394 citations · 477 across the 13 of their papers we have counts for
3 papers · 2 filters
A General Approach to Dropout in Quantum Neural Networks
Francesco Scala, Andrea Ceschini, Massimo Panella +1
In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A…
A Review on Quantum Approximate Optimization Algorithm and its Variants
Kostas Blekos, Dean Brand, Andrea Ceschini +4
The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classicall…
Resource Saving via Ensemble Techniques for Quantum Neural Networks
Massimiliano Incudini, Michele Grossi, Andrea Ceschini +4
Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limi…