Supervised learning for robust quantum control in composite-pulse systems

• URL: http://arxiv.org/abs/2308.11861v2
• Date: Sun, 7 Apr 2024 09:10:00 GMT
• Title: Supervised learning for robust quantum control in composite-pulse systems
• Authors: Zhi-Cheng Shi, Jun-Tong Ding, Ye-Hong Chen, Jie Song, Yan Xia, X. X. Yi, Franco Nori,
• Abstract summary: We develop a supervised learning model for implementing robust quantum control in composite-pulse systems. This model exhibits great resistance to all kinds of systematic errors, including single, multiple, and time-varying errors. This work provides a highly efficient learning model for fault-tolerant quantum computation by training various physical parameters.
• Score: 7.474008952791777
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this work, we develop a supervised learning model for implementing robust quantum control in composite-pulse systems, where the training parameters can be either phases, detunings, or Rabi frequencies. This model exhibits great resistance to all kinds of systematic errors, including single, multiple, and time-varying errors. We propose a modified gradient descent algorithm for adapting the training of phase parameters, and show that different sampling methods result in different robust performances. In particular, there is a trade-off between high fidelity and robustness for a given number of training parameters, and both can be simultaneously enhanced by increasing the number of training parameters (pulses). For its applications, we demonstrate that the current model can be used for achieving high-fidelity arbitrary superposition states and universal quantum gates in a robust manner. This work provides a highly efficient learning model for fault-tolerant quantum computation by training various physical parameters.

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