1 citations · 1 across the 2 of their papers we have counts for
3 papers
Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph
Salvatore Sinno, Thomas Groß, Nicholas Chancellor +2
Minor embedding is essential for mapping largescale combinatorial problems onto quantum annealers, particularly in quantum machine learning and optimization. This work presents an…
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing
Salvatore Sinno, Markus Bertl, Arati Sahoo +3
This study explores the implementation of large Quantum Restricted Boltzmann Machines (QRBMs), a key advancement in Quantum Machine Learning (QML), as generative models on D-Wave's…
Performance of Commercial Quantum Annealing Solvers for the Capacitated Vehicle Routing Problem
Salvatore Sinno, Thomas Groß, Alan Mott +4
Quantum annealing (QA) is a heuristic search algorithm that can run on Adiabatic Quantum Computation (AQC) processors to solve combinatorial optimization problems. Although theoret…