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

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 …

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

With the rapid advance of quantum machine learning, several proposals for the quantum-
analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark …

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

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

Quantum machine learning is expected to be one of the first practical applications of near-
term quantum devices. Pioneer theoretical works suggest that quantum generative …

Abstract Machine learning has become a ubiquitous and effective technique for data
processing and classification. Furthermore, due to the superiority and progress of quantum …

Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …