Function Smoothing Regularization for Precision Factorization Machine Annealing in Continuous Variable Optimization Problems

TL;DR

This paper identifies noise issues in Hamiltonian functions derived from factorization machines in quantum annealing for continuous optimization, and proposes a smoothing regularization method to improve performance.

Contribution

It introduces a novel smoothing regularization technique to reduce noise in Hamiltonian functions obtained by factorization machines, enhancing quantum annealing effectiveness.

Findings

The noise in Hamiltonian surfaces hampers quantum annealing performance.

The proposed regularization method effectively reduces noise.

Improved problem-solving capabilities demonstrated on practical cases.

Abstract

Solving continuous variable optimization problems by factorization machine quantum annealing (FMQA) demonstrates the potential of Ising machines to be extended as a solver for integer and real optimization problems. However, the details of the Hamiltonian function surface obtained by factorization machine (FM) have been overlooked. This study shows that in the widely common case where real numbers are represented by a combination of binary variables, the function surface of the Hamiltonian obtained by FM can be very noisy. This noise interferes with the inherent capabilities of quantum annealing and is likely to be a substantial cause of problems previously considered unsolvable due to the limitations of FMQA performance. The origin of the noise is identified and a simple, general method is proposed to prevent its occurrence. The generalization performance of the proposed method and its ability to solve practical problems is demonstrated.