# Nonnegative/binary matrix factorization with a D-Wave quantum annealer

arXiv: https://arxiv.org/abs/1704.01605 · HTML: https://mlsanity.com/paper/1704.01605/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2017.

## Built on by (most cited first, 23 of 23)

- [Quantum Permutation Synchronization](https://mlsanity.com/paper/2101.07755/) (2021)
- [Q-Match: Iterative Shape Matching via Quantum Annealing](https://mlsanity.com/paper/2105.02878/) (2021)
- [Adiabatic Quantum Graph Matching with Permutation Matrix Constraints](https://mlsanity.com/paper/2107.04032/) (2021)
- [Quantum Motion Segmentation](https://mlsanity.com/paper/2203.13185/) (2022)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering](https://mlsanity.com/paper/2405.19722/) (2024)
- [Quantum Visual Feature Encoding Revisited](https://mlsanity.com/paper/2405.19725/) (2024)
- [Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming](https://mlsanity.com/paper/2204.10579/) (2022)
- [Robust Fitting on a Gate Quantum Computer](https://mlsanity.com/paper/2409.02006/) (2024)
- [FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes](https://mlsanity.com/paper/2210.17037/) (2022)
- [Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing](https://mlsanity.com/paper/2311.01028/) (2023)
- [Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations](https://mlsanity.com/paper/2212.01312/) (2022)
- [Quantum autoencoders for image classification](https://mlsanity.com/paper/2502.15254/) (2025)
- [Analyzing the Effectiveness of Quantum Annealing with Meta-Learning](https://mlsanity.com/paper/2408.00570/) (2024)
- [Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2501.02114/) (2025)
- [Minor Embedding for Quantum Annealing with Reinforcement Learning](https://mlsanity.com/paper/2507.16004/) (2025)
- [Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum Annealing](https://mlsanity.com/paper/2110.07239/) (2021)
- [Convex Non-negative Matrix Factorization Through Quantum Annealing](https://mlsanity.com/paper/2203.15634/) (2022)
- [Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chains](https://mlsanity.com/paper/2312.00264/) (2023)
- [Collaborative filtering based on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2410.10381/) (2024)
- [Binary Quadratic Quantization: Beyond First-Order Quantization for Real-Valued Matrix Compression](https://mlsanity.com/paper/2510.18650/) (2025)
- [Non-Negative Matrix Factorization Using Non-Von Neumann Computers](https://mlsanity.com/paper/2512.00675/) (2025)
- [Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder](https://mlsanity.com/paper/2606.27411/) (2026)

## Abstract (arXiv)

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method can be used to analyze large datasets. The D-Wave only limits the number of features that can be extracted from the dataset. We apply this method to learn the features from a set of facial images.