A projected least-squares tomography protocol adapted for distributed quantum processors delivers rigorous non-asymptotic trace-norm error bounds that grow exponentially with the number of nodes and certified bounds on entanglement negativity.
Predicting many properties of a quantum system from very few measurements.Nature Physics, 16(10):1050–1057, June 2020
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A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.
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Rigorous quantum state tomography for distributed quantum computing
A projected least-squares tomography protocol adapted for distributed quantum processors delivers rigorous non-asymptotic trace-norm error bounds that grow exponentially with the number of nodes and certified bounds on entanglement negativity.
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Non-variational supervised quantum kernel methods: a review
A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.