# Curriculum-based Deep Reinforcement Learning for Quantum Control

arXiv: https://arxiv.org/abs/2012.15427 · HTML: https://mlsanity.com/paper/2012.15427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

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

- [Tomography of Quantum States from Structured Measurements via quantum-aware transformer](https://mlsanity.com/paper/2305.05433/) (2023)
- [Curriculum Learning with Quality-Driven Data Selection](https://mlsanity.com/paper/2407.00102/) (2024)
- [Auxiliary Task-based Deep Reinforcement Learning for Quantum Control](https://mlsanity.com/paper/2302.14312/) (2023)
- [Lyapunov-Aware Quantum-Inspired Reinforcement Learning for Continuous-Time Vehicle Control: A Feasibility Study](https://mlsanity.com/paper/2510.18852/) (2025)
- [Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systems](https://mlsanity.com/paper/2601.22339/) (2026)

## Abstract (arXiv)

Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel deep reinforcement learning approach by constructing a curriculum consisting of a set of intermediate tasks defined by a fidelity threshold. Tasks among a curriculum can be statically determined using empirical knowledge or adaptively generated with the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based deep reinforcement learning (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical simulations on closed quantum systems and open quantum systems demonstrate that the proposed method exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with fewer control pulses.