Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning
Articolo
Data di Pubblicazione:
2023
Citazione:
Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning / I. Gianani, C. Benedetti. - In: AVS QUANTUM SCIENCE. - ISSN 2639-0213. - 5:1(2023), pp. 014405.1-014405.9. [10.1116/5.0137398]
Abstract:
The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum walks, the parameters themselves may not be directly accessible, and, thus, it is necessary to find alternative estimation strategies exploiting other observables. Here, we perform the multiparameter estimation of the Hamiltonian parameters characterizing a continuous-time quantum walk over a line graph with n-neighbor interactions using a deep neural network model fed with experimental probabilities at a given evolution time. We compare our results with the bounds derived from estimation theory and find that the neural network acts as a nearly optimal estimator both when the estimation of two or three parameters is performed.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Machine learning; quantum walks; estimation theory; Cramer-Rao bound
Elenco autori:
I. Gianani, C. Benedetti
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