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Neural-network quantum state tomography
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DOI：10.1038/s41567-018-0048-5.png)
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Abstract
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The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods to validate and fully exploit quantum resources. Quantum state tomography (QST) aims to reconstruct the full quantum state from simple measurements, and therefore provides a key tool to obtain reliable analytics(1-3). However, exact brute-force approaches to QST place a high demand on computational resources, making them unfeasible for anything except small systems(4,5). Here we show how machine learning techniques can be used to perform QST of highly entangled states with more than a hundred qubits, to a high degree of accuracy. We demonstrate that machine learning allows one to reconstruct traditionally challenging many-body quantities-such as the entanglement entropyfrom simple, experimentally accessible measurements. This approach can benefit existing and future generations of devices ranging from quantum computers to ultracold-atom quantum simulators(6-8).
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