In a paper published in Nature Reviews Physics, a team of experts in academia, industry, and government has created a one-stop resource on the use of quantum computers to accelerate solutions for the finance sector. The paper discusses challenges in three categories at the intersection of finance and computing: optimization, machine learning, and stochastic modeling.
The team comprises scientists at the US Department of Energy’s (DOE) Argonne National Laboratory, JPMorgan Chase, Fujitsu Research of America, Menten AI, the University of Chicago’s Pritzker School of Molecular Engineering, and the University of Delaware. The research was facilitated by the Chicago Quantum Exchange (CQE), an intellectual hub that brings together academia, government, and industry to advance quantum research, train the future quantum workforce, and drive the quantum economy. Argonne and UChicago are founding members of the CQE, which is also anchored by DOE’s Fermi National Accelerator Laboratory, the University of Illinois Urbana-Champaign, the University of Wisconsin–Madison, and Northwestern University. JPMorgan Chase is a CQE corporate partner.
The report is written for researchers who aren’t necessarily quantum computing experts.
“We got together as a group of researchers from different institutions to better understand the state of the art of quantum computing for financial applications,” said Marco Pistoia, head of Global Technology Applied Research at JPMorgan Chase. “We wanted this to be appreciated by a larger audience. Our paper can be the starting point for researchers to better understand the landscape and then dive deeper in the areas that they’re interested in.”
Quantum computing harnesses features of physics at the level of the atom to perform computations at speeds that leave traditional computing in the dust. In some cases, a quantum computer will be able to calculate in a few minutes what it would take a supercomputer 10,000 years to run.
“The upside of quantum computing is absolutely humongous,” said Argonne scientist Yuri Alexeev, one of the report’s co-authors. “We’re talking about a potential speedup of millions of times for solving certain problems.”
It is precisely the advantage of supersonic speed that finance experts are interested in.
“In the financial world, time and accuracy are of the essence,” Alexeev said. “Getting solutions quickly can have huge benefits.”
The three categories of challenges the paper discusses—optimization, machine learning, and stochastic modeling—are at the intersection of finance and computing.
Optimization refers to methods for rapidly obtaining the best solution to a problem. For example, financial companies could use quantum computers to rapidly select assets that would provide the maximum return on a customer’s investment with minimal risk.
The second category, machine learning, is already a part of many financial institutions’ toolkits. In machine learning, computers draw on massive data sets to make predictions about various behaviors, such as patterns in the stock market. Combining quantum algorithms with machine learning can massively speed up those predictions.
The third category, stochastic modeling, is used across the sciences to predict the spread of disease, the evolution of a chemical reaction, or weather patterns. The mathematical technique models complex processes by making random changes to a variable and observing how the process responds to the changes. The method is used in finance, for instance, to describe the evolution of stock prices and interest rates. With the power of quantum computing behind it, stochastic modeling can provide faster and more accurate predictions about the market.
Convening different sectors in applying quantum technology solutions to industry challenges has been a major focus of the CQE’s work, including through The Bloch, a CQE-led coalition of academic, government, industry, and nonprofit partners that won Chicagoland a designation as a US Regional Technology and Innovation Hub for quantum technologies late last year. The Bloch (pronounced “block”), which is vying for up to $70 million in project funding through the second phase of the Tech Hubs program, will build capacity for quantum companies, create pathways for quantum jobs, and establish end-to-end quantum industry solutions, including in the finance sector.
As the Nature Reviews Physics report makes clear, there is no shortage of finance challenges for quantum computers to tackle.
“We’ve heard many times that finance will benefit greatly from quantum computing,” Pistoia said. “All the work we examined confirms that quantum computing for finance is a vibrant field.”