Quantum computing could be involved in some of the most complex calculations in financial markets within five years, much sooner than expected, according to a study conducted jointly by Goldman Sachs.
The findings come as banks and other companies at the forefront of quantum research have turned to attempting to achieve practical results using the imperfect quantum computers that are expected to be in use in the next few years, rather than expect a lot more. powerful systems that should one day revolutionize computing.
The bank’s research, conducted with quantum start-up QC Ware, suggests that programmers looking to tap the machines could get practical results sooner in return for giving up some of the huge performance gains promised by the systems. quantum.
The work reflects a recent effort by companies investing in the field to seek a “quantum advantage,” or a marginal practical improvement over existing computers. This is a more modest goal than waiting for full “quantum supremacy,” the term used when quantum systems are able to solve problems that are essentially impossible for a classical computer.
Research has looked into the use of quantum machines to value complex derivatives, one of the most IT-demanding tasks in financial markets and a significant cost to banks. The calculations are based on so-called Monte Carlo simulations, which involve making a large number of projections about future random market movements to calculate the probability of a particular outcome.
Research has highlighted short-term advancements that will allow prices to be offered over the phone to clients wishing to trade complex derivatives, rather than waiting the hours it sometimes takes to perform calculations using computers. today, said Paul Burchard, head of research. in Goldman R&D. “There is a very large IT bill that we pay every year to set the price of these derivatives and run risks on them,” he added.
In previous research Last year with IBM, Goldman calculated that it would need a quantum computer with about 7,500 quantum bits, or qubits, to run a full Monte Carlo simulation.
IBM and Google are among the companies in the race to build such systems, which are expected to arrive within five years.
However, they use qubits that only retain their quantum state for short periods of time, making systems rife with errors. Research into the techniques needed to overcome this problem is still in its early stages, which means that the full benefits of quantum machines could be many years away.
The banks latest research, with QC Ware, looked at how to run a less exhaustive simulation that could be done in the short time available before errors crept in.
Details of the work were first presented at the Q2B 2020 conference and have since been refined in a peer-reviewed paper ahead of publication.
Rather than an expected 1,000-fold improvement from a fully error-corrected quantum computer, performing such a computation using today’s imperfect quantum hardware could yield a 10-fold gain in five years, researchers say – again enough to justify setting up computers. use on practical problems.
The same technique was likely to prove useful in other industries and accelerate the adoption of quantum computing more widely, said Matt Johnson, CEO of QC Ware.
Monte Carlo simulations have been used in other areas of finance, as well as in industries such as aerospace and automotive, he added, making this type of computer problem “fairly uniform across the board. industry ”.
This article has been modified since publication to correct the name of the conference where portions of the research were first presented.