The Future of Quantum in FinanceBy
Quantum computing in 2022 is a race that’s developing measurable national outlines. For those still uncertain about the likelihood or importance of this shift from binary digital to quantum, Global Tech Outlook magazine published a list of the top countries participating in the race ranked according to their investments in the technology.
Leading the list, China has allocated $10 billion for the Academy of Sciences’ Center for Excellence in Quantum Information and Quantum Physics, the National Quantum Laboratory, and other agencies. Germany has promised $2.4 billion for quantum research and development, and Canada will invest $360 million for a national quantum strategy. The United States, under the Department of Energy, is investing $625 million in five quantum computing centers, and Japan will invest $276 million in quantum with a focus on practical quantum cryptography. These funds are in addition to the avid interest and serious funding from the current leaders in classical computing, including IBM, Google, Honeywell, Microsoft, and Amazon, and a raft of pure players like Rigetti Computing and IonQ, five or six of whom are now seeking listings on Wall Street.
For those who aren’t examining possible use cases for quantum in finance, now is the time to start looking over the applications best suited for quantum. The white paper Quantum computing for finance: overview and prospects offers a useful list of financial problems and how quantum computation can be applied to them. Authors Román Orús (Johannes Gutenberg University), Samuel Mugel (Quantum for Quants Commission), and Enrique Lizaso (Quantum World Association) offer their perspectives on quantum in finance. They explain, “The race to the quantum computer is largely motivated by the sheer amount of technological disruption this machine is expected to bring. Of crucial importance, we can expect our approach to finance to be completely transformed.”
Three areas that are emphasized throughout the white paper are quantum’s speed and talent for optimization, deep learning, and amplitude estimation for Monte Carlo sampling.
Many problems in finance center on optimization issues, and the authors note, “These are tasks which are particularly hard for classical computers but find a natural formulation using quantum optimization methods. In recent years, this field has known a tremendous growth, partly due to the commercial availability of quantum annealers.” Quantum annealing is a computing method that provides a way to find solutions when the problems can present large numbers of solutions. The authors point out that quantum annealers can be used to optimize portfolios, find arbitrage opportunities, and perform credit scoring.
Financial problems often require that you search for patterns in past data. “This is a natural way to consider economic forecasting problems, an area where machine learning methods have proved to be extremely successful.” Recent developments in the algorithms for quantum machine learning can cut the costs of these searches and even enable some that would otherwise be impossible.
The third set of problems involve Monte Carlo-based methods for predicting the behavior of financial systems. The authors explain, “We consider quantum amplitude estimation and how it can result in a quantum speed-up for Monte Carlo sampling.”
After describing the types of problems to be covered, the paper reviews some basic principles of quantum computing, including superposition states and entanglement, and also presents a five-step description of a quantum algorithm. This discussion is followed by a case for quantum computing in finance.
Although the section describing currently available quantum hardware is a little out-of-date (the paper is dated February 2019), there’s a useful introduction of some of the companies leading this effort, and a visit to any of their websites will provide an update on where their machines, qubit counts, and quantum networking possibilities are today. There’s also a separate section devoted to “the other great family of quantum computers”—quantum annealers. These computers are dedicated to finding “local minima in combinatorial optimization problems.”
Today, universal quantum processors and practical, long-distance networks are still works in progress. Despite that, the authors explain, “It is possible, however, that faulty quantum computers will find interesting applications far before we achieve fault-tolerant quantum computing. We would expect it is in this area that the first real disruptions to finance will occur, and we urge researchers to investigate this fascinating direction of study.”