DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
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Machine learning optimizes adaptive quantum teleportation protocols to achieve higher fidelity than standard Bell-state methods in several noise models.
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Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
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Beyond Bell Teleportation: Machine-Learned Adaptive Protocols
Machine learning optimizes adaptive quantum teleportation protocols to achieve higher fidelity than standard Bell-state methods in several noise models.