# Support vector machines on the D-Wave quantum annealer

arXiv: https://arxiv.org/abs/1906.06283 · HTML: https://mlsanity.com/paper/1906.06283/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 30 of 33)

- [Recent advances for quantum classifiers](https://mlsanity.com/paper/2108.13421/) (2021)
- [Q-Match: Iterative Shape Matching via Quantum Annealing](https://mlsanity.com/paper/2105.02878/) (2021)
- [Quantum Annealing Formulation for Binary Neural Networks](https://mlsanity.com/paper/2107.02751/) (2021)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers](https://mlsanity.com/paper/2205.04346/) (2022)
- [A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications](https://mlsanity.com/paper/2106.02964/) (2021)
- [Scalable Quantum Neural Networks for Classification](https://mlsanity.com/paper/2208.07719/) (2022)
- [A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification](https://mlsanity.com/paper/2303.11705/) (2023)
- [quantum Case-Based Reasoning (qCBR)](https://mlsanity.com/paper/2104.00409/) (2021)
- [Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing](https://mlsanity.com/paper/2311.01028/) (2023)
- [Quantum artificial vision for defect detection in manufacturing](https://mlsanity.com/paper/2208.04988/) (2022)
- [QuAnt: Quantum Annealing with Learnt Couplings](https://mlsanity.com/paper/2210.08114/) (2022)
- [Local Binary and Multiclass SVMs Trained on a Quantum Annealer](https://mlsanity.com/paper/2403.08584/) (2024)
- [Coreset of Hyperspectral Images on Small Quantum Computer](https://mlsanity.com/paper/2204.04691/) (2022)
- [Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC](https://mlsanity.com/paper/2303.15053/) (2023)
- [Analyzing the Effectiveness of Quantum Annealing with Meta-Learning](https://mlsanity.com/paper/2408.00570/) (2024)
- [Probabilistic Quantum SVM Training on Ising Machine](https://mlsanity.com/paper/2503.16363/) (2025)
- [Outlier-Robust Multi-Model Fitting on Quantum Annealers](https://mlsanity.com/paper/2504.13836/) (2025)
- [Minor Embedding for Quantum Annealing with Reinforcement Learning](https://mlsanity.com/paper/2507.16004/) (2025)
- [Quantum-Assisted Support Vector Regression](https://mlsanity.com/paper/2111.09304/) (2021)
- [An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks](https://mlsanity.com/paper/2202.11899/) (2022)
- [Boosting the Performance of Quantum Annealers using Machine Learning](https://mlsanity.com/paper/2203.02360/) (2022)
- [Efficient correlation-based discretization of continuous variables for annealing machines](https://mlsanity.com/paper/2301.07244/) (2023)
- [A general learning scheme for classical and quantum Ising machines](https://mlsanity.com/paper/2310.18411/) (2023)
- [Adiabatic Quantum Support Vector Machines](https://mlsanity.com/paper/2401.12485/) (2024)
- [Quantum-Classical Sentiment Analysis](https://mlsanity.com/paper/2409.16928/) (2024)
- [Collaborative filtering based on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2410.10381/) (2024)
- [Quantum Annealing for Robust Principal Component Analysis](https://mlsanity.com/paper/2501.10431/) (2025)
- [Exploring an implementation of quantum learning pipeline for support vector machines](https://mlsanity.com/paper/2509.04983/) (2025)
- [On the Impact of Weight Discretization in QUBO-Based SVM Training](https://mlsanity.com/paper/2510.26323/) (2025)

## Abstract (arXiv)

Kernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2000Q quantum annealer and study its performance in comparison to SVMs trained on conventional computers. The method is applied to both synthetic data and real data obtained from biology experiments. We find that the quantum annealer produces an ensemble of different solutions that often generalizes better to unseen data than the single global minimum of an SVM trained on a conventional computer, especially in cases where only limited training data is available. For cases with more training data than currently fits on the quantum annealer, we show that a combination of classifiers for subsets of the data almost always produces stronger joint classifiers than the conventional SVM for the same parameters.