Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

The power of quantum computing technologies is based on the fundamentals of quantum
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Quantum technologies have become powerful tools for a wide range of application
disciplines, which tend to range from chemistry to agriculture, natural language processing …

Simple Summary This work successfully employs a novel approach in processing patient
and drug data to predict the drug response for cancer patients. The approach uses a deep …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

Kernel-based support vector machines (SVMs) are supervised machine learning algorithms
for classification and regression problems. We introduce a method to train SVMs on a D …

Feature selection is a common step in many ranking, classification, or prediction tasks and
serves many purposes. By removing redundant or noisy features, the accuracy of ranking or …

Here we discuss advances in the field of quantum machine learning. The following
document offers a hybrid discussion; both reviewing the field as it is currently, and …

Fitting geometric models onto outlier contaminated data is provably intractable. Many
computer vision systems rely on random sampling heuristics to solve robust fitting, which do …