# Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering

arXiv: https://arxiv.org/abs/2507.15063 · HTML: https://mlsanity.com/paper/2507.15063/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [Feature Selection on Quantum Computers](https://mlsanity.com/paper/2203.13261/) (2022)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models](https://mlsanity.com/paper/2408.03560/) (2024)
- [Quantum Annealing for Clustering](https://mlsanity.com/paper/1408.2035/) (2014)
- [Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing](https://mlsanity.com/paper/2310.12153/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces](https://mlsanity.com/paper/2606.06941/) (2026)

## Abstract (arXiv)

This paper explores the applications of quantum annealing (QA) and classical simulated annealing (SA) to a suite of combinatorial optimization problems in machine learning, namely feature selection, instance selection, and clustering. We formulate each task as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implement both quantum and classical solvers to compare their effectiveness. For feature selection, we propose several QUBO configurations that balance feature importance and redundancy, showing that quantum annealing (QA) produces solutions that are computationally more efficient. In instance selection, we propose a few novel heuristics for instance-level importance measures that extend existing methods. For clustering, we embed a classical-to-quantum pipeline, using classical clustering followed by QUBO-based medoid refinement, and demonstrate consistent improvements in cluster compactness and retrieval metrics. Our results suggest that QA can be a competitive and efficient tool for discrete machine learning optimization, even within the constraints of current quantum hardware.