Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

The last two decades have seen an explosive growth in the theory and practice of both
quantum computing and machine learning. Modern machine learning systems process huge …

Quantum machine learning is often highlighted as one of the most promising practical
applications for which quantum computers could provide a computational advantage …

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 …

Quantum computing has been proven to excel in factorization issues and unordered search
problems due to its capability of quantum parallelism. This unique feature allows …

With quantum computing technologies nearing the era of commercialization and quantum
supremacy, machine learning (ML) appears as one of the promising'killer'applications …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …