Reinforcement learning pulses for transmon qubit entangling gates

被引:2
|
作者
Nam Nguyen, Ho [1 ]
Motzoi, Felix [2 ]
Metcalf, Mekena [3 ,7 ]
Birgitta Whaley, K. [4 ]
Bukov, Marin [5 ]
Schmitt, Markus [2 ,6 ]
机构
[1] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA
[2] Forschungszentrum Julich, Inst Quantum Control PGI 8, D-52425 Julich, Germany
[3] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA
[4] Univ Calif Berkeley, Dept Chem, Berkeley, CA 94720 USA
[5] Max Planck Inst Phys Komplexer Syst, Nothnitzer Str 38, D-01187 Dresden, Germany
[6] Univ Regensburg, D-93053 Regensburg, Germany
[7] One Embarcadero Ctr, HSBC Holdings Plc, San Francisco, CA USA
来源
MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2024年 / 5卷 / 02期
关键词
quantum control; reinforcement learning; transmon qubits; entangling gates; deep learning; DYNAMICS;
D O I
10.1088/2632-2153/ad4f4d
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The utility of a quantum computer is highly dependent 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.
引用
收藏
页数:32
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