Reverse annealing for nonnegative/binary matrix factorization
John Golden, Daniel O’Malley
Abstract
It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.
Topics & Concepts
Quantum annealingAnnealing (glass)SubroutineSimulated annealingBinary numberFactorizationComputer scienceAlgorithmMatrix decompositionQuantumMathematicsQuantum computerMaterials sciencePhysicsQuantum mechanicsArithmeticComposite materialOperating systemEigenvalues and eigenvectorsQuantum Computing Algorithms and ArchitectureNeural Networks and Reservoir ComputingQuantum Information and Cryptography