In the coming decade, the emergence of quantum computers is expected to have a revolutionary impact on various industries, surpassing the computational abilities of traditional computers. Quantum computing will profoundly transform the financial sector, with its potential to accelerate banking processes and enable accurate financial predictions, risk analysis, and pattern analysis.
Despite its potential, the technology is still in its early stages. A team of researchers from industry, academia, and the U.S. Department of Energy's Argonne National Laboratory, including the University of Delaware's Ilya Safro, recently published a primer on quantum computing and finance in Nature Reviews Physics. The paper provides a comprehensive overview of the current state of quantum computing for financial applications, highlighting its benefits and limitations compared to traditional computing techniques.
The primer also sheds light on the challenges that must be addressed before quantum computing can be widely used in the financial industry. This is a topic of great importance to the University of Delaware, which offers one of the first interdisciplinary graduate degree programs in quantum science and engineering in the United States. The research, led by a team that includes UD, Argonne, JPMorgan Chase, and University of Chicago scientists, emphasizes the need for collaboration across different sectors.
The team aims to achieve "practical quantum advantage" in finance, where processes are faster, more accurate, and more energy-efficient. According to Safro, even a small improvement in this field could have a significant impact, leading to cost reductions, increased productivity, and better practices across industries.
The primer is written for researchers who are not necessarily experts in quantum computing, making it a valuable resource for those interested in using quantum computers to accelerate solutions for the financial sector. It discusses challenges in three key areas at the intersection of finance and computing: optimization, machine learning, and stochastic modeling.
Quantum computing utilizes the principles of physics at the atomic level to perform computations at incredible speeds, far exceeding traditional computing capabilities. In some cases, a quantum computer can complete a calculation in a matter of minutes, a task that would take a supercomputer 10,000 years to complete.
This extraordinary speed is precisely what makes quantum computing attractive to the financial sector. As Argonne scientist Yuri Alexeev explains, In the financial world, time and accuracy are of the essence. Getting solutions quickly can have huge benefits.
This could apply to a variety of financial processes, including portfolio management, investment strategies, and detecting credit card fraud.
The paper highlights three main challenges for using quantum computing in finance: optimization, machine learning, and stochastic modeling. Optimization involves finding the best solution to a problem in a short amount of time, a process that quantum computers are well-suited for. Machine learning is already used in many financial institutions, but combining it with quantum algorithms can significantly speed up predictions and analysis. Stochastic modeling, which is used to predict complex processes such as stock prices, can also benefit from the power of quantum computing.
Safro notes that one of the most exciting aspects of ongoing research in this area is the unknown. There is no specific quantum technology that is guaranteed to dominate the market, which means that various technologies and vendors must compete to improve quantum hardware and make it more accessible for practical applications.
Once researchers demonstrate the scalability of quantum computing devices, it will open up new opportunities for quantum computing in solving large, real-world problems and hybridizing them with traditional supercomputers. This breakthrough is expected to lead to a surge in job opportunities in quantum computing, similar to the growth we have seen in artificial intelligence.