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 …

Highlights•Drug discovery is time consuming, expensive and experiences increasing
challenges.•Generation of new drug candidates is one of the major challenges.•Quantum …

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …

Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields
such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges …

Demonstrating the practical advantage of quantum computation remains a long-standing
challenge whereas quantum machine learning becomes a promising application that can be …

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

In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and
excretion (ADME) and toxicity properties of new chemical entities is one of the most critical …

Quantum machine learning promises to revolutionize traditional machine learning by
efficiently addressing hard tasks for classical computation. While claims of quantum speed …

In order to characterize and benchmark computational hardware, software, and algorithms, it
is essential to have many problem instances on-hand. This is no less true for quantum …