In this episode of our Transactions to Trust podcast, host Maida Zahid and Curtis Nybo, Director of AI and Quantum at CGI, explore how quantum computing is reshaping financial services and what leaders can do today to prepare for measurable impact.
While often perceived as futuristic, quantum computing is a present-day reality. It is not an incremental improvement on existing systems but, as Curtis Nybo explains, "a fundamentally new way of processing information."
Unlike classical computers that use bits (0s or 1s), quantum computers use qubits. Through superposition and entanglement, qubits evaluate multiple possibilities simultaneously, enabling financial institutions to tackle optimization and simulation problems that grow exceptionally in complexity.
Quantum systems will not replace classical infrastructure. Instead, they will augment it, solving high-complexity challenges while classical systems continue handling transactional workloads efficiently. "They're not better at everything," notes Nybo. "For most everyday tasks, even things like processing transactions, running databases, sending emails, classical systems...will remain the most efficient." The true power of quantum computing lies in solving problems whose complexity grows exponentially.
Financial institutions continuously manage large-scale optimization and simulation challenges. As Curtis explains, quantum capabilities introduce new opportunities across three horizons:
- Near-term: Enhance portfolio optimization and fraud detection using hybrid quantum-classical models that identify complex patterns across vast datasets.
- Mid-term: Accelerate Monte Carlo simulations to price derivatives and measure risk faster, improving responsiveness to volatile markets.
- Long-term: Enable real-time global portfolio optimization and systemic risk modeling at scale, beyond today’s computational limits.
Quantum computing introduces a significant security consideration. "Harvest now, decrypt later" attacks allow malicious actors to store encrypted data in anticipation of future quantum decryption capabilities.
For financial institutions managing long-lived sensitive data, this creates urgency to begin transitioning to post-quantum cryptography (PQC).
Preparing now helps safeguard client trust, regulatory compliance and institutional resilience.
The era of quantum advantage is emerging. With noisy intermediate-scale quantum (NISQ) systems already available via the cloud, institutions can begin experimentation today.
Leaders can take pragmatic steps:
- Educate leadership to distinguish hype from measurable opportunity
- Start with focused pilots to validate high-value use cases
- Develop internal talent through targeted learning initiatives
- Prepare architecture to integrate cloud-based quantum services
- Plan for PQC migration to mitigate long-term cryptographic risk
As Curtis concludes, the journey begins with knowledge and a strategic plan. By acting now, financial institutions can move beyond speculation and begin building a practical, outcomes-driven path into the quantum era.
MAIDA ZAHID:
Hi everybody, and welcome to another episode of our new podcast from Transactions to Trust, a financial services podcast. I'm your host, Maida, and I'm part of the marketing team here at CGI Canada. And today we're diving deeper into quantum computing, what it really is, why it matters, and how it could change the way financial institutions operate. And to help us unpack all of this, today we are joined again by our expert, Curtis Nybo. Curtis is the director of AI and Quantum, and he leads the quantum practice here at CGI. Welcome, Curtis.
CURTIS NYBO:
Hey everyone. Glad to be back. As Maida said, my name's Curtis Nybo. I'm a director of quantum computing at CGI, and I lead our global quantum computing practice where we really focus on where we can apply real quantum solutions today. There's a lot going on in research, there's a lot going on in the news. So, what's real? What can companies actually leverage? And how can we actually implement quantum in the state that it's in today? And it gets better and better every day. So where can we find those advantages? Especially when it comes to finance and banking. My background is actually in finance. I did a Master of Quantitative Finance, and then I did a master's of Astrophysics. So, I kind of have the best of both worlds from an academic sense. And then like I said, with CGI quantum, we try and spend our time finding ways to be able to apply that to industry. So happy to be here.
MAIDA ZAHID:
Very exciting. And talk about smarty pants, but let's get into it. So, as you said, you know, quantum computing, astrophysics, and finance, and when people hear quantum computing, it sounds very futuristic, I'm not going to lie. Can you help us understand what is it really, and especially why should financial institutions care?
CURTIS NYBO:
Yeah, absolutely. So, quantum computing is really interesting and it's an emerging technology at its core. I'm a part of the emerging technologies practice within Western Canada as well, where we focus on trying to find the newest and greatest technologies. And so, quantum computing fits into that because it really is emerging. It's got a ways to go in a lot of aspects, but the state that it's in today is real. And so, what quantum computing is, to just give a brief introduction into this topic for those that might be new to it. To kind of break it down, quantum computing, a good way to think about it, is a fundamentally new way of processing information. It's not AI, it's not an algorithm, it's a fundamentally new way of doing computation.
