Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-
related activities and experiments in the last few years (although biology and quantum …

The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …

Machine learning models capable of handling the large data sets collected in the financial
world can often become black boxes expensive to run. The quantum computing paradigm …

Quantum machine learning is a rapidly growing field at the intersection of quantum
computing and machine learning. In this work, we examine our quantum machine learning …

In this work we present a complete hybrid classical-quantum algorithm involving a quantum
sampler based on neutral-atom platforms. This approach is inspired by classical column …

Several benchmarks have been proposed to holistically measure quantum computing
performance. While some have focused on the end user's perspective (eg, in application …

Quantum kernel methods exploit quantum computers to calculate quantum kernels (QKs) for
the use of kernel-based learning models. Despite a potential quantum advantage of the …

Transformers are increasingly employed for graph data, demonstrating competitive
performance in diverse tasks. To incorporate graph information into these models, it is …

We present a comprehensive investigation into the learning capabilities of a simple d-level
system (qudit). Our study is specialized for classification tasks using real-world databases …

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in
potentially life-saving applications. Deep learning provides a promising solution to finding …