Quantum State Tomography with Conditional Generative Adversarial Networks
This work addresses the problem of efficient quantum state reconstruction for researchers in quantum computing, though it is incremental as it adapts existing CGAN methods to a specific domain.
The authors tackled the challenge of quantum state tomography in intermediate-scale quantum devices by applying conditional generative adversarial networks (CGANs) with custom layers to reconstruct density matrices, achieving high fidelity orders of magnitude faster and from less data than standard maximum-likelihood methods.
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.