2017/06/30 by Abolfazl Ramezanpour, A. Ramezanpour
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Configuration space #Function (biology) #Mathematical optimization #Mathematics #Neural Networks and Reservoir Computing #Nonlinear system #Optimization problem #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum mechanics #Reinforcement learning #Simulated annealing #Space (punctuation) #cond-mat.dis-nn #cond-mat.stat-mech #cs.AI #cs.LG #quant-ph
paper · pdf · doi:10.1103/physreva.96.052307
published as Phys. Rev. A 96, 052307 (2017) · 14 pages, 5 figures
arxiv created 2017/11/03 · openalex publication_date 2017/11/03 · arxiv updated 2017/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A reinforcement algorithm solves a classical optimization problem by introducing a feedback to the system, which slowly changes the energy landscape and converges the algorithm to an optimal solution in the configuration space. Here, we use this strategy to concentrate (localize) the wave function of a quantum particle, which explores the configuration space of the problem, preferentially on an optimal configuration. We examine the method by solving numerically the equations governing the evolution of the system, which are similar to the nonlinear Schr"odinger equations, for small problem sizes. In particular, we observe that reinforcement increases the minimal energy gap of the system in a quantum annealing algorithm. Our numerical simulations and the latter observation show that such kind of quantum feedback might be helpful in solving a computationally hard optimization problem by a quantum reinforcement algorithm.