mlsanityData to 14 Oct 2026

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

arXiv 1704.01605 ↗First posted 5 Apr 2017cs.LG quant-ph stat.MLcited by 23

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 2; 2024: 5; 2025: 5; 2026: 1.

23 papers.

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  1. 2021Quantum Permutation Synchronizationcited by 32
  2. 2021Q-Match: Iterative Shape Matching via Quantum Annealingcited by 27
  3. 2021Adiabatic Quantum Graph Matching with Permutation Matrix Constraintscited by 20
  4. 2022Quantum Motion Segmentationcited by 16
  5. 2021Feature Selection for Recommender Systems with Quantum Computingcited by 14
  6. 2024QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clusteringcited by 11
  7. 2024Quantum Visual Feature Encoding Revisitedcited by 10
  8. 2022Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programmingcited by 6
  9. 2024Robust Fitting on a Gate Quantum Computercited by 5
  10. 2022FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodescited by 4
  11. 2023Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealingcited by 4
  12. 2022Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitationscited by 2
11 more
  1. 2025Quantum autoencoders for image classificationcited by 2
  2. 2024Analyzing the Effectiveness of Quantum Annealing with Meta-Learningcited by 1
  3. 2025Relaxation-assisted reverse annealing on nonnegative/binary matrix factorizationcited by 1
  4. 2025Minor Embedding for Quantum Annealing with Reinforcement Learningcited by 1
  5. 2021Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum Annealingcited by 0
  6. 2022Convex Non-negative Matrix Factorization Through Quantum Annealingcited by 0
  7. 2023Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chainscited by 0
  8. 2024Collaborative filtering based on nonnegative/binary matrix factorizationcited by 0
  9. 2025Binary Quadratic Quantization: Beyond First-Order Quantization for Real-Valued Matrix Compressioncited by 0
  10. 2025Non-Negative Matrix Factorization Using Non-Von Neumann Computerscited by 0
  11. 2026Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencodercited by 0