A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

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

Recurrent neural networks are the foundation of many sequence-to-sequence models in
machine learning, such as machine translation and speech synthesis. With applied quantum …

The Quantum Internet, by enabling quantum communications among remote quantum
nodes, is a network capable of supporting functionalities with no direct counterpart in the …

Several architectures have been proposed for quantum neural networks (QNNs), with the
goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …