mlsanityData to 14 Oct 2026

Multi-Armed Bandits and Quantum Channel Oracles

arXiv 2301.08544 ↗First posted 20 Jan 2023quant-ph stat.MLcited by 3

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Abstract (arXiv)

Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible when the arms and the randomness of the rewards of the arms can be queried in superposition. Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition. We show that then the query complexity is the same as for classical algorithms. This generalizes the prior result that no speed-up is possible for unstructured search when the oracle has positive failure probability.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 20 Jan 2023.

Builds on

12 of 49 references.

References · 12

  1. 2016Quantum Machine Learningcited by 326
  2. 2016Optimal Best Arm Identification with Fixed Confidencecited by 239
  3. 2021Quantum advantage in learning from experimentscited by 188
  4. 2013lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Banditscited by 149
  5. 2017Quantum machine learning: a classical perspectivecited by 95
  6. 2016Quantum Recommendation Systemscited by 70
  7. 2018q-means: A quantum algorithm for unsupervised machine learningcited by 55
  8. 2013Quantum support vector machine for big data classificationcited by 35
  9. 2016Towards Instance Optimal Bounds for Best Arm Identificationcited by 30
  10. 2020Quantum exploration algorithms for multi-armed banditscited by 22
  11. 2022Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regretscited by 19
  12. 2020Quantum Banditscited by 18

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Towards Noise-Resilient Quantum Multi-Armed and Stochastic Linear Banditscited by 0
  2. 2026Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimizationcited by 0
  3. 2026Quantum Multi-Armed Bandits and Linear Bandits: Lower Bounds and Algorithmscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
  2. 2025Quantum spatial best-arm identification via quantum walks
  3. 2026Reinforcement learning for quantum processes with memory
  4. 2024Faster Q-Learning Algorithms for Restless Bandits
  5. 2021Quantum Algorithms for Reinforcement Learning with a Generative Model
  6. 2025Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
  7. 2023Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
  8. 2021Solving Multi-Arm Bandit Using a Few Bits of Communication