Quantum State Tomography with Conditional Generative Adversarial Networks ()
Journal article, 2021

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 dueling neural networks, a generator and a discriminator, learn multimodal 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, using orders of magnitude fewer iterative steps, and less data, than both accelerated projected-gradient-based and iterative maximum-likelihood estimation. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pretrained on similar quantum states.


Shahnawaz Ahmed

Chalmers, Microtechnology and Nanoscience (MC2), Applied Quantum Physics

Carlos Sánchez Munõz

Universidad Autonoma de Madrid (UAM)

F. Nori


University of Michigan

Anton Frisk Kockum

Chalmers, Microtechnology and Nanoscience (MC2), Applied Quantum Physics

Physical Review Letters

0031-9007 (ISSN) 1079-7114 (eISSN)

Vol. 127 14 140502

Subject Categories

Computer Engineering

Subatomic Physics





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