M
Marcello Benedetti
Researcher at University College London
Publications - 44
Citations - 3073
Marcello Benedetti is an academic researcher from University College London. The author has contributed to research in topics: Quantum computer & Quantum. The author has an hindex of 17, co-authored 38 publications receiving 1824 citations. Previous affiliations of Marcello Benedetti include Ames Research Center.
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Parameterized quantum circuits as machine learning models
TL;DR: In this paper, the authors present the components of these models and discuss their application to a variety of data-driven tasks, such as supervised learning and generative modeling, as well as their application in machine learning applications.
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Parameterized quantum circuits as machine learning models
TL;DR: This Review presents components of parameterized quantum circuits and discusses their application to a variety of data-driven tasks such as supervised learning and generative modeling.
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Hierarchical quantum classifiers
Edward R. Grant,Marcello Benedetti,Shuxiang Cao,Shuxiang Cao,Andrew Hallam,Andrew Hallam,Joshua Lockhart,Vid Stojevic,Andrew G. Green,Simone Severini +9 more
TL;DR: In this article, a hierarchical tensor network is used for binary classification of quantum data encoded in a quantum state, which can be used to classify highly entangled quantum states, for which there is no known efficient classical method.
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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
TL;DR: In this paper, the authors theoretically motivate and empirically validate an initialization strategy which can resolve the barren plateau problem for practical applications, which involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity.
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Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
TL;DR: A systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures.