Search Results for author: Michele Amoretti

Found 5 papers, 0 papers with code

Multi-Class Quantum Convolutional Neural Networks

no code implementations19 Apr 2024 Marco Mordacci, Davide Ferrari, Michele Amoretti

The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network.

Information Retrieval Multi-class Classification +1

Deterministic Algorithms for Compiling Quantum Circuits with Recurrent Patterns

no code implementations17 Feb 2021 Davide Ferrari, Ivano Tavernelli, Michele Amoretti

In particular, such patterns appear in quantum circuits that are used to compute the ground state properties of molecular systems using the variational quantum eigensolver (VQE) method together with the RyRz heuristic wavefunction Ansatz.

Quantum Physics Computational Complexity Data Structures and Algorithms

Compiler Design for Distributed Quantum Computing

no code implementations17 Dec 2020 Davide Ferrari, Angela Sara Cacciapuoti, Michele Amoretti, Marcello Caleffi

In distributed quantum computing architectures, with the network and communications functionalities provided by the Quantum Internet, remote quantum processing units (QPUs) can communicate and cooperate for executing computational tasks that single NISQ devices cannot handle by themselves.

Quantum Physics Distributed, Parallel, and Cluster Computing Networking and Internet Architecture

Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices

no code implementations8 Jan 2018 Davide Ferrari, Michele Amoretti

Quantum compiling means fast, device-aware implementation of quantum algorithms (i. e., quantum circuits, in the quantum circuit model of computation).

BIG-bench Machine Learning Quantum Machine Learning

Measuring the Complexity of Ultra-Large-Scale Adaptive Systems

no code implementations27 Jul 2012 Michele Amoretti, Carlos Gershenson

Here we propose the design and analysis of ULS systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory.

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