Forecasting financial crashes with quantum computing

TL;DR

This paper demonstrates how quantum annealers can be used to predict financial crashes by mapping the problem to a quantum Hamiltonian, offering a potentially more efficient approach for financial stability analysis.

Contribution

It introduces a novel method to handle the intractable problem of crash prediction using quantum annealing and QUBO formulations, applicable to near-term quantum processors.

Findings

Quantum annealers can compute equilibrium market values after shocks.

Mapping financial crash prediction to QUBO makes it feasible on quantum hardware.

Potential for more efficient financial stability assessments.

Abstract

A key problem in financial mathematics is the forecasting of financial crashes: if we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a computationally intractable (NP-hard) problem. Financial crashes are inherently difficult to predict, even for a regulator which has complete information about the financial system. In this paper we show how this problem can be handled by quantum annealers. More specifically, we map the equilibrium condition of a toy-model financial network to the ground-state problem of a spin-1/2 quantum Hamiltonian with 2-body interactions, i.e., a quadratic unconstrained binary optimization (QUBO) problem. The equilibrium market values of institutions after a sudden shock to the network can then be calculated via adiabatic quantum computation and, more generically, by quantum annealers. Our procedure could be implemented on near-term quantum processors, thus providing a potentially more efficient way to assess financial equilibrium and predict financial crashes.