Quantum Circuit Synthesis

Quantum circuit synthesis focuses on efficiently translating quantum algorithms into executable instructions for physical quantum computers, minimizing resource consumption and errors. Current research heavily utilizes machine learning, particularly reinforcement learning and diffusion models, to automate and optimize this process, often surpassing traditional methods in speed and performance for various circuit types and qubit counts. These advancements are crucial for realizing the potential of quantum computing, enabling the practical implementation and scaling of complex quantum algorithms across diverse hardware architectures.