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.
Papers
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Dissertation
Quantum-classical generative models for machine learning
TL;DR: A quantum generative adversarial network that works with quantum data is derive and the generative modeling performance on the canonical Bars-and-Stripes dataset is used to design a benchmark for hybrid systems.
Journal ArticleDOI
Bayesian learning of parameterised quantum circuits
TL;DR: This work takes a probabilistic point of view and reformulate the classical optimisation as an approximation of a Bayesian posterior, which is induced by combining the cost function to be minimised with a prior distribution over the parameters of the quantum circuit.
Journal Article
Quantum-assisted learning of graphical models with arbitrary pairwise connectivity
TL;DR: This work successfully training hardware-embedded models with all-to-all connectivity on a real dataset of handwritten digits and two synthetic datasets and shows the generative capabilities of the models learned with the assistance of the quantum annealer in experiments with up to 940 quantum bits.
Posted Content
Filtering variational quantum algorithms for combinatorial optimization
David Amaro,Carlo Modica,Matthias Rosenkranz,Mattia Fiorentini,Marcello Benedetti,Michael Lubasch +5 more
TL;DR: In this paper, the Filtering Variational Quantum Eigensolver (F-VQE) is proposed to reduce the number of qubits required on a quantum computer.
Posted Content
Estimation of effective temperatures in quantum annealers for sampling applications: A case study towards deep learning
TL;DR: In this article, a simple effective-temperature estimation algorithm was proposed to overcome the limitations of using quantum annealers for Boltzmann sampling from quantum hardware. But, the algorithm is not suitable for deep learning applications, since the quantum dynamical arguments suggest that an instance-dependent effective temperature will be different from its physical temperature.