  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.
