mlsanityData to 14 Oct 2026

Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning

arXiv 2512.04095 ↗First posted 21 Nov 2025eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Dynamic security control (DSC) is considered a pivotal step for the future power grid, which is increasingly penetrated by inverter-based resources. However, the efficiency of such practices, whether governed by automatic generation control or virtual inertia scheduling, can be intractable due to the complexity of the problem and the need to solve the differentialalgebraic equation in a timely manner with the required accuracy. In this regard, the model-free deep reinforcement learning algorithm demonstrates reliable performance. In addition, the introduction of fault-tolerant and near-term quantum computing terminologies, i.e., noisy intermediate-scale quantum, opens avenues for improving the performance of model-free algorithms leveraging quantum capabilities. This paper provides an organized framework and assesses its dependability by evaluating the performance of a quantum-embedded algorithm on the DSC of the IEEE 39-bus test system. Hence, the obtained results demonstrate promising applications, along with shortcomings that can be addressed and further developed later.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 32 references that are in the map. First posted on arXiv 21 Nov 2025.

Builds on

6 of 32 references.

References · 6

  1. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  2. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  3. 2020Quantum reinforcement learning in continuous action spacecited by 29
  4. 2021Variational Quantum Soft Actor-Criticcited by 15
  5. 2023Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problemscited by 2
  6. 2025Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learningcited by 1

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  1. 2025Quantum-Accelerated Deep Reinforcement Learning for Frequency Regulation Enhancement
  2. 2025Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment
  3. 2025Lyapunov-Aware Quantum-Inspired Reinforcement Learning for Continuous-Time Vehicle Control: A Feasibility Study
  4. 2025Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
  5. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
  6. 2026Q-DASC: State-of-the-Art Safe Quantum Control for HVAC under Local Model Misspecification
  7. 2026Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation
  8. 2024Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control