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 expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation 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 …

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 …

Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade, and achieve disruptive impact on numerous industry sectors …

Quantum reservoir computing (QRC) is a highly promising computational paradigm that
leverages quantum systems as a computational resource for nonlinear information …

Quantum machine learning (QML) can complement the growing trend of using learned
models for a myriad of classification tasks, from image recognition to natural speech …

Quantum neural network (QNN) is one of the promising directions where the near-term noisy
intermediate-scale quantum (NISQ) devices could find advantageous applications against …