So, it's built on physics that govern how nature behaves at very small scales, which is quantum physics. And so, to understand quantum computing, it helps to start with how it relates to the systems we use every day, like classical computers. So, our classical computers run on bits. A bit can be either a zero or a one. It's pretty deterministic, and all modern computing is built on manipulating these enormous strings of zeros and ones. And so, a quantum classical computer, those bits are either in a state of zero or one. They can't be kind of halfway. They're literally, you know, they used to be transistors, I think they're just voltage levels now inside those CPUs, but they have to be either a zero or a one. And so, what quantum computing does is introduce a new unit or a new way to represent that information, to represent those bits. And it's called a quantum bit or a qubit. And so, unlike a classical bit, these qubits they exist in a state that we call superposition. You might have heard about this just from reading, which really means that these qubits can represent a zero and one at the same time until it's measured. And so, it might sound abstract, but really it just means that in practice a quantum system can represent many possible combinations of values simultaneously.
So, really a good way to think about it, quantum computing as well, is very energy efficient compute for solving very hard problems because it can represent many possible combinations somewhat and you can think about it at the same time. And so, superposition isn't the only thing. There's a bunch of different aspects of quantum physics that we take advantage of, but superposition and entanglement are the two that are the big ones. And so, entanglement links qubits together in such a way that the state of one qubit is mathematically connected to the state of another, basically, whether it's a zero or a one. And so that relationship holds no matter how complex a system becomes, and it really creates an exponentially expanding state space as more qubits are added.
So, a good way to visualize this is if you have 10 classical bits, you can represent one combination of two to the 10 zeros and ones at the same time. But if you have 10 qubits, you can represent two to the 10 possible states simultaneously. So, you can basically explore a solution space much faster than a classical computer using superposition and entanglement.
And so that doesn't mean that quantum computers try every answer at once and magically pick the right one. There's some work being done kind of under the hood on these quantum processors, which basically try and manipulate the probabilities of it being in a zero or a one in a way that increases the likelihood of measuring the correct answer once you measure it, while also suppressing the incorrect ones. And so, it's really more accurate to say that quantum computing is really an exercise about in interference and probability engineering.
You're not trying to brute force parallelize these computations. But really, that's why quantum computers work well with classical computers, and they won't necessarily replace classical computers anytime soon. They're not better at everything. For most everyday tasks, even things like processing transactions, running databases, sending emails, classical systems are built for that. They're very efficient at that, and they probably will remain the most efficient at those tasks.
But where we look to quantum computing, this area shows promise in solving certain categories of problems that grow exponentially in complexity. So, your large-scale optimization, your high-dimensional simulations, your cryptographic factorization, complex probabilistic modeling, these are all areas that quantum computers are very good at. And that's exactly why financial services and banking institutions are paying attention to quantum computing, because in banking and capital markets, we deal with optimization problems like portfolio allocation, your liquidity management, even abstract derivative pricing models. In these spaces, the solutions, the possible solutions grow explosively as the constraints and the variables increase. And so, this is a great area to be able to apply quantum computing. So, really, quantum computing, I like to say, it's not about faster spreadsheets or incremental efficiency gains. It's a completely new way of being able to tackle complex problems that might not be even possible to tackle today with classical computing. So, the real takeaway is that quantum computing is a completely new way of computing and it can potentially be much faster. It can potentially be more energy-efficient, and it can be potentially safer from a cryptographic standpoint as well. So hopefully that gives a bit of an intro into quantum computing and why it applies to finance and banking, because we want to tackle these complex problems.
MAIDA ZAHID:
Yeah, definitely. There are lots of advantages to be taken care of. And it seems like we have a big opportunity in front of us. And you kind of touched on why financial services is one of the industries most closely watching. And you talked about a few use cases, as well. But can we get a little bit into what are some realistic quantum use cases in banking? Can we talk near-term, midterm, long-term? Like what does the timeline sort of look like?
CURTIS NYBO:
Yeah, exactly. That's a good place to start with use cases when you look at it from near-term, midterm, long term, because again, it's an emerging technology. It's still growing and it's still becoming more and more mature to be able to put into practice. So, a lot of people often think about the amount of data that financial institutions have, and that is a part of it, why quantum computing is helpful, but it's less about the amount of data, and it's more about, again, the complexity of the problems to be solved. We want problems with tons of solutions, they have tons of variables, they have tons of constraints, and the possible solution space becomes huge.
And classical computers just straight up struggle to be able to work through all those solutions one at a time versus a quantum computer that can explore those somewhat simultaneously. And so, you were asking about use cases, right? Specifically, Maida. So, optimization problems are a big use case. So, portfolio optimization, asset allocation, liquidity management, trade routing, overall capital optimization are good use cases. There's also outside of optimization, there's risk and simulation.
