Experimental neural network enhanced quantum tomography

Abstract

Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well developed and benefit from precise knowledge of the model describing state preparation and the measurement apparatus. However, physical models suffer from intrinsic limitations as actual measurement operators and trial states cannot be known precisely. This scenario inevitably leads to state-preparation-and-measurement (SPAM) errors degrading reconstruction performance. Here we develop and experimentally implement a machine learning based protocol reducing SPAM errors. We trained a supervised neural network to filter the experimental data and hence uncovered salient patterns that characterize the measurement probabilities for the original state and the ideal experimental apparatus free from SPAM errors. We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements. The average reconstruction fidelity is shown to be enhanced by 10% and 27%, respectively. The presented methods apply to the vast range of quantum experiments which rely on tomography.arXiv:1904.05902v2 [quant-ph]

Quantum motional state tomography with nonquadratic potentials and neural networks

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Experimental quantum homodyne tomography via machine learning

“…Very recently, the method was used in application to an experimental Rydberg-atom simulator with eight and nine atoms, using a pure-state, constant-phase approximation and measurements in a single basis [29]. Neural network techniques in the context of QST were also employed, albeit in a very different setting, to pre-process the data, thereby reducing the effect of state preparation and measurement errors [30].…”

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