# Towards Noise-Resilient Quantum Multi-Armed and Stochastic Linear Bandits

arXiv: https://arxiv.org/abs/2603.18431 · HTML: https://mlsanity.com/paper/2603.18431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 19 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [A Survey on Contextual Multi-armed Bandits](https://mlsanity.com/paper/1508.03326/) (2015)
- [Quantum exploration algorithms for multi-armed bandits](https://mlsanity.com/paper/2007.07049/) (2020)
- [Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets](https://mlsanity.com/paper/2205.14988/) (2022)
- [Quantum Bandits](https://mlsanity.com/paper/2002.06395/) (2020)
- [Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access](https://mlsanity.com/paper/1807.00867/) (2018)
- [Quantum Approximate Counting, Simplified](https://mlsanity.com/paper/1908.10846/) (2019)
- [Multi-Armed Bandits and Quantum Channel Oracles](https://mlsanity.com/paper/2301.08544/) (2023)

## Abstract (arXiv)

Quantum multi-armed bandits (MAB) and stochastic linear bandits (SLB) have recently attracted significant attention, as their quantum counterparts can achieve quadratic speedups over classical MAB and SLB. However, most existing quantum MAB algorithms assume ideal quantum Monte Carlo (QMC) procedures on noise-free circuits, overlooking the impact of noise in current noisy intermediate-scale quantum (NISQ) devices. In this paper, we study a noise-robust QMC algorithm that improves estimation accuracy when querying quantum reward oracles. Building on this estimator, we propose noise-robust QMAB and QSLB algorithms that enhance performance in noisy environments while preserving the advantage over classical methods. Experiments show that our noise-robust approach improves QMAB estimation accuracy and reduces regret under several quantum noise models.