Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subtyping …

Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC)
with a gradient-based classical optimizer, has been broadly applied to many experimental …

Existing drug discovery pipelines take 5–10 years and cost billions of dollars. Computational
approaches aim to sample from regions of the whole molecular and solid-state compounds …

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML)
methodologies, with a unique perspective on their applications for wireless communications …

This paper introduces a new evolutionary algorithm with the support of an actual quantum
processor, a computing device which uses phenomena from quantum mechanics to enable …

Quantum machine learning (QML) is a discipline that holds the promise of revolutionizing
data processing and problem‐solving. However, dissipation and noise arising from the …

Classical self-supervised networks suffer from convergence problems and reduced
segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often …

Learn the principles of quantum machine learning and how to apply them While focus is on
financial use cases, all the methods and techniques are transferable to other fields Purchase …

Quantum computing is no longer a promise of the future but a rapidly evolving reality.
Advances in quantum hardware are making it possible to make tangible a computational …

As one of the most meaningful research topics in computer vision, crowd counting and
localization problems have been applied in many applications such as Video surveillance …