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• W3103174826 abstract "As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices. Computational techniques can be used to reconstruct a state from data, however the growing number of qubits demands ongoing algorithmic advances in order to keep pace with experiments. In this paper, we present an open-source software package called QuCumber that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements. QuCumber uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits. New measurements can be generated from the machine to obtain physical observables not easily accessible from the original data." @default.
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• W3103174826 date "2019-07-16" @default.
• W3103174826 modified "2023-10-10" @default.
• W3103174826 title "QuCumber: wavefunction reconstruction with neural networks" @default.
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• W3103174826 doi "https://doi.org/10.21468/scipostphys.7.1.009" @default.
• W3103174826 hasPublicationYear "2019" @default.
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