# Quantum-Accelerated Deep Reinforcement Learning for Frequency Regulation Enhancement

arXiv: https://arxiv.org/abs/2512.04439 · HTML: https://mlsanity.com/paper/2512.04439/

## 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 4 of its 20 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Placed in

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

## Builds on (most cited first, 4 of 4)

- [Quantum reinforcement learning in continuous action space](https://mlsanity.com/paper/2012.10711/) (2020)
- [Benchmarking Quantum Reinforcement Learning](https://mlsanity.com/paper/2501.15893/) (2025)
- [Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2203.01292/) (2022)
- [Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies](https://mlsanity.com/paper/2508.03194/) (2025)

## Abstract (arXiv)

In modern power systems, frequency regulation is a fundamental prerequisite for ensuring system reliability and assessing the robustness of expansion projects. Conventional feedback control schemes, however, exhibit limited accuracy under varying operating conditions because their gains remain static. Consequently, deep reinforcement learning methods are increasingly employed to design adaptive controllers that can be generalized to diverse frequency control tasks. At the same time, recent advances in quantum computing provide avenues for embedding quantum capabilities into such critical applications. In particular, the potential of quantum algorithms can be more effectively explored and harnessed on near-term quantum devices by leveraging insights from active controller design. In this work, we incorporate a quantum circuit together with an ansatz into the operation of a deep deterministic policy gradient agent. The simulation results of the IEEE 14-bus test system demonstrate the potential of this integrated approach that can achieve reliable, robust performance across diverse real-world challenges.