Continuous-variable quantum tomography of high-amplitude states

TL;DR

This paper introduces a neural network-based quantum tomography method for continuous-variable states, enabling high-amplitude state reconstruction with improved resource scaling and targeted phase space analysis.

Contribution

We develop a neural network approach for direct density matrix reconstruction in the continuous position basis, surpassing previous low-amplitude limitations.

Findings

Enables high-amplitude state tomography in continuous-variable systems

Resource requirements scale slowly with state amplitude

Allows targeted phase space region reconstruction

Abstract

Quantum state tomography is an essential component of modern quantum technology. In application to continuous-variable harmonic-oscilator systems, such as the electromagnetic field, existing tomography methods typically reconstruct the state in discrete bases, and are hence limited to states with relatively low amplitudes and energies. Here we overcome this limitation by utilizing a feed-forward neural network to obtain the density matrix directly in the continuous position basis. An important benefit of our approach is the ability to choose specific regions in the phase space for detailed reconstruction. This results in relatively slow scaling of the amount of resources required for the reconstruction with the state amplitude, and hence allows us to dramatically increase the range of amplitudes accessible with our method.