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

Quantum machine learning, which involves running machine learning algorithms on
quantum devices, has garnered significant attention in both academic and business circles …

Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic
fluctuations. We connect these physics-inspired AI algorithms by unifying them under a …

This review investigates the landscapes of hybrid quantum–classical optimization algorithms
that are prevalent in many rapidly developing quantum technologies, where the objective …

Quantum computing is an emerging technology with potentially far-reaching implications for
national prosperity and security. Understanding the timeframes over which economic …

Achieving quantum speedups in practical tasks remains challenging for current noisy
intermediate-scale quantum (NISQ) devices. These devices always encounter significant …

Quantum batteries are predicted to have the potential to outperform their classical
counterparts and are therefore an important element in the development of quantum …

Quantum machine learning (QML) is an emerging research area that combines quantum
computation with classical machine learning (ML). The primary objective of QML is to …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

As quantum computing hardware steadily increases in qubit count and quality, one
important question is how to allocate these resources to mitigate the effects of hardware …