So, being able to use quantum computers to do better Monte Carlo simulations. And actually, that's a really good example of a use case in quantum computing where Monte Carlo simulations are widely used to price derivatives as well as measure risks. You're running millions of scenarios trying to achieve the most accurate result, basically for a scenario that you can't measure directly. So you’ve got to run tons of simulations and you try and average the outcomes, you try and see through running millions of simulations where you're most likely to end up in that simulation, and hopefully where you'd end up in the real world to be able to kind of predict some of those features. And so, Monte Carlo simulations, they consume a huge amount of computing time as well as computing cost. And so, quantum methods have the potential to reduce the number of simulations needed, and hopefully still be able to reach the same level of accuracy.
And not only is it necessarily a benefit of accuracy that you'll get, you might get a benefit of speed. You might get a similar result in accuracy, but you got it, you know, a hundred times faster. Or it used a hundred times less energy. And I'm just saying a hundred times, but it used a lot less energy, or it got to that solution a lot faster. Or you might have got better accuracy as well.
And so, when you think about Monte Carlo simulations, for example, for an example, it really would help with accelerating pricing, your capital calculations, your risk measurement processes. You could run more detailed models in less time, responding faster to market changes. All those types of things really play a large part in quantum computing.
There's also the cryptographic risk, and that's more of a near-term issue. And I think we have a podcast coming up on that, but we can talk about that maybe a little bit briefly later. But really, what is the way I would sort these into near-term, mid-term, and long-term for use cases is a really important way to put it, because there's a lot of academic research going on into quantum. And not all use cases can be put into production today, but they're rapidly approaching that. And there are a few that we can put into production today.
And a good one, a good example of that that always comes to the top of my mind is portfolio optimization. So, when we're doing your portfolio optimization today, it can take a long time. You might take a long time to have to rebalance those portfolios, but with quantum portfolio optimization, we can encode these portfolio decisions, the asset types, the balance of risk and return metrics into these quantum models, and we can test them to see where quantum computers work alongside classical computers. So, we could implement right now a quantum hybrid model with a quantum classical type of approach, where the quantum component can evaluate a possible portfolio combination. It could do that potentially faster, or you could use it to explore a larger portfolio space. And you could use the quantum component to evaluate the possible portfolio combinations while the classical system refines and interprets the results and feeds it to your dashboard.
So really we're starting to see an intersection where we can use quantum computing as part of a larger process or part of an AI process where we can implement it into an AI agent where the agent knows an optimization needs to be performed, passes that to the quantum computer, it then gets the result from that and continues on in the process. So, we're probably not we're not really seeing the replacement of traditional optimization tools right now as it is, but we're starting to see a lot of augmentation to the existing classical tools with quantum optimization, areas where quantum optimization can be applied to be able to derive a benefit.
Another area in the near term is risk model research. So, modeling large data sets, complex correlation calculations. This is where quantum research has been really focusing on the large mathematical relationships between all the different risk variables. And so, understanding whether quantum tools could enable faster recalculation of exposures, better identification of hidden risk. Those are the main areas for risk model research to be able to apply quantum computing.
Another area is fraud detection. So, in your transaction networks, they're massive, they're complex. We can use quantum computers to be able to recognize some of those complex patterns and relationships that might be difficult for traditional AI or classical systems to detect efficiently. So, there's a lot of work being done in fraud detection in quantum computing.
I would say in the midterm, we're looking at those faster Monte Carlo type of simulations. So, running those millions of scenarios a little bit quicker to hopefully get the same results. But we're also seeing some advantage in derivative pricing. There's been some use cases in the quantum space using quantum to provide better pricing around derivatives and different exotic derivatives that normally would require heavy computational modeling. We can use quantum computers to kind of recalibrate these prices more frequently as the market conditions change.
And then I think there's a lot of use cases in the long term, but the long term really focuses on expanding the use cases to become larger and larger. So, we start to tackle huge problems as we look to the long term, which might be five to 10 years away, where we're starting to use quantum computers for real-time global portfolio optimizations. You no longer have to make so many assumptions about your calculations or about your portfolios. You can model those largely global portfolios that include those complex constraints across all your asset classes, all your regions, incorporating constraints around all the regulations. And then you could rebalance those as markets change instead of having just a periodic rebalancing. And it would really represent a bit of a structural shift in the way we do asset management if we could be able to incorporate all that detail and remove those assumptions that we currently use in classical solutions.
And really lastly, I would say that the market-wide systemic risk calculations, being able to take in larger amounts of variables and information to be able to apply it to your calculations. So, I think that's where I would put it for the near use cases, the midterm use cases, and the long-term use cases.
MAIDA ZAHID:
Very nice. And thanks for kind of building that context out for us. It sounds like there's a lot happening and there's a lot being done at the same time. I liked when you mentioned the hybrid kind of quantum computing model. There's lots happening, you know, we had the people can start doing this. But one of the things you mentioned was encryption risk. And is there something banks should be worried about? And another thing I wanted to ask was is there a sense of urgency? Because things are happening so fast.
