Because qubits, the building blocks of quantum, “can learn with much less and noisier data, they’re very efficient at learning,” Shete says. This means quantum can take on machine learning challenges with fewer constraints than traditional HPC demands.
Nilesh Vaidya, EVP and global industry head for retail banking and wealth management at Capgemini, agrees about the value of quantum for machine learning. “Applying machine learning techniques using quantum computing capability prepares the models better and faster,” Vaidya says. “Today, it takes a while to create and deploy models and visualize the outcomes, but with quantum some parts of it can be greatly accelerated.”
In addition to the technical feasibility qualifications, Shete advises enterprises to cherry-pick projects where even a little improvement can lead to good business value. Stakeholder interest is also key. “You could have all the other factors fitting in but if the business unit lead that you’re working with is either resistant or unwilling to change, that’s a stumbling block,” Shete says.
“If you understand the strengths and weaknesses of quantum, then in each field you can find a good niche where you can add large value,” Broer says. “But if you assume it can add value to everything then you’ll be very disappointed. It’s like a hammer looking for a nail, it’s going to be a lot of work to find that nail but once you have it, you can get started.”
Industry-quantum provider partnerships
How exactly to get started? While a few financial institutions are building their quantum teams from the ground up, many are choosing to partner with experts in the field.
Markus Pflitsch, Terra Quantum’s CEO and founder, argues that “it’s just not feasible for banks and other industries to build quantum capabilities in-house given the dearth of talent.” In addition to providing access to “best-in-breed” quantum hardware, firms such as Terra Quantum can run quantum software on in-house simulators based on classical HPC components, which is how Cirdan addressed its exotic derivative problem. When quantum computers move beyond the Noisy Intermediate Scale Quantum (NISQ) devices they occupy today, the Terra Quantum software can also translate to those platforms.
Shete points out that quantum specialists can also cross-pollinate solutions from different industries. For example, “the simulation work we’re doing in options pricing has got lots of similarities with work that can be done in molecular simulation in chemical companies,” he says. A quantum-only company might seed ideas borrowed from one sector across the board, Shete suggests.
The future with quantum
One machine learning challenge Terra Quantum is currently working on involves understanding customers with time-series prediction models: “It’s about predicting customer behavior, really understanding how customers will react, what is the best grouping of different customers, what the correlations are and how they should be put together, and hence what are the best products customers should be nudged toward,” Shete says.
In markets, time-series predictions help understand how markets will behave and evaluate correlation between different types of assets. And in risk management, quantum can be deployed “for Monte Carlo simulations or understanding anti-money laundering or compliance issues that might be happening within your bank,” Shete says.
For its part, Ally expects to evaluate more quantum-related projects in the future, including credit loss modeling, where one can predict what percentage of loans granted to customers might end up as losses. The proof-of-concept projects Ally has conducted so far are its trial run for when quantum is ready for prime time.
“It’s important for us to test the technology and be ready,” Muthukrishnan says. “It’s like constantly working out and doing your sprints so when the real race happens, you’re ready to go. You can’t sit around and wait for things to happen — it’s all about consistency, preparation, and then being able to rise to the occasion when the time is right.”