Flexible learning of quantum states with generative query neural networks

Journal article

Resource-efficient quantum state tomography is one of the key ingredients of future quantum technologies. In this work, we propose a new tomography protocol combining standard quantum state reconstruction methods with an attention-based neural network architecture. We show how the proposed protocol is able to improve the averaged fidelity reconstruction over linear inversion and maximum-likelihood estimation in the finite-statistics regime, reducing at least by an order of magnitude the amount of necessary training data. We demonstrate the potential use of our protocol in physically relevant scenarios, in particular, to certify metrological resources in the form of many-body entanglement generated during the spin squeezing protocols. This could be implemented with the current quantum simulator platforms, such as trapped ions, and ultra-cold atoms in optical lattices

Authors: Zhu, Y; Wu, Y-D; Bai, G; Wang, D-S; Wang, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Communications
• Volume: 13
• Issue: 1
• Pages: 6222-6222
• Article number: 6222
• Publication date: 2022-10-20
• DOI: 10.1038/s41467-022-33928-z
• EISSN: 2041-1723
• ISSN: 2041-1723
• Language: English
• Keywords: Artificial neural network; Computer science; Machine learning; Quantum state; Quantum mechanics; Generative grammar; Generative model; Theoretical computer science; Artificial intelligence; Physics; Quantum; Cluster analysis; Set (abstract data type); Representation (politics)