Nam Nguyen, Ho and Motzoi, Felix and Metcalf, Mekena and Birgitta Whaley, K. and Bukov, Marin and Schmitt, Markus (2024) Reinforcement learning pulses for transmon qubit entangling gates. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 5 (2): 025066. ISSN 2632-2153
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DYNAMICS; quantum control; reinforcement learning; transmon qubits; entangling gates; deep learning |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Maschinelles Lernen (Prof. Dr. Merle Behr) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 09 Dec 2025 07:23 |
| Last Modified: | 09 Dec 2025 07:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65001 |
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