4 citations · 6 across the 5 of their papers we have counts for
6 papers
A Hybrid Classical-Quantum Annealing Algorithm for the TSP
Siwei Hu, Victor Lopata, Salvatore Sinno +2
Hybrid quantum-classical algorithms can help mitigating the physical limitations of current quantum devices, particularly the low qubit count and the reduced topological connectivi…
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…
Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?
Markus Bertl, Alan Mott, Salvatore Sinno +1
The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Tr…
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…
Quantum Annealing based Hybrid Strategies for Real Time Route Optimization
Sushil Mario, Pavan Teja Pothamsetti, Louie Antony Thalakottor +6
One of the most well-known problems in transportation and logistics is the Capacitated Vehicle Routing Problem (CVRP). It involves optimizing a set of truck routes to service a set…
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…