2019/04/02 by B. J. P. Jones, George O. O'Brien, Jones, Benjamin D. M. +7 · 5 citations
Computer Science · Engineering · #Neural Networks and Reservoir Computing #Optical Network Technologies #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1904.01336
One of the most promising applications of near-term quantum computing is the\nsimulation of quantum systems, a classically intractable task. Quantum\nsimulation requires computationally expensive matrix exponentiation;\nTrotter-Suzuki decomposition of this exponentiation enables efficient\nsimulation to a desired accuracy on a quantum computer. We apply the Covariance\nMatrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the\nTrotter-Suzuki decompositions of a canonical quantum system, the Heisenberg\nChain; we reduce simulation error by around 60%. We introduce this problem to\nthe computational search community, show that an evolutionary optimisation\napproach is robust across runs and problem instances, and find that\noptimisation results generalise to the simulation of larger systems.\n