Data di Pubblicazione:
2018
Citazione:
Approximate supervised learning of quantum gates via ancillary qubits / L. Innocenti, L. Banchi, S. Bose, A. Ferraro, M. Paternostro. - In: INTERNATIONAL JOURNAL OF QUANTUM INFORMATION. - ISSN 0219-7499. - 16:8(2018 Dec), pp. 1840004.1-1840004.15. [10.1142/S021974991840004X]
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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
quantum-computation; quantum-gates; quantum-information;
Elenco autori:
L. Innocenti, L. Banchi, S. Bose, A. Ferraro, M. Paternostro
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