# Quantum Machine Learning for Secondary Frequency Control

arXiv: https://arxiv.org/abs/2512.02065 · HTML: https://mlsanity.com/paper/2512.02065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [The complexity of non-stationary reinforcement learning](https://mlsanity.com/paper/2307.06877/) (2023)
- [Enhancing Classification Performance via Reinforcement Learning for Feature Selection](https://mlsanity.com/paper/2403.05979/) (2024)

## Abstract (arXiv)

Frequency control in power systems is critical to maintaining stability and preventing blackouts. Traditional methods like meta-heuristic algorithms and machine learning face limitations in real-time applicability and scalability. This paper introduces a novel approach using a pure variational quantum circuit (VQC) for real-time secondary frequency control in diesel generators. Unlike hybrid classical-quantum models, the proposed VQC operates independently during execution, eliminating latency from classical-quantum data exchange. The VQC is trained via supervised learning to map historical frequency deviations to optimal Proportional-Integral (PI) controller parameters using a pre-computed lookup table. Simulations demonstrate that the VQC achieves high prediction accuracy (over 90%) with sufficient quantum measurement shots and generalizes well across diverse test events. The quantum-optimized PI parameters significantly improve transient response, reducing frequency fluctuations and settling time.