Learning Temporal Quantum Tomography
arXiv:2103.13973 · doi:10.1103/PhysRevLett.127.260401
Abstract
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.
arXiv commentMain: 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
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