Quantum algorithm for credit valuation adjustments

• Published in: New journal of physics Vol. 24; no. 2; pp. 23036 - 23063
• Main Authors: Alcazar, Javier, Cadarso, Andrea, Katabarwa, Amara, Mauri, Marta, Peropadre, Borja, Wang, Guoming, Cao, Yudong
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2022
• Subjects: Algorithms; Amplitudes; Approximation; Banking; Circuits; Coherence; Computers; Great Recession; Logic circuits; Monte Carlo simulation; Parameter estimation; Physics; Proposals; quantum algorithm; Quantum computing; quantum finance; quantum information; Quantum mechanics; Quantum phenomena; Random variables; Risk assessment; Stochastic models; Valuation
• ISSN: 1367-2630; 1367-2630
• DOI: 10.1088/1367-2630/ac5003

Abstract Quantum mechanics is well known to accelerate statistical sampling processes over classical techniques. In quantitative finance, statistical samplings arise broadly in many use cases. Here we focus on a particular one of such use cases, credit valuation adjustment (CVA), and identify opportunities and challenges towards quantum advantage for practical instances. To build a NISQ-friendly quantum circuit able to solve such problem, we draw on various heuristics that indicate the potential for significant improvement over well-known techniques such as reversible logical circuit synthesis. In minimizing the resource requirements for amplitude amplification while maximizing the speedup gained from the quantum coherence of a noisy device, we adopt a recently developed Bayesian variant of quantum amplitude estimation using engineered likelihood functions. We perform numerical analyses to characterize the prospect of quantum speedup in concrete CVA instances over classical Monte Carlo simulations.