Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary Strategies
This work addresses the challenge of efficient quantum simulation for near-term quantum computing, though it is incremental as it applies an existing optimization method to a known bottleneck.
The paper tackled the problem of reducing simulation error in quantum systems by optimizing Trotter-Suzuki decompositions using the CMA-ES algorithm on a Heisenberg Chain, achieving a 60% reduction in error.
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.