# A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications

arXiv: https://arxiv.org/abs/2106.02964 · HTML: https://mlsanity.com/paper/2106.02964/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 5 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Support vector machines on the D-Wave quantum annealer](https://mlsanity.com/paper/1906.06283/) (2019)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)
- [Image classification using quantum inference on the D-Wave 2X](https://mlsanity.com/paper/1905.13215/) (2019)
- [Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems](https://mlsanity.com/paper/1910.13045/) (2019)
- [Fast clique minor generation in Chimera qubit connectivity graphs](https://mlsanity.com/paper/1507.04774/) (2015)
- [Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer](https://mlsanity.com/paper/1911.06259/) (2019)
- [Training a quantum annealing based restricted Boltzmann machine on cybersecurity data](https://mlsanity.com/paper/2011.13996/) (2020)
- [Developing Quantum Annealer Driven Data Discovery](https://mlsanity.com/paper/1603.07980/) (2016)
- [Optimizing Adiabatic Quantum Program Compilation using a Graph-Theoretic Framework](https://mlsanity.com/paper/1704.01996/) (2017)

## Built on by (most cited first, 7 of 7)

- [Black-box optimization for integer-variable problems using Ising machines and factorization machines](https://mlsanity.com/paper/2209.01016/) (2022)
- [From Quantum Graph Computing to Quantum Graph Learning: A Survey](https://mlsanity.com/paper/2202.09506/) (2022)
- [Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing](https://mlsanity.com/paper/2311.01028/) (2023)
- [Quantum Deep Learning Still Needs a Quantum Leap](https://mlsanity.com/paper/2511.01253/) (2025)
- [A general learning scheme for classical and quantum Ising machines](https://mlsanity.com/paper/2310.18411/) (2023)
- [Collaborative filtering based on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2410.10381/) (2024)
- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)

## Abstract (arXiv)

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-Wave's quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.