Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design

Researchers propose Reinforcement Learning for Variational Quantum Circuits (RLVQC) to separate ansatz discovery from deployment in quantum circuit design, enabling the use of modular circuit blocks learned on small systems and applied to larger problems. This approach advances practical applications of quantum computing by overcoming scalability issues with classical machine learning methods as qubit numbers increase.

Read the full article at arXiv cs.LG (ML)

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