QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning

arXiv:2211.01365v3 Announce Type: replace-cross
Abstract: Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. This work bridges the gap between Koopman operator theory, which has found utility in applications because it allows for a linear representation of nonlinear dynamical systems, and natural gradient methods in quantum optimization, leading to a significant acceleration of gradient-based quantum optimization. We present Quantum-circuit Alternating Controlled Koopman learning …

abstract application applications arxiv complexity computing cs.ai cs.lg found gap gradient key math.oc optimization parameters physics.comp-ph quant-ph quantum quantum computing theory type utility work