# Feature Selection on Quantum Computers

arXiv: https://arxiv.org/abs/2203.13261 · HTML: https://mlsanity.com/paper/2203.13261/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [Survey on Feature Selection](https://mlsanity.com/paper/1510.02892/) (2015)

## Built on by (most cited first, 21 of 21)

- [Dynamic Range Reduction via Branch-and-Bound](https://mlsanity.com/paper/2409.10863/) (2024)
- [An Advantage Using Feature Selection with a Quantum Annealer](https://mlsanity.com/paper/2211.09756/) (2022)
- [A novel feature selection method based on quantum support vector machine](https://mlsanity.com/paper/2311.17646/) (2023)
- [Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering](https://mlsanity.com/paper/2507.15063/) (2025)
- [Quantum feature-map learning with reduced resource overhead](https://mlsanity.com/paper/2510.03389/) (2025)
- [αQBoost: An Iteratively Weighted Adiabatic Trained Classifier](https://mlsanity.com/paper/2210.07984/) (2022)
- [Computing Marginal and Conditional Divergences between Decomposable Models with Applications](https://mlsanity.com/paper/2310.09129/) (2023)
- [Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models](https://mlsanity.com/paper/2407.21062/) (2024)
- [Performance-Driven QUBO for Recommender Systems on Quantum Annealers](https://mlsanity.com/paper/2410.15272/) (2024)
- [Quantum Annealing for Robust Principal Component Analysis](https://mlsanity.com/paper/2501.10431/) (2025)
- [Quantum Annealing Feature Selection on Light-weight Medical Image Datasets](https://mlsanity.com/paper/2502.19201/) (2025)
- [Standardization of Multi-Objective QUBOs](https://mlsanity.com/paper/2504.12419/) (2025)
- [Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems](https://mlsanity.com/paper/2508.00027/) (2025)
- [On the Impact of Weight Discretization in QUBO-Based SVM Training](https://mlsanity.com/paper/2510.26323/) (2025)
- [Quantum feature encoding optimization](https://mlsanity.com/paper/2512.02422/) (2025)
- [Towards interpretable AI with quantum annealing feature selection](https://mlsanity.com/paper/2604.25649/) (2026)
- [Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware](https://mlsanity.com/paper/2604.26834/) (2026)
- [Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search](https://mlsanity.com/paper/2606.09081/) (2026)
- [QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language](https://mlsanity.com/paper/2609.10629/) (2026)
- [From Risk Scoring to Risk Allocation: A Density-Driven Framework for Diverse Monitoring in Multi-Agent Systems](https://mlsanity.com/paper/2609.26146/) (2026)
- [Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection](https://mlsanity.com/paper/2610.03161/) (2026)

## Abstract (arXiv)

In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higherquality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark datasets. We observe competitive performance.