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 have made extraordinary progress over the past decade, and
significant milestones have been achieved along the path of pursuing universal fault-tolerant …

Quantum annealing is a computing paradigm that has the ambitious goal of efficiently
solving large-scale combinatorial optimization problems of practical importance. However …

The Fujitsu Digital Annealer is designed to solve fully connected quadratic unconstrained
binary optimization (QUBO) problems. It is implemented on application-specific CMOS …

Quantum computing has rapidly advanced in recent years due to substantial development in
both hardware and algorithms. These advances are carrying quantum computers closer to …

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

Quantum computers can in principle solve certain problems exponentially more quickly than
their classical counterparts. We have not yet reached the advent of useful 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 …

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 …

Quantum circuit Born machines are generative models which represent the probability
distribution of classical dataset as quantum pure states. Computational complexity …