At a time when quantum computing and AI are advancing at lightning speed, BMO is zeroing in on opportunities to merge the technologies and supercharge AI capabilities.  

The $1.5 trillion bank in April launched the BMO Institute for Applied Artificial Intelligence and Quantum, an enterprisewide initiative to accelerate innovation and deployment of the two technologies in a responsible manner.  

BMO Chief AI and Quantum Officer Kristin Milchanowski, who oversees the institute, is identifying synergies between quantum and AI in financial services, with a focus on “building capability and demonstrating the art of the possible,” she told FinAi News.  

“We’ve made progress in research and intellectual property, particularly in areas like quantum game theory and advanced anomaly detection, which connect directly to financial services problems,” Milchanowski said.

“I often point to external examples as well. For instance, there are already demonstrations where quantum systems have solved certain simulations thousands of times significantly faster than classical approaches.” 

While the use cases for quantum-AI convergence are still narrow and do not yet translate to broad practical advantage, “they give us a clear signal of where this is heading,” Milchanowski said. 

“For us, the priority is making sure we’re ready to translate that into business value as the technology matures,” she said.  

The global market for quantum AI is projected to hit $7.8 billion by 2035, up from $280 million in 2025, according to research company Research and Markets.  

Milchanowski recently sat down with FinAi News to discuss Toronto-based BMO’s new institute, quantum-AI use cases and how quantum fits into the bank’s AI strategy. What follows is an edited version of the conversation.  

FinAi News: What opportunities are advancing quantum technologies creating in tandem with AI deployment? How does the AI-quantum institute help BMO capitalize on these opportunities? 

Kristin Milchanowski: What we’re seeing is that AI is already transforming how we make decisions, but there are limits to how much complexity classical systems can handle. Quantum has the potential to expand those boundaries where classical computing has traditionally failed, supporting selected components of existing infrastructure. 

The opportunity is in areas where the problem space is too large and/or too dynamic for today’s approaches — optimization, simulation and risk modeling are good examples. Over time, quantum has the potential to allow us to explore far more possibilities and arrive at better outcomes more efficiently. 

That’s where the institute plays a critical role. It brings structure to how we approach this. We’re not chasing isolated experiments. We’re building enterprise readiness, aligning research to real business problems and ensuring we’re positioned to act when quantum advantage becomes commercially meaningful. 

FinAi: What are your primary goals for the AI-quantum institute over the next six to 12 months? 

KM: The next six to 12 months are about readiness and focus. We’re building the foundation, including governance, operating model and partnerships, so we can scale responsibly. 

Key partnerships allow us to engage constructively with leading organizations across Canada and the U.S. as the field continues to develop, while taking a responsible, informed approach grounded in collaboration, learning and readiness. As the first Canadian bank to access the IBM Quantum Network, utilization of the network will include accelerating the development of new approaches to help clients prosper and support business growth, optimizing investment portfolio strategies and uncovering deep insights into risk management solutions. Additionally, our partnerships with both Quantum Industry Canada and Chicago Quantum Exchange are designed to support knowledge sharing, workforce development and dialogue across academia, industry and policy communities.

Equally important, we’re investing in talent and ecosystem relationships. Quantum and AI require different ways of thinking and programming, so building those capabilities now is critical. 

When it comes to Quantum, success at this stage is not about scaled deployment. It is about ensuring we are prepared for the moment when the technology crosses into clear business value.

FinAi: How does quantum technology fit into BMO’s overall AI strategy? 

KM: Quantum is an extension of our AI strategy, not a separate track. 

Our focus is on becoming a digital-first, AI-powered bank where we use data and intelligence to improve decisions, drive growth and operate more efficiently. Quantum fits in by increasing the depth and range of those decisions over time. 

Right now, that means targeted research and capability building. Longer term, it becomes a force multiplier for AI, particularly in areas where classical approaches become computationally constrained.

FinAi: How would you describe the synergistic relationship between quantum and AI in the context of financial services? 

KM: AI and quantum solve different parts of the same problem. 

AI helps us interpret data, detect patterns and make predictions. Quantum has the potential to expand how many scenarios we can evaluate and how complex those scenarios can be. These two technologies are different, but they can work together in specific applications, helping each other achieve better outcomes. Early work in areas like quantum-enhanced and hybrid algorithms points to that potential. 

In financial services, where we deal with uncertainty, interconnected systems and large-scale data, that combination can meaningfully improve the quality of decision-making, not just the speed. 

FinAi: Which areas of banking operations are best suited for AI-quantum convergence and why? 

KM: We focus on areas where complexity and scale are already limiting what we can do today. 

That includes things like collateral and capital optimization, where there are many competing constraints. It includes liquidity and risk simulation, where we need to model a wide range of potential scenarios. And it includes fraud detection, where patterns can be subtle and highly interconnected. 

These are strong use cases because they align with domains where quantum computing has potential advantages, namely combinatorial optimization and high-dimensional simulation, while AI remains critical for problem formulation, feature extraction and integration into real-world workflows. 

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