Journal
NPJ QUANTUM INFORMATION
Volume 5, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41534-019-0222-3
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Funding
- National Natural Science Foundation of China [11175094, 11605153, 11605005, 11875159, U1801661, 11905099, 11975117]
- National Basic Research Program of China [2015CB921002]
- Science, Technology and Innovation Commission of Shenzhen Municipality [ZDSYS20170303165926217, JCYJ20170412152620376]
- Guangdong Innovative and Entrepreneurial Research Team Program [2016ZT06D348]
- Guangdong Basic and Applied Basic Research Foundation [2019A1515011383]
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Canadian Institute for Advanced Research (CIFAR)
- Chinese Ministry of Education [20173080024]
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Quantum state tomography is a daunting challenge of experimental quantum computing, even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the full state thereafter is hard. Here, we present a machine-learning method to recover the ground states of k-local Hamiltonians from just the local information, where a fully connected neural network is built to fulfill the task with up to seven qubits. In particular, we test the neural network model with a practical dataset, that in a 4-qubit nuclear magnetic resonance system our method yields global states via the 2-local information with high accuracy. Our work paves the way towards scalable state tomography in large quantum systems.
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