Aser Cortines
Director of Engineering
How Quantum Computing Can Help Meet the Goals of Sustainable Finance
Large financial institutions play an important role in driving society’s sustainability goals. Banks and hedge funds can support the decarbonization of industrial and institutional clients while also influencing capital flows through advice to individual clients. For example, financial institutions can increase the credit availability for corporate energy transition actions. At the individual level, banks and investor advisors can help clients who want to enhance the environmental, social and governmental (ESG) profile of their investments.
Aligning a financial portfolio with the drive towards net zero and new ESG requirements demands innovative new tools for better decision-making and monitoring performance related to targets. Banks and other financial entities will need to invest in new data-driven solutions and cutting edge-methods to tackle the complexity of these challenges. Quantum computing is a disruptive technology with the potential to help financial institutions become drivers of the global ESG transformation.
Quantum Computing and Sustainable Finance
Before delving into the quantum computing landscape, it is important to understand the crucial role of Machine Learning in sustainable finance. Machine learning, and particularly Natural Language Processing (NLP) technology, has been instrumental in dealing with the increasing complexity of sustainable finance. NLP algorithms analyze corporate reports, news articles and social media content to glean insights into corporate ESG practices and public perceptions. This capability is foundational for financial entities navigating the challenges involved in integrating ESG criteria into investment equations. And quantum computing will be invaluable in the integration of those advanced data-driven solutions.
Quantum computing is a paradigm shift in technology. It harnesses the principles of quantum mechanics to process and analyze information in ways that far exceed the capabilities of classical computers. Still in the early stages of development, quantum computing will offer enhanced computational power for a multitude of problems. Among the most promising applications are quantum optimization algorithms. Recent publications indicate that certain quantum algorithms can be run today in modern quantum annealers. These are a type of quantum processor that can be efficiently used to solve complex optimization problems in high dimension, of the variety that includes the classic (although still unsolved) traveling salesman problem.
Optimization techniques are useful in solving many computational problems in finance. They can be used in the processes of resource allocation, understanding demand optimality and liquidation strategies, among others. Adding ESG considerations into the financial equation makes these problems even more complex. For example, the classic resource allocation problem consists of finding the portfolio with higher returns with smaller risk (usually measured by its volatility or some other risk indicator such as VaR). ESG considerations, such as assessing the carbon footprint of the portfolio, makes resolution of these optimization equations much harder.
As a consequence, structuring a large portfolio that meets both profitability and ESG expectations can be a challenging task for managers. Classical optimization algorithms cannot readily solve these extremely complex, highly non-convex functions. By leveraging quantum mechanical phenomenon such as tunneling, quantum optimization algorithms help solvers generate better and more stable solutions.
Quantum computing can also contribute to sustainable finance through restructuring index funds to meet ESG standards. Oftentimes these indexes contain a diversity of companies that may or may not comply with the fund’s ESG targets. In practical terms this means portfolio managers have to limit the structuring of the fund to those assets that satisfy ESG requirements while meeting financial performance requirements. This is a challenging problem that cannot readily be solved by a classical optimization solver. Yet quantum annealing algorithms have proven successful in structuring portfolios that satisfy these constraints.
Conclusion
While machine learning, especially NLP, has been pivotal in managing the complexities of sustainable finance, quantum computing advances this technological revolution. Quantum optimization algorithms already offer tools to address challenges that are currently intractable for classical computing methods. As quantum technology continues to develop, it offers significant potential for revolutionizing the financial industry. By enabling more informed and effective decision-making in the realm of sustainable finance, it is poised to play a crucial role in the sustainable management of economic resources.
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References
Orús R., Mugel S. and Lizaso E.; Quantum computing for finance: Overview and prospects, Reviews in Physics, vol 4, 2019.
Johnson, M., Amin, M., Gildert, S. et al. Quantum annealing with manufactured spins. Nature 473, 194–198 (2011).
Albash T. and Lidar D. A., Adiabatic quantum computation, Rev. Mod. Phys. 90, 2018
Muthukrishnan S., Abash T. and Lidar D.A. Tunneling and Speedup in Quantum Optimization for Permutation-Symmetric Problems. Phys. Rev. X, volume 6, issue 3, 2016.
Palmer S., Karagiannis K., Florence A., Rodriguez A., Orus R., Naik H., Mugel S., Financial Index Tracking via Quantum Computing with Cardinality Constraints. ArXiv:2208.11380