# Framework for learning agents in quantum environments

arXiv: https://arxiv.org/abs/1507.08482 · HTML: https://mlsanity.com/paper/1507.08482/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2015.

## Built on by (most cited first, 9 of 9)

- [A Survey on Quantum Reinforcement Learning](https://mlsanity.com/paper/2211.03464/) (2022)
- [Variational Quantum Reinforcement Learning via Evolutionary Optimization](https://mlsanity.com/paper/2109.00540/) (2021)
- [Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning](https://mlsanity.com/paper/2206.04741/) (2022)
- [Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets](https://mlsanity.com/paper/2205.14988/) (2022)
- [Uncovering Instabilities in Variational-Quantum Deep Q-Networks](https://mlsanity.com/paper/2202.05195/) (2022)
- [A Primer on Quantum Machine Learning](https://mlsanity.com/paper/2511.15969/) (2025)
- [Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret](https://mlsanity.com/paper/2302.10796/) (2023)
- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)
- [From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware](https://mlsanity.com/paper/2509.07614/) (2025)

## Abstract (arXiv)

In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically specified environments which allow for quantum enhancements in learning, by contrasting environments to quantum oracles. We show that whether or not quantum improvements are at all possible depends on the internal structure of the quantum environment. If the environments are constructed and the internal structure is appropriately chosen, or if the agent has limited capacities to influence the internal states of the environment, we show that improvements in learning times are possible in a broad range of scenarios. Such scenarios we call luck-favoring settings. The case of constructed environments is particularly relevant for the class of model-based learning agents, where our results imply a near-generic improvement.