Learning Temporal Quantum Tomography

• URL: http://arxiv.org/abs/2103.13973v2
• Date: Mon, 29 Mar 2021 16:36:48 GMT
• Title: Learning Temporal Quantum Tomography
• Authors: Quoc Hoan Tran and Kohei Nakajima
• Abstract summary: Quantifying and verifying the control level in preparing a quantum state are central challenges in building quantum devices. We develop a practical and approximate tomography method using a recurrent machine learning framework. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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