  Prediction of financial crashes in a complex financial network is known to be
an NP-hard problem, which means that no known algorithm can guarantee to find
optimal solutions efficiently. We experimentally explore a novel approach to
this problem by using a D-Wave quantum computer, benchmarking its performance
for attaining financial equilibrium. To be specific, the equilibrium condition
of a nonlinear financial model is embedded into a higher-order unconstrained
binary optimization (HUBO) problem, which is then transformed to a spin-$1/2$
Hamiltonian with at most two-qubit interactions. The problem is thus equivalent
to finding the ground state of an interacting spin Hamiltonian, which can be
approximated with a quantum annealer. The size of the simulation is mainly
constrained by the necessity of a large quantity of physical qubits
representing a logical qubit with the correct connectivity. Our experiment
paves the way to codify this quantitative macroeconomics problem in quantum
computers.
