Enhancing quantum state tomography via resource-efficient attention-based neural networks

Abstract

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.

The Author Accepted Manuscript (AAM);

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• Optimizing quantum sensing networks via genetic algorithms and deep learning
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Deep Neural Network-assisted improvement of quantum compressed sensing tomography
• Neural quantum states for entanglement depth certification from randomized Pauli measurements