[HTML][HTML] Systematic literature review: Quantum machine learning and its applications

D Peral-García, J Cruz-Benito… - Computer Science …, 2024 - Elsevier
Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

Quantum computing models for artificial neural networks

S Mangini, F Tacchino, D Gerace, D Bajoni… - Europhysics …, 2021 - iopscience.iop.org
Neural networks are computing models that have been leading progress in Machine
Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale …

A general approach to dropout in quantum neural networks

F Scala, A Ceschini, M Panella… - Advanced Quantum …, 2023 - Wiley Online Library
In classical machine learning (ML),“overfitting” is the phenomenon occurring when a given
model learns the training data excessively well, and it thus performs poorly on unseen data …

Engineered dissipation to mitigate barren plateaus

A Sannia, F Tacchino, I Tavernelli, GL Giorgi… - npj Quantum …, 2024 - nature.com
Variational quantum algorithms represent a powerful approach for solving optimization
problems on noisy quantum computers, with a broad spectrum of potential applications …

Entanglement entropy production in quantum neural networks

M Ballarin, S Mangini, S Montangero… - Quantum, 2023 - quantum-journal.org
Abstract Quantum Neural Networks (QNN) are considered a candidate for achieving
quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era. Several …

Quantum embedding search for quantum machine learning

N Nguyen, KC Chen - IEEE Access, 2022 - ieeexplore.ieee.org
This paper introduces an automated search algorithm (QES, pronounced as “quest”), which
derives optimal design of entangling layout for supervised quantum machine learning. First …