mlsanityData to 14 Oct 2026

Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment

arXiv 2504.14412 ↗First posted 19 Apr 2025eess.SY cs.LG cs.SYcited by 1

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

The increasingly challenging task of maintaining power grid security requires innovative solutions. Novel approaches using reinforcement learning (RL) agents have been proposed to help grid operators navigate the massive decision space and nonlinear behavior of these complex networks. However, applying RL to power grid security assessment, specifically for combinatorially troublesome contingency analysis problems, has proven difficult to scale. The integration of quantum computing into these RL frameworks helps scale by improving computational efficiency and boosting agent proficiency by leveraging quantum advantages in action exploration and model-based interdependence. To demonstrate a proof-of-concept use of quantum computing for RL agent training and simulation, we propose a hybrid agent that runs on quantum hardware using IBM's Qiskit Runtime. We also provide detailed insight into the construction of parameterized quantum circuits (PQCs) for generating relevant quantum output. This agent's proficiency at maintaining grid stability is demonstrated relative to a benchmark model without quantum enhancement using N-k contingency analysis. Additionally, we offer a comparative assessment of the training procedures for RL models integrated with a quantum backend.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 27 references that are in the map. First posted on arXiv 19 Apr 2025.

Builds on

6 of 27 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2019Parameterized quantum circuits as machine learning modelscited by 332
  4. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
  5. 2020Quantum computing for energy systems optimization: Challenges and opportunitiescited by 15
  6. 2018Stochastic defense against complex grid attackscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Quantum Artificial Intelligence for Mission-Critical Systems: Foundations, Architectural Elements, and Future Directionscited by 1

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  5. 2026Reinforcement Learning for Quantum Technology
  6. 2024An Introduction to Quantum Reinforcement Learning (QRL)
  7. 2025Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
  8. 2026MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments