# Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.04095 · HTML: https://mlsanity.com/paper/2512.04095/

## 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.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 6 of 6)

- [Tianshou: a Highly Modularized Deep Reinforcement Learning Library](https://mlsanity.com/paper/2107.14171/) (2021)
- [Reinforcement Learning with Quantum Variational Circuits](https://mlsanity.com/paper/2008.07524/) (2020)
- [Quantum reinforcement learning in continuous action space](https://mlsanity.com/paper/2012.10711/) (2020)
- [Variational Quantum Soft Actor-Critic](https://mlsanity.com/paper/2112.11921/) (2021)
- [Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems](https://mlsanity.com/paper/2304.10159/) (2023)
- [Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning](https://mlsanity.com/paper/2507.13265/) (2025)

## 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.