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To summarize, prediction of financial instability is hard, and quantum computers could be tapped to use a quantum algorithm to make this easier. Banks and other financial institutions could use information from quantum computers to provide accurate crash predictions, even before they ever had to fail. Imagine a day when a financial institution could use a quantum computer to forewarn it of impending malfeasance. Or better yet, they could use quantum computation to reconstruct the financial network and run an alarm on a hypothetical future network state.
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 Rapid Communication, 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 two-body interactions, i.e., a quadratic unconstrained binary optimization 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.