# Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

arXiv: https://arxiv.org/abs/2205.04346 · HTML: https://mlsanity.com/paper/2205.04346/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 9 of 9)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Support vector machines on the D-Wave quantum annealer](https://mlsanity.com/paper/1906.06283/) (2019)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [A Survey of Quantum Theory Inspired Approaches to Information Retrieval](https://mlsanity.com/paper/2007.04357/) (2020)
- [Reverse Annealing for Nonnegative/Binary Matrix Factorization](https://mlsanity.com/paper/2007.05565/) (2020)
- [Neural Feature Selection for Learning to Rank](https://mlsanity.com/paper/2102.11345/) (2021)

## Built on by (most cited first, 11 of 11)

- [Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing](https://mlsanity.com/paper/2311.01028/) (2023)
- [Quantum Sparse Coding](https://mlsanity.com/paper/2209.03788/) (2022)
- [Feature Selection for Classification with QAOA](https://mlsanity.com/paper/2211.02861/) (2022)
- [CRUISE on Quantum Computing for Feature Selection in Recommender Systems](https://mlsanity.com/paper/2407.02839/) (2024)
- [Analyzing the Effectiveness of Quantum Annealing with Meta-Learning](https://mlsanity.com/paper/2408.00570/) (2024)
- [Minor Embedding for Quantum Annealing with Reinforcement Learning](https://mlsanity.com/paper/2507.16004/) (2025)
- [Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer](https://mlsanity.com/paper/2310.00018/) (2023)
- [Performance-Driven QUBO for Recommender Systems on Quantum Annealers](https://mlsanity.com/paper/2410.15272/) (2024)
- [Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems](https://mlsanity.com/paper/2508.00027/) (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)

## Abstract (arXiv)

Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification. The experimental analysis includes 15 state-of-the-art datasets. The effectiveness obtained with quantum computing hardware is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems. In terms of scalability, current generation quantum computers are able to provide a limited speedup over certain classical algorithms and hybrid quantum-classical strategies show lower computational cost for problems of more than a thousand features.