Learning Temporal Quantum Tomography

TL;DR

This paper introduces a novel recurrent machine learning-based quantum tomography method tailored for quantum systems with temporal processing, enabling efficient characterization of quantum devices' control and memory capabilities.

Contribution

It develops a practical approximate tomography approach for temporally processed quantum systems using quantum reservoir computing and recurrent learning.

Findings

Demonstrated quantum learning tasks using the proposed method.

Proposed a quantum short-term memory capacity metric.

Showed effectiveness in evaluating near-term quantum devices.

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.