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• W2889654966 abstract "We present strategies for the training of a qubit network aimed at the ancilla-assisted synthesis of multi-qubit gates based on a set of restricted resources. By assuming the availability of only time-independent single and two-qubit interactions, we introduce and describe a supervised learning strategy implemented through momentum-stochastic gradient descent with automatic differentiation methods. We demonstrate the effectiveness of the scheme by discussing the implementation of nontrivial three qubit operations, including a QFT and a half-adder gate." @default.
• W2889654966 created "2018-09-27" @default.
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• W2889654966 date "2018-12-01" @default.
• W2889654966 modified "2023-09-25" @default.
• W2889654966 title "Approximate supervised learning of quantum gates via ancillary qubits" @default.
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• W2889654966 doi "https://doi.org/10.1142/s021974991840004x" @default.
• W2889654966 hasPublicationYear "2018" @default.
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