# Quantum bandit with amplitude amplification exploration in an adversarial environment

arXiv: https://arxiv.org/abs/2208.07144 · HTML: https://mlsanity.com/paper/2208.07144/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [A Survey on Quantum Reinforcement Learning](https://mlsanity.com/paper/2211.03464/) (2022)
- [Quantum contextual bandits and recommender systems for quantum data](https://mlsanity.com/paper/2301.13524/) (2023)
- [Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search](https://mlsanity.com/paper/2412.18208/) (2024)
- [Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization](https://mlsanity.com/paper/2509.16002/) (2025)
- [Quantum spatial best-arm identification via quantum walks](https://mlsanity.com/paper/2509.05890/) (2025)

## Abstract (arXiv)

The rapid proliferation of learning systems in an arbitrarily changing environment mandates the need for managing tensions between exploration and exploitation. This work proposes a quantum-inspired bandit learning approach for the learning-and-adapting-based offloading problem where a client observes and learns the costs of each task offloaded to the candidate resource providers, e.g., fog nodes. In this approach, a new action update strategy and novel probabilistic action selection are adopted, provoked by the amplitude amplification and collapse postulate in quantum computation theory, respectively. We devise a locally linear mapping between a quantum-mechanical phase in a quantum domain, e.g., Grover-type search algorithm, and a distilled probability-magnitude in a value-based decision-making domain, e.g., adversarial multi-armed bandit algorithm. The proposed algorithm is generalized, via the devised mapping, for better learning weight adjustments on favourable/unfavourable actions and its effectiveness is verified via simulation.