Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models
This work addresses a specific bottleneck in quantum machine learning for applications like supply chain management and fraud detection, representing an incremental improvement over prior heuristic methods.
The paper tackles the computational demand in training Adiabatic Quantum Machine Learning models by introducing a hybrid algorithm with an r-flip strategy to solve large-scale QUBO problems more effectively, resulting in better solution quality and lower computational costs compared to existing methods, as shown in tests on benchmark problems and large instances.
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.