New Pre-Print “Designing Fast Quantum Gates with Tunable Couplers: A Reinforcement Learning Approach”

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Achieving fast and high-fidelity two-qubit entangling gates is crucial for future fault-tolerant error-correcting codes in scalable systems as well as for efficient implementation of quantum algorithms in current noisy devices. Our preprint ( presents an advancement in quantum computing, leveraging reinforcement learning (RL) to rapidly generate two-qubit gates in superconducting qubits, achieving an unprecedented 11 ns long controlled-Z gate while maintaining a high fidelity. This marks a fivefold improvement over existing methods, underscoring RL’s potential in mitigating leakage errors and significantly expediting quantum gate operations, hence showing immense promise for advancing quantum computing hardware systems, paving the way for enhanced performance in noisy environments.