At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

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 …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …

Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …

Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

TensorCircuit is an open source quantum circuit simulator based on tensor network
contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and …

A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …