Quantum State Tomography with Conditional Generative Adversarial Networks

TL;DR

This paper introduces a novel quantum state tomography method using conditional GANs that achieves faster, high-fidelity reconstructions with less data compared to traditional techniques.

Contribution

The paper presents a new CGAN-based approach for quantum state tomography, including custom neural layers for physical density matrix conversion, enabling efficient quantum state reconstruction.

Findings

Reconstructs optical quantum states with high fidelity

Operates significantly faster than maximum-likelihood methods

Can reconstruct states in a single evaluation after pre-training

Abstract

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two duelling neural networks, a generator and a discriminator, learn multi-modal models from data. We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix. To reconstruct the density matrix, the generator and discriminator networks train each other on data using standard gradient-based methods. We demonstrate that our QST-CGAN reconstructs optical quantum states with high fidelity orders of magnitude faster, and from less data, than a standard maximum-likelihood method. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pre-trained on similar quantum states.