Black-box optimization using factorization and Ising machines

TL;DR

This paper reviews a novel black-box optimization method using factorization machines transformed into quadratic models solved by Ising machines, enabling efficient large-scale optimization across various scientific fields.

Contribution

It introduces the FMQA algorithm that leverages Ising machines for fast, large-scale black-box optimization, along with Python tools and application examples.

Findings

Enables optimization of complex problems using Ising machines.

Demonstrates successful applications in physics, chemistry, and social sciences.

Handles binary, integer, and graph-based optimization problems.

Abstract

Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed into a quadratic unconstrained binary optimization model that can be solved using IMs. This makes it possible to optimize the acquisition function in BBO, which is a difficult task using conventional methods without IMs. Consequently, it has the advantage of handling large BBO problems. To be able to perform BBO with the FMQA algorithm immediately, we introduce the FMQA algorithm along with Python packages to run it. In addition, we review examples of applications of the FMQA algorithm in various fields, including physics, chemistry, materials science, and social sciences. These successful examples include binary and integer optimization problems, as well as more general optimization problems involving graphs, networks, and strings, using a binary variational autoencoder. We believe that BBO using the FMQA algorithm will become a key technology in IMs including quantum annealers.