CURTIS NYBO:
Yeah, absolutely. And there is a huge sense of urgency. I think we'll have a dedicated podcast to the quantum encryption, the risk to quantum encryption, because it's just such a vast area. There's a lot of detail there. But really, in short, there is a major sense of urgency. We know that quantum computers can break RSA encryption and elliptic curve cryptography. Using Shore's algorithm, we know it can do that.
And so, it's just a matter of time until a quantum computer is available that can do that at scale. For now, RSA is safe. But the problem is that data can just be collected and stored. Storage is cheap, and the bad actors just have to sit on that data until a day arrives, which is called Q Day, when a quantum computer is large enough to be able to run Shore's algorithm to break the current encryption standard.
So, it's called a harvest now decrypt later attack. And I think we'll be diving into that in much more detail in our next segment. But the urgency is there, and it is something that financial institutions should be very knowledgeable about because their data that they store is long-term data. Criminals can hang on to that data for a long time and it'll still be valid by the time they're able to access it. So, there's a huge risk there, and that's the post-quantum cryptography topic. Yeah, it is kind of a world of its own as well.
But I will add that quantum computers aren't the cure to the threat from quantum computers. You don't need a quantum computer to implement post-quantum cryptography. So, I'll leave that with everyone.
MAIDA ZAHID:
Interesting. I like that, you know, lots to unpack here for sure. But yeah, we'll definitely feature more on that in another episode. But let's move on. There's a lot of hype around quantum, just like there was with AI. And I wanted to understand from your perspective, is it realistic, or are we still decades away? Because you talked about near-term, short-term, long-term. But right now, how do you see it? Is it realistic?
CURTIS NYBO:
Yep, I would 100% say it's realistic. So, we're approaching an area where we can take advantage of these noisy intermediate-scale quantum computers that we have today. The goal is to get to a fault-tolerant quantum computer where basically we have really good quality qubits. We can hold superposition states for a very long time, do long calculations. That's where every quantum computing provider is trying to get to.
And so right now we have noisy intermediate-scale quantum computers, which is kind of a middle ground of where we can start to apply these production use cases to solve these business challenges. And so, it is real. We can take advantage of that, especially in the optimization use cases. There's very strong use cases in production today. And I would just say that there's major investments from all the quantum computing firms to be able to get their computers to market. We're at the stage now where it's less about the research and it's more about how can we actually get our tools in the hands of users. So, lots of providers like D-Wave have done a fantastic job of building out their software so that their users can interact with their quantum computers. And that's really the first step. And that's really where it starts. And that's where we are today, where we can actually use these quantum computers today.
MAIDA ZAHID:
Nice, nice. Well, that helps us guide where we can go from here. But let's say if there's a CIO or CTO or chief risk officer listening, what should they actually be doing about quantum right now? Anything actionable?
CURTIS NYBO:
Yeah, there are a lot of actionable steps. And a lot of it has to do with similar to what you're probably doing with your AI approach. You're trying to educate leadership, you're trying to start small and identify use cases, you're developing the talent around that. And we can do the same thing with quantum computing. You can leverage a lot of the same frameworks you probably built out in your AI practices, where we want to educate leadership and build executive literacy around quantum computing. We want to be able to separate that hype from impact that we just talked about, what's real. And we want to have the team members within your organization knowledgeable about where quantum can potentially apply and where it could apply in the future.
And so, you would start small, identifying one to two high-value use cases. If you're really interested, you can build some partnerships with some universities, some vendors, um, CGI. And then you want to be able to build out those use cases by developing the talent to be able to do that. So, upskilling your teams, find those quantum-aware and quantum-enthusiastic team members within your organization and get them the tools and get them the access needed to be able to start building that talent.
And then really, it's not about buying hardware, it's about preparing your architecture and your teams to be able to utilize the hardware that already exists. And we can do that today. And then I will add again, as a risk officer, you're probably very interested in the cybersecurity risk. And so, beginning with your PQC or post-quantum cryptography planning as well, which again, we'll talk about in a in a later segment.
But really comes down to the education, the starting small, developing talent, and going from there within your organization. That's what I would be doing for quantum today, and that's what we're doing to help a lot of our clients.
MAIDA ZAHID:
Awesome. I like how you kind of started with knowledge, and knowledge sharing and educating leaders and tying in with how the AI hype cycle panned out as well. But thanks very much, Curtis. This was excellent. I think we covered a lot today and lots still to be covered. Thanks so much for joining us today.
CURTIS NYBO:
Yeah, thank you. Happy to be here.
MAIDA ZAHID:
Thanks, everybody. See you at the next one.
