  One of the most promising applications of near-term quantum computing is the
simulation of quantum systems, a classically intractable task. Quantum
simulation requires computationally expensive matrix exponentiation;
Trotter-Suzuki decomposition of this exponentiation enables efficient
simulation to a desired accuracy on a quantum computer. We apply the Covariance
Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the
Trotter-Suzuki decompositions of a canonical quantum system, the Heisenberg
Chain; we reduce simulation error by around 60%. We introduce this problem to
the computational search community, show that an evolutionary optimisation
approach is robust across runs and problem instances, and find that
optimisation results generalise to the simulation of larger systems.
