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Inside Quantum Technology’s Inside Scoop: Quantum in the Finance Industry

Quantum computing can significantly help the finance industry through machine learning, annealing, and other processes.
By Kenna Hughes-Castleberry posted 04 Oct 2022

Of the many industries that quantum computing is sure to benefit from, the finance industry is one of the biggest. “Essentially, all big banks now have their own quantum team,” explained Roman Orus, the Co-Founder and Chief Scientific Officer of Multiverse Computing, a leading quantum software company. Orus is also an Ikerbasque Research Professor at Spain’s Donostia International Physics Center (DCIP), where he wrote an influential paper on quantum computing and finance. “There are many different places where quantum computing can help in finance,” Orus added.

As much of the finance industry is focused on analyzing large pools of raw data and drawing various conclusions, quantum computing can significantly improve this process. As quantum computers use algorithms to run multiple calculations at once, they can produce results at a faster rate, which is crucial for the trading that happens at a rapid pace in the stock markets. The answers that quantum computers give are also unique from classical computers, giving other advantages. “Like quantum physics, they are probabilistic rather than deterministic,” explained a 2020 article from McKinsey& Company. “[This means] that they can vary even when the input is the same.” These various inputs are especially important for optimization problems, financial simulations, fraud detection, and market prediction, all processes that banks and other financial institutions utilize on a daily basis.

Reading Monte Carlo Simulations

One of the most common optimization simulations, especially for financial portfolios, is the Monte Carlo simulation. This method uses a random sampling of inputs to solve a statistical problem, with the simulation giving a visual solution to this problem. “In the financial sector, these Monte Carlo simulations are commonly used for stress testing and credit risk assessment, but they are costly, time-consuming, and require a lot of computing horsepower,” explained Zapata Computing‘s Chief Marketing Officer Katherine Londergan. Because the Monte Carlo simulation can use various inputs, it has been utilized by various quantum companies to test their technology. Zapata Computing, a market-leading quantum company based in Boston, recently published a paper focused on using this simulation for credit valuation adjustments. “Our work with BBVA [a global bank] is exploring the potential of quantum advantage for Monte Carlo use cases including credit valuation adjustment (CVA) and derivative pricing,” Londergan stated. “Banks, like BBVA, are actively exploring ways of making these simulations less time-consuming through quantum computers.”

Other financial processes that quantum computing may be applied to include fraud detection and market predictions. Financial institutions already use machine learning algorithms to help in these situations, but in the future may adopt quantum machine learning to improve things even more. “With the quantum computer, you can improve machine learning algorithms,” Orus said. For cases with live data streams, such as in fraudulent transactions, quantum machine learning may be able to process the data at a faster rate, helping to keep financial processes more secure and efficient.

Quantum Annealing and the Finance Industry

While quantum computing will no doubt benefit the finance industry, quantum annealing specifically will play its own important role. “Quantum annealing is a particular model of quantum computation,” Orus explained, “[So, it’s] built to solve only one specific problem, which is optimization. So, you may have a cost function you need to minimize, the risk of a portfolio of assets, for instance. This is the type of problem that you can solve with quantum annealing.” Companies like D-Wave or Lockheed Martin (which utilizes D-Wave’s technology) are already developing quantum annealers, many of which may be used by financial institutions. Because many problems within the finance industry involve optimization, quantum annealers will add benefits to a wider range of applications than what may be expected. “Even for the simulation of certain economic models, you can also do this via quantum annealing,” added Orus. “For instance, to find economic equilibrium, which is just an optimization problem.”

Though quantum computing will add many advantages to the financial sector, there are many stages before this technology can become more widely adopted. “Looking for incremental advantage with quantum computers in finance will be challenging,” stated Londergan. “We’ve found our financial customers to be highly advanced in harnessing the power of AI and ML, so we’re collaborating on near-term use cases where we can get an incremental advantage.” While getting to this advantage may take some time, other experts like Orus are looking at some of the immediate challenges facing the quantum industry. “I think the main setback is the development of the hardware,” he said. “The processors that we have nowadays are still of relatively small size and noisy.” Once the hardware is improved and able to scale, this innovative technology will hopefully be more easily adoptable.

But there are also steps that financial institutions will need to take to adopt quantum computing. As Londergan explained: “To succeed in adopting quantum, financial institutions will need to be flexible modular, and have a forward compatible approach for building quantum-enabled applications. This means algorithms, data streams, and quantum-classical hardware backends can be easily swapped in and out—without a ‘rip and replace’ of computing infrastructure.” Along with this flexible mindset, banks and other institutions may need to change the timeline of when they implement this technology, as it could take some time. “It’s worth calling out that Zapata believes large simulations, like these Monte Carlo use cases, are beyond a decade out,” added Londergan.

Other experts like Orus believe the widespread adoption of quantum computing is actually much closer. “It’s already starting to penetrate the industry,” Orus said. “We’re starting to find, essentially, the first real-life use cases. So, I would say in the next two, three years, a wide majority of the big banks will have at least some quantum solution in production.”

Kenna Hughes-Castleberry is a staff writer at Inside Quantum Technology and the Science Communicator at JILA (a partnership between the University of Colorado Boulder and NIST). Her writing beats include deep tech, the metaverse, and quantum technology.

Categories: quantum computing

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