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

Machine learning is a branch of artificial intelligence, and it has been widely used in many
science and engineering areas, such as data mining, natural language processing …

Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …

Deep learning achieves unprecedented success involves many fields, whereas the high
requirement of memory and time efficiency tolerance have been the intractable challenges …

Benchmarking models via classical simulations is one of the main ways to judge ideas in
quantum machine learning before noise-free hardware is available. However, the huge …

Near-term quantum computers are limited by the decoherence of qubits to only being able to
run low-depth quantum circuits with acceptable fidelity. This severely restricts what quantum …

An elegant quantum version of least-square support vector machine, which is exponentially
faster than the classical counterpart, was given by Rebentrost et al. using the matrix …

Quantum machine learning (QML) is an emerging research field, which is devoted to
devising and implementing quantum algorithms that could enable machine learning faster …

In this paper, a quantum extension of classical deep neural network (DNN) is introduced,
which is called QDNN and consists of quantum structured layers. It is proved that the QDNN …

Quantum computing enables quantum neural networks (QNNs) to have great potential to
surpass artificial neural networks. The powerful generalization of neural networks is …