Reinforcement learning pulses for transmon qubit entangling gates

Abstract

The utility of a quantum computer depends heavily on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor - in contrast to many established gate implementation strategies. In this work, we utilize a continuous-control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency, fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.

18 + 8 pages, 13 + 6 figures

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Cited by in corpus (9)

• Artificial Intelligence for Quantum Computing
• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Fast generation of entanglement between coupled spins using optimization and deep learning methods
• TorchQC -- A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control
• Minimal Time Robust Control for Two Superconducting Qubits
• Quantum noise modeling through Reinforcement Learning
• Achieving fast and robust perfect entangling gates via reinforcement learning
• Geometric quantum control and the random Schrödinger equation
• Low-rank optimal control of quantum devices