Quantum Compilation

Quantum compilation aims to translate high-level quantum algorithms into optimized sequences of instructions executable on specific quantum hardware, minimizing gate count and error while adhering to architectural constraints. Current research heavily utilizes machine learning, particularly reinforcement learning and transformer networks, to develop heuristic compilers that outperform traditional deterministic methods, especially for complex multi-qubit systems and modular architectures. These advancements are crucial for realizing the potential of noisy intermediate-scale quantum (NISQ) devices and accelerating the development of practical quantum algorithms by improving efficiency and fidelity.