you get, it would be the same. So those particles are always correlated. The measurements, the results we get from the measurements, are always correlated.

Curtis Nybo: 7:31

So we've talked about superposition, we've talked about entanglement, and the third thing is kind of bringing a little bit of them all together.

Curtis Nybo: 7:37

So quantum computing isn't just throwing qubits into superposition, you know, and hoping they do the best. Quantum interference plays a pretty critical role in ensuring that quantum computers reach that right answer as efficiently as possible. So they interfere, they're able to communicate with each other. They're trying to cancel out the wrong ones and amplify the probabilities of the correct ones. So if you think of throwing a pebble into a pond, the probabilities of the correct ones. So if you think of throwing a pebble into a pond, the waves can interfere constructively, so they can amplify each other, or they can interfere destructively and they can cancel each other out. So quantum computers use that interference in a similar way to enhance the probability of a correct answer, while still canceling out incorrect answers, and so this is a pretty crucial aspect of it for certain algorithms that take advantage of this aspect. But overall we've talked about superposition, entanglement and interference, and so hopefully Maida, that was a lot, hopefully it made a little bit of sense.

Maida Zahid: 8:34

That was a lot, but it was very fascinating. I think the theory behind it is very fascinating and definitely very applicable. So that takes me to my next question is can you help take that theory and apply it to real world use cases, and what are you seeing in your work? What industries, where are we applying these Especially? Let's start with, let's say, the energy utility sector. What are some of the common use cases?

Curtis Nybo: 9:01

So, like I said, we've talked about how the quantum computer differs from the classical computer. Before talking about the actual use cases, it is important to note that there's different types of quantum computers and they each apply to different use cases. So they're usually split between quantum annealers and gate-based quantum computers. So quantum annealers are quantum computers that use quantum fluctuations to find the global minimum of a given objective function. So it tries to find the lowest energy state of an energy landscape. So this works off of a fundamental rule in physics that everything seeks a minimum energy state a rock falling to the ground or water running downhill losing gravitational potential, energy Electrons in atoms seeking a lower energy state by emitting a photon, planets that settle into stable orbits by gravity pulling them into the lowest possible energy orbit. Everything in physics seeks a minimum energy state, and so a quantum annealing quantum computer tries to take advantage of that, and so we try to formulate a problem and translate it into an energy minimization problem, and then we're able to use some interesting quantum phenomena to be able to interact with that problem. So quantum annealers can also use things like quantum tunneling, which is another phenomenon where a particle can pass through an energy barrier if it doesn't have enough classical energy to overcome it. So if a ball is rolling downhill and doesn't have enough energy to climb the other side, it'll roll back down, whereas a quantum annealing computer has the capability to essentially have that ball tunnel through that local minima of the problem of the hill, to keep continuing down the hill into a smaller energy or a lower energy state. And then the second one is the gate-based quantum computers that I mentioned. Second one is the gate-based quantum computers that I mentioned before. So a gate-based quantum computer is more similar to our current computers that we use, that are using, like our classical computer will use logic gates. And so a quantum computer that's gate-based has the similar type of logic gates, but they're built to handle qubits. And through using qubits and providing transformations through gates on those qubits, they can take advantage of the same quantum phenomena that we just talked about before. And so gate-based quantum computers, they require some different error correction techniques, but they're much more universal, whereas a quantum annealer is mainly geared towards optimization problems. So when we talk about finding that local minima, that local, that small lowest energy state, we're to optimize a problem, whereas a gate-based quantum computer is a lot more universal. It can tackle your quantum, your um, your cryptography problems. It can do simulation problems. It essentially can handle a lot more variety in computational problems. So when we talk since we've now you know there's a little bit of a difference between quantum computers. Why quantum computing matters for the energy and utility sector? Well, because they have this unique ability to take advantage of these quantum phenomena. They can also directly simulate quantum mechanical systems.

Curtis Nybo: 12:12

So this is something that when I talked about Richard Feynman earlier in the 80s, this was the original purpose of quantum computers. So there's there's a equation called the Schrodinger equation, which is kind of the fundamental equation in quantum mechanics that describes how quantum systems behave. And so when they tried to initially solve the Schrodinger equation for certain molecules, which you know encompasses all the information of that molecule, classical computers have a lot of trouble, especially as the molecule gets larger. And so quantum computers can actually solve the Schrodinger equation for quite complex molecules. And this is crucial for the developments in battery development, drug discovery, material science, that sort of thing. And so when we're trying to model, when scientists are trying to model the atoms of like hydrogen or helium, a classical computer can do that pretty good, but it uses a lot of approximations. So when we're using approximations we're taking a trade-off of compute time for accuracy of the result, whereas a quantum computer doesn't need to necessarily have that trade-off. It can accurately model these atoms and these molecules through the shortening equation without having to do that trade-off of putting in a bunch of assumptions to try and make that calculation actually be able to run. And so the amount of information required to model these molecules scales exponentially with the number of electrons. So it just gets quite complicated. And whereas quantum computers offer that new approach to mimic nature at a quantum level that allows for exact solutions, you know, to that Schrodinger equation to actually be solved.

Curtis Nybo: 13:56

And so this leads to, especially in the space of energy and utilities, the discovery of new battery materials, so we can simulate lithium, we can simulate sodium and different battery chemistries at an atomic level, things we've never been able to do before, that have taken a huge amount of computational power to do.

Curtis Nybo: 14:14

It enables discovery of high density materials, better electrode stability. Overall. We're seeing a lot of use in especially the automotive use case, as when it comes to energy storage, not only for electric cars, where the car manufacturers are really leaning into using quantum to solve these problems, but also things like renewable energy, where the wind might not be blowing as hard one day. We need to be able to store that energy and be able to disperse it at a later time, and that's where a lot of this the new battery material comes in. Another one is hydrogen fuel cells. So quantum simulations have been known and been used to identify catalysts for those hydrogen fuel cells, and same with solar panels better discovery of the photovoltaic materials that are used within those solar panels. When it comes to oil and gas and coal that carbon capture technology we're able to model those molecules and how they behave and how we can successfully harness those. So overall it's pretty applicable to a lot of different areas in energy and utilities. But I should say it's not a silver bullet, it's not magic.

Maida Zahid: 15:22

Well, thank you for your time, Curtis, and thanks everybody for listening. You can find the rest of the episodes in our series on cgicom and you can subscribe to our podcast on Apple Podcasts and Spotify or wherever you get your podcast from. Thanks very much.