Forbes How Quantum Computing Can Better Predict, Prevent Recessions

Fears of a recession are growing. The global economy is dealing with an ever-changing set of pressures related to global pandemics, supply chain disruptions, geopolitical conflicts and the highest inflation rate in decades. Recently, Dr. Sam Mugel, chief technology officer of quantum financial algorithms company Multiverse Computing, talked about how quantum computing can prevent recessions in Forbes.

Dr. Sam Mugel

Photon Box compiled, as follows.

Fears of a recession are justified, as previous economic crises have caused catastrophic loss of value and enormous social damage. In the United States alone, the global financial crisis of 2007-2008 triggered a recession that lasted 18 months and led to a 4.3% decline in U.S. GDP; and layoffs doubled the unemployment rate to more than 10%. As a result, central banks and policy makers have been very active in protecting the stability and efficiency of the financial system.

Providing these organizations with insight into economic behavior is of tremendous value, but the United States has been very poor at predicting economic crises. Until recently, economists have relied on empirical tools and risk models to predict future crises based on historical comparisons. These tools failed to predict crises like the Great Recession of 2008, and it is the inherent complexity of economies that is the culprit.

Economies have evolving networks that include multiple players and assets. They also require interactions among participants that are sometimes irrational and can have nonlinear effects that make predicting outcomes extremely difficult. This complexity makes them difficult to model effectively: even using today's most powerful supercomputers.

1) Financial quantum computing

Several financial institutions are looking to the emerging field of quantum computing for new solutions. These systems use quantum mechanical properties to create combinatorial computing power beyond that of classical systems.

Applications such as modeling and optimization have already demonstrated incremental benefits in pricing derivatives, optimizing portfolios, and reducing default risk. And now, attention is turning to the use of quanta as a tool to encode quantitative macroeconomic problems, revealing how wealth evolves over time in response to changes or perturbations within financial networks.

This task requires us to sample a finite space of solutions that grow with the number of added links in a mathematical set. It has been shown that quantum annealers, originally developed to solve complex optimization problems, are well suited to this task. Indeed, economic networks have been solved over them, creating the most stable economic equilibrium states and determining how changes in these variables will destabilize this stability.

2) Testing macroeconomic simulation tools

Recently, the Bank of Canada partnered with Multiverse Computing on a proof of concept designed to test existing quantum programs in a simulation of a complex, evolving economic network. The simulation modeled the simultaneous adoption of cryptocurrency payments by up to 10 companies. Economists developed a macroeconomic modeling algorithm, but lacked the computational power to solve the important problem. A quantum algorithm was developed for this thorny problem and the modeling yielded some very valuable insights.

As more powerful machines become available, we will be able to further improve the model. This will eventually give governments increasingly sophisticated macroeconomic tools for monitoring vulnerabilities, predicting threats to market stability, and mitigating the effects of severe economic shocks.

3) The future of quantum modeling

Improving the complexity of optimization problems modeled by quantum annealers depends on continued technological advances. The number of quantum bits and error rates in currently available hardware are major limiting factors: quantum annealing systems with 1000-fold error reduction will be needed to model the full economy. By then, quantum computers will be able to simulate financial networks, which would require as many resources as atoms in the universe to achieve on a classical computer.

As tools to simulate complex networks are further developed over the next decade, central banks and financial institutions will be better able to improve economic resilience. Insight into economic vulnerability will help protect entities such as financial institutions and pension funds from shocks that may occur during anomalous events over the life cycle of a portfolio; it will also help central banks better defend against future efforts to weaponize the economy.

While quantum computing still has a long way to go in realizing its full potential, the technology is already yielding valuable new insights and proposing solutions for aspects of market forecasting and stability that did not exist before.

Reference link：

https://www.forbes.com/sites/forbestechcouncil/2022/08/16/could-quantum-computing-better-predict-and-prevent-economic-downturns/?sh=194c60047552