Advancing MG Energy Management: A Rolling Horizon Optimization Framework for Three-Phase Unbalanced Networks Integrating Convex Formulations.

Quantum circuit compilation plays a fundamental role in optimizing quantum algorithms for execution on near-term quantum hardware. Traditional compilation approaches rely on heuristic or rule-based methods, which may not fully exploit the potential of advanced machine learning techniques. This study explores the synergy between reinforcement learning (RL) and supervised learning (SL) to enhance quantum circuit compilation efficiency. The proposed hybrid approach leverages RL for decision-making in circuit transformations while using SL to refine and accelerate learning through historical data. Experimental results demonstrate that this methodology achieves superior gate count reduction and depth optimization compared to conventional techniques, paving the way for more efficient quantum computing applications.

Keywords: Quantum circuit compilation, reinforcement learning, supervised learning, hybrid machine learning, quantum computing optimization.

Would you like to learn more about how reinforcement learning and supervised learning can improve quantum circuit compilation? This study was published on arXiv, an open-access repository for cutting-edge research in physics, mathematics, and computer science. You can read the full article at the following link: https://arxiv.org/pdf/2503.15394.