5 citations · 14 across the 5 of their papers we have counts for
5 papers
Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Chiara Vercellino, Paolo Viviani, Giacomo Vitali +5
Graph embedding is a recurrent problem in quantum computing, for instance, quantum annealers need to solve a minor graph embedding in order to map a given Quadratic Unconstrained B…
Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
Chiara Vercellino, Giacomo Vitali, Paolo Viviani +5
Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the gap between real-world applica…
BBQ-mIS: a parallel quantum algorithm for graph coloring problems
Chiara Vercellino, Giacomo Vitali, Paolo Viviani +5
Among the limitations of current quantum machines, the qubits count represents one of the most critical challenges for porting reasonably large computational problems, such as thos…
Deep Learning for real-time neural decoding of grasp
Paolo Viviani, Ilaria Gesmundo, Elios Ghinato +9
Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this con…
Pushing the boundaries of parallel Deep Learning -- A practical approach
Paolo Viviani, Maurizio Drocco, Marco Aldinucci
This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvemen…