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 …

Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …

The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-
level benchmark suite and insightful evaluation metrics for characterizing the properties of …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

This paper presents a quantum convolutional neural network (QCNN) for the classification of
high energy physics events. The proposed model is tested using a simulated dataset from …

In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …

Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …

Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …

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 …

Quantum machine learning (QML) has received increasing attention due to its potential to
outperform classical machine learning methods in problems, such as classification and …