Quantum computing and finance | U Daily

Quantum computers exceed the computational power of classical computers and are expected to have a transformative impact on many industrial sectors over the next decade. In finance, for example, quantum computing will one day be used to speed up banking operations, make financial predictions, and analyze financial patterns and risks.

However, this technology is still in its infancy.

Ilya Safro of the University of Delaware is an associate professor and associate professor of graduate studies and research in the School of Computer and Information Sciences and part of a team of researchers from industry, academia, and the U.S. Department of Energy’s Argonne National Laboratory. , recently an introduction to quantum computing and finance.

Paper published in nature review physicsWe summarize the latest advances in quantum computing for financial applications and outline its advantages and limitations over classical computing techniques used in the financial industry. We also highlight some of the challenges that still need to be addressed to use quantum computing in this way.

This is an important topic around the world and at UD, one of the first institutions in the United States to offer an interdisciplinary graduate degree program in quantum science and engineering.

The research, facilitated by the Chicago Quantum Exchange (CQE) and led by a team including scientists from UD, Argonne, JPMorgan Chase and the University of Chicago, laid the foundation for future applications and was cross-sectoral. It highlights the need for cooperation. Other partners include Fujitsu Research of America, Inc. and Menten AI.

The hope is to bring so-called “practical quantum advantage” to the financial world, where processes are faster, more accurate and more energy efficient, Safro said.

“Finance is a field where even small improvements are literally felt in dollars,” Safro said. “Even small improvements at an economic level will be very important. For example, entire industries will become more efficient, resulting in significant cost savings, increased productivity, or more sustainable practices.” It may lead to.”

This is one reason why finance is likely to be a major beneficiary of quantum computing.

Written for researchers who are not necessarily experts in quantum computing, the team considers this primer to be a one-stop resource on using quantum computers to accelerate solutions for the financial sector. This paper discusses his three categories of challenges: optimization, machine learning, and stochastic modeling at the intersection of finance and computing.

“We came together as a group of researchers from various institutions to better understand the cutting-edge technology of quantum computing for financial applications,” said Marco Pistoia, head of global technology and applied research at JPMorgan Chase. ” he said. “We wanted this to be appreciated by a wider audience. Our paper can serve as a starting point for researchers to better understand the situation and delve deeper into areas of interest. There is a possibility.”

Quantum computing takes advantage of features of atomic-level physics to perform calculations at speeds that defy classical computing. In some cases, quantum computers will be able to calculate calculations in minutes that would take a supercomputer 10,000 years to perform.

“The benefits of quantum computing are absolutely huge,” said Argonne scientist Yuri Alekseev, one of the report’s co-authors. “We’re talking about the potential for millions of times speedups to solve specific problems.”

It is precisely the advantages of supersonic speed that are of interest to financial professionals.

“In the world of finance, time and accuracy are extremely important,” Alexeev said. “If we can get a solution quickly, there are big benefits.”

This applies to everything from improving portfolio management to optimizing investment strategies to driving faster detection of credit card fraud, just to name a few.

“All of these problems sound very general, but they are actually mathematical problems. Moreover, many of them are mathematical optimization problems,” Safro said. His expertise is in algorithms and models for quantum computing, machine learning, and artificial intelligence systems, with a particular focus on natural language processing.

The three categories of challenges discussed in this paper (optimization, machine learning, and stochastic modeling) lie at the intersection of finance and computing.

Optimization refers to a method of quickly obtaining an optimal solution to a problem. For example, financial companies can use quantum computers to quickly select assets that offer the greatest return on a customer’s investment with the least amount of risk.

The second category, machine learning, is already part of many financial institutions’ toolkits. In machine learning, computers utilize large data sets to make predictions about various behaviors, such as stock market patterns. Combining quantum algorithms and machine learning can significantly speed up predictions.

The third category, stochastic modeling, is used throughout science to predict the spread of diseases, the development of chemical reactions, or weather patterns. This mathematical method models complex processes by making random changes to variables and observing how the process responds to the changes. This method is used, for example, in the financial field to describe trends in stock prices and interest rates. Harnessing the power of quantum computing, stochastic modeling can provide faster and more accurate predictions about the market.

The growing strength of UD in quantum computing

as nature review physics The report makes clear that quantum computers have no shortage of financial challenges to address. Training future employees is an important part of solving future challenges.

UD is one of the first institutions of higher education in the nation to offer graduate and doctoral-level degrees in quantum science and engineering. The UD program, which begins in 2023, will offer his three tracks: Quantum Nanotechnology, Quantum Theory or Quantum Algorithms and Computation. The interdisciplinary program, located within the university’s graduate school, is designed to provide the next generation of quantum experts with in-demand skills, including algorithms, theoretical physics, engineering, and nanoscale science. Masu.

Safro said one of the things that makes this field and ongoing research exciting is the unknown.

“Right now, there is no specific quantum technology that we know for sure will take over the market,” he said.

This will enable multiple quantum technologies and vendors to scale up quantum hardware to make it more powerful, reliable, and accessible for use in a wide range of applications, from scientific research to practical applications in various industries. It means you have to compete to make things easier.

“If researchers can demonstrate the practical scalability of quantum computing devices with any of these technologies, we will know how to build increasingly large quantum computers and how they can be used to tackle very large real-world problems.” It will give us a precise roadmap for how to hybridize it with classical supercomputers,” Safro continued. “With this literal advancement, I think the number of jobs available with quantum computing will explode, similar to what we’re seeing today with artificial intelligence.”

About Chicago Quantum Exchange

This 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 advance the quantum economy. I did. Argonne University and the University of Chicago are founding members of his CQE, along with the DOE’s Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the University of Wisconsin-Madison, and Northwestern University. JPMorgan Chase is a corporate partner of CQE.