# Reverse Annealing for Nonnegative/Binary Matrix Factorization

arXiv: https://arxiv.org/abs/2007.05565 · HTML: https://mlsanity.com/paper/2007.05565/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers](https://mlsanity.com/paper/2205.04346/) (2022)
- [Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing](https://mlsanity.com/paper/2311.01028/) (2023)
- [Single-Qubit Fidelity Assessment of Quantum Annealing Hardware](https://mlsanity.com/paper/2104.03335/) (2021)
- [Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2501.02114/) (2025)
- [Binary Orthogonal Non-negative Matrix Factorization](https://mlsanity.com/paper/2210.10660/) (2022)
- [Collaborative filtering based on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2410.10381/) (2024)
- [Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share](https://mlsanity.com/paper/2501.15865/) (2025)
- [Non-Negative Matrix Factorization Using Non-Von Neumann Computers](https://mlsanity.com/paper/2512.00675/) (2025)

## Abstract (arXiv)

It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.