Learning Temporal Quantum Tomography
This addresses the challenge of characterizing quantum states in devices with temporal processing for quantum computing and device verification, representing an incremental advance in quantum tomography methods.
The authors tackled the problem of quantum state tomography for devices with temporal processing, which requires different methods than standard tomography, by developing a practical approximate tomography method using a recurrent machine learning framework with quantum reservoir interactions. They demonstrated their algorithms for quantum learning tasks and proposed a quantum short-term memory capacity metric to evaluate temporal processing in near-term quantum devices.
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.