# Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models

arXiv: https://arxiv.org/abs/2407.21062 · HTML: https://mlsanity.com/paper/2407.21062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [Feature Selection on Quantum Computers](https://mlsanity.com/paper/2203.13261/) (2022)
- [Adiabatic Quantum Linear Regression](https://mlsanity.com/paper/2008.02355/) (2020)
- [Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection](https://mlsanity.com/paper/2208.07963/) (2022)
- [NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems](https://mlsanity.com/paper/2212.10990/) (2022)
- [QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting](https://mlsanity.com/paper/2303.09772/) (2023)
- [Building an iterative heuristic solver for a quantum annealer](https://mlsanity.com/paper/1507.07605/) (2015)

## Abstract (arXiv)

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.