Learning Temporal Quantum Tomography

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.

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

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Cited by in corpus (10)

• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Natural quantum reservoir computing for temporal information processing
• Potential and limitations of quantum extreme learning machines
• Quantum reservoir computing in finite dimensions
• Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks
• Data-Centric Machine Learning in Quantum Information Science
• Adaptive Online Learning of Quantum States
• Entanglement quantification enhanced by dark count correction
• The surprising persistence of time-dependent quantum entanglement