Quantum State Tomography with Conditional Generative Adversarial Networks

• URL: http://arxiv.org/abs/2008.03240v2
• Date: Fri, 4 Dec 2020 18:14:37 GMT
• Title: Quantum State Tomography with Conditional Generative Adversarial Networks
• Authors: Shahnawaz Ahmed, Carlos S\'anchez Mu\~noz, Franco Nori, Anton Frisk Kockum
• Abstract summary: We apply conditional generative adversarial networks (CGANs) to quantum state tomography (QST) In the CGAN framework, two duelling neural networks, a generator and a discriminator, learn multi-modal models from data. 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.
• Score: 0.7646713951724009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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