Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models

TL;DR

This paper presents a hybrid heuristic algorithm with an r-flip strategy that improves the efficiency and solution quality of large-scale QUBO problems in adiabatic quantum machine learning, reducing computational costs.

Contribution

Introduction of a novel r-flip hybrid heuristic that enhances QUBO problem solving in AQML, outperforming existing MSTS methods in quality and efficiency.

Findings

r-flip method yields better solutions than MSTS

Significant reduction in computational time

Effective on large-scale QUBO instances

Abstract

Numerous established machine learning models and various neural network architectures can be restructured as Quadratic Unconstrained Binary Optimization (QUBO) problems. A significant challenge in Adiabatic Quantum Machine Learning (AQML) is the computational demand of the training phase. To mitigate this, approximation techniques inspired by quantum annealing, like Simulated Annealing and Multiple Start Tabu Search (MSTS), have been employed to expedite QUBO-based AQML training. This paper introduces a novel hybrid algorithm that incorporates an "r-flip" strategy. This strategy is aimed at solving large-scale QUBO problems more effectively, offering better solution quality and lower computational costs compared to existing MSTS methods. The r-flip approach has practical applications in diverse fields, including cross-docking, supply chain management, machine scheduling, and fraud detection. The paper details extensive computational experiments comparing this r-flip enhanced hybrid heuristic against a standard MSTS approach. These tests utilize both standard benchmark problems and three particularly large QUBO instances. The results indicate that the r-flip enhanced method consistently produces high-quality solutions efficiently, operating within practical time constraints.