10 citations · 25 across the 5 of their papers we have counts for
5 papers
High-Parallel FPGA-Based Discrete Simulated Bifurcation for Large-Scale Optimization
Fabrizio Orlando, Deborah Volpe, Giacomo Orlandi +3
Combinatorial Optimization (CO) problems exhibit exponential complexity, making their resolution challenging. Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm…
Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy
Antonio Tudisco, Andrea Marchesin, Maurizio Zamboni +2
Recent advancements in Quantum Computing and Machine Learning have increased attention to Quantum Machine Learning (QML), which aims to develop machine learning models by exploitin…
AMARETTO: Enabling Efficient Quantum Algorithm Emulation on Low-Tier FPGAs
Christian Conti, Deborah Volpe, Mariagrazia Graziano +2
Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitat…
A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem
Deborah Volpe, Nils Quetschlich, Mariagrazia Graziano +2
Leveraging quantum computers for optimization problems holds promise across various application domains. Nevertheless, utilizing respective quantum computing solvers requires descr…
Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers
Deborah Volpe, Nils Quetschlich, Mariagrazia Graziano +2
Optimizing objective functions stands to benefit significantly from leveraging quantum computers, promising enhanced solution quality across various application domains in the futu…