Article Version 1 This version is not peer-reviewed
Hybrid Classical-Quantum Computing: Applications to Statistical Mechanics of Financial Markets
Version 1 : Received: 17 April 2021 / Approved: 19 April 2021 / Online: 19 April 2021 (09:09:32 CEST)
How to cite: Ingber, L. Hybrid Classical-Quantum Computing: Applications to Statistical Mechanics of Financial Markets. Preprints 2021, 2021040459 Ingber, L. Hybrid Classical-Quantum Computing: Applications to Statistical Mechanics of Financial Markets. Preprints 2021, 2021040459
Hybrid Classical-Quantum computing has already arrived at several commercial quantum computers, offered to researchers and businesses. Here, applications are made to a model of financial options, Statistical Mechanics of Financial Markets (SMFM). These applications were published in many papers since the 1980's. This project only uses Classical (super-)computers to include quantum features of these models. Since 1989, an optimization code, Adaptive Simulated Annealing (ASA), has been to fit parameters in such models. Since 2015, a path-integral algorithm, PATHINT, used previously to accurately describe several systems in several disciplines, has been generalized from 1 dimension to N dimensions, and from classical to quantum systems, qPATHINT. Published papers have described the use of qPATHINT to neocortical interactions and financial options. The classical space by SMFM applies nonlinear nonequilibrium multivariate statistical mechanics to fit parameters in conditional short-time probability distributions, while the quantum space described by qPATHINT deals specifically with quantum systems, e.g., quantum money. This project thereby demonstrates how some hybrid classical-quantum systems may be calculated quite well using only classical (super-)computers.
path integral; quantum systems; classical optimization; financial options
Business, Economics and Management, Finance
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.Leave a public comment
Send a private comment to the author(s)