arXiv 2103.139732021-03quant-phcs.LGphysics.data-an

学习时间量子断层扫描

Learning Temporal Quantum Tomography

The University of Tokyo（东京大学）；Next Generation Artificial Intelligence Research Center, The University of Tokyo（东京大学下一代人工智能研究中心）

Quoc Hoan Tran, Kohei Nakajima

备注：: Main: 6 pages, 4 figures; Supplementary: 29 pages -> Revised version; Close to the accepted version. The results of tomography task for the quantum switch have been added to the Supplementary Material
发表信息：: Phys. Rev. Lett. 127, 260401 (2021)
DOI：: 10.1103/PhysRevLett.127.260401

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摘要：

在制备量子态的过程中，量化和验证控制水平是构建量子器件的核心挑战。量子态的特征来自实验测量，使用称为层析成像的程序，这需要大量的资源。此外，还没有制定用于具有时间处理的量子设备的断层扫描，其与标准断层扫描根本不同。我们开发了一个实用的和近似的层析成像方法，使用一个经常性的机器学习框架，这种有趣的情况。该方法基于称为量子库的系统与量子态流之间的重复量子相互作用。来自储存器的测量数据连接到线性读出器，以训练施加到输入流的量子信道之间的递归关系。我们展示了我们的量子学习任务的算法，然后提出了一个量子短期记忆能力，以评估短期量子设备的时间处理能力。

英文摘要：

Quantifying and verifying the control level in preparing a quantum state are central challenges in building quantum devices. The quantum state is characterized from experimental measurements, using a procedure known as tomography, which requires a vast number of resources. Furthermore, the tomography for a quantum device with temporal processing, which is fundamentally different from the standard tomography, has not been formulated. We develop a practical and approximate tomography method using a recurrent machine learning framework for this intriguing situation. The method is based on repeated quantum interactions between a system called quantum reservoir with a stream of quantum states. Measurement data from the reservoir are connected to a linear readout to train a recurrent relation between quantum channels applied to the input stream. We demonstrate our algorithms for quantum learning tasks followed by the proposal of a quantum short-term memory capacity to evaluate the temporal processing ability of near-term quantum devices.

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