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arXiv · 2103.13973

Learning Temporal Quantum Tomography

Quoc Hoan Tran·Kohei Nakajima
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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.

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Quoc Hoan Tran, Kohei Nakajima. 2021-12-07. Learning Temporal Quantum Tomography. https://doi.org/10.1103/physrevlett.127.260401

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