Quantum Langevin Dynamics for Optimization

arXiv:2311.15587v2 Announce Type: replace-cross
Abstract: We initiate the study of utilizing Quantum Langevin Dynamics (QLD) to solve optimization problems, particularly those non-convex objective functions that present substantial obstacles for traditional gradient descent algorithms. Specifically, we examine the dynamics of a system coupled with an infinite heat bath. This interaction induces both random quantum noise and a deterministic damping effect to the system, which nudge the system towards a steady state that hovers near the global minimum of objective functions. We …

abstract algorithms arxiv cs.ds cs.lg dynamics functions gradient heat math.oc noise obstacles optimization quant-ph quantum random solve study type