# Universal Quantum Control through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1803.01857 · HTML: https://mlsanity.com/paper/1803.01857/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2018.

## Built on by (most cited first, 30 of 44)

- [Quantum Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.07715/) (2021)
- [Quantum Architecture Search via Continual Reinforcement Learning](https://mlsanity.com/paper/2112.05779/) (2021)
- [An Invitation to Distributed Quantum Neural Networks](https://mlsanity.com/paper/2211.07056/) (2022)
- [A Primer on Quantum Machine Learning](https://mlsanity.com/paper/2511.15969/) (2025)
- [Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits](https://mlsanity.com/paper/2203.16707/) (2022)
- [Privacy-preserving machine learning with tensor networks](https://mlsanity.com/paper/2202.12319/) (2022)
- [Identifying optimal cycles in quantum thermal machines with reinforcement-learning](https://mlsanity.com/paper/2108.13525/) (2021)
- [Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning](https://mlsanity.com/paper/2204.06904/) (2022)
- [Learning a quantum computer's capability](https://mlsanity.com/paper/2304.10650/) (2023)
- [Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search](https://mlsanity.com/paper/2502.03962/) (2025)
- [Control of Stochastic Quantum Dynamics by Differentiable Programming](https://mlsanity.com/paper/2101.01190/) (2021)
- [Practical distributed quantum information processing with LOCCNet](https://mlsanity.com/paper/2101.12190/) (2021)
- [Deep reinforcement learning for quantum multiparameter estimation](https://mlsanity.com/paper/2209.00671/) (2022)
- [Quantum compiling with a variational instruction set for accurate and fast quantum computing](https://mlsanity.com/paper/2203.15574/) (2022)
- [Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions](https://mlsanity.com/paper/2106.01779/) (2021)
- [Sample-efficient Model-based Reinforcement Learning for Quantum Control](https://mlsanity.com/paper/2304.09718/) (2023)
- [Exploring Quantum Control Landscape and Solution Space Complexity through Dimensionality Reduction & Optimization Algorithms](https://mlsanity.com/paper/2502.11905/) (2025)
- [Active Learning for the Optimal Design of Multinomial Classification in Physics](https://mlsanity.com/paper/2109.08612/) (2021)
- [Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning](https://mlsanity.com/paper/2204.04785/) (2022)
- [Decoding surface codes with deep reinforcement learning and probabilistic policy reuse](https://mlsanity.com/paper/2212.11890/) (2022)
- [Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations](https://mlsanity.com/paper/2403.03761/) (2024)
- [Artificially intelligent Maxwell's demon for optimal control of open quantum systems](https://mlsanity.com/paper/2408.15328/) (2024)
- [Reinforcement learning to learn quantum states for Heisenberg scaling accuracy](https://mlsanity.com/paper/2412.02334/) (2024)
- [Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments](https://mlsanity.com/paper/2507.18606/) (2025)
- [When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Control](https://mlsanity.com/paper/2601.18973/) (2026)
- [Quantum Stream Learning](https://mlsanity.com/paper/2112.06628/) (2021)
- [Parallel bandit architecture based on laser chaos for reinforcement learning](https://mlsanity.com/paper/2205.09543/) (2022)
- [Beyond Heisenberg Limit Quantum Metrology through Quantum Signal Processing](https://mlsanity.com/paper/2209.11207/) (2022)
- [Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement Learning](https://mlsanity.com/paper/2212.10705/) (2022)
- [Asymmetric quantum decision-making](https://mlsanity.com/paper/2305.02117/) (2023)

## Abstract (arXiv)

Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization. They harness non-local regularities of noisy control trajectories and facilitate transfer learning between tasks. To leverage these powerful capabilities for quantum control optimization, we propose a new control framework to simultaneously optimize the speed and fidelity of quantum computation against both leakage and stochastic control errors. For a broad family of two-qubit unitary gates that are important for quantum simulation of many-electron systems, we improve the control robustness by adding control noise into training environments for reinforcement learning agents trained with trusted-region-policy-optimization. The agent control solutions demonstrate a two-order-of-magnitude reduction in average-gate-error over baseline stochastic-gradient-descent solutions and up to a one-order-of-magnitude reduction in gate time from optimal gate synthesis counterparts.