Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

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 …

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

Complex-valued neural networks have many advantages over their real-valued
counterparts. Conventional digital electronic computing platforms are incapable of executing …

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

Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Abstract Machine learning and quantum computing are two technologies that each have the
potential to alter how computation is performed to address previously untenable problems …

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized
quantum circuit V (θ) to minimize a cost function C. While VQAs may enable practical …

Quantum computers can exploit a Hilbert space whose dimension increases exponentially
with the number of qubits. In experiment, quantum supremacy has recently been achieved …

A basic idea of quantum computing is surprisingly similar to that of kernel methods in
machine learning, namely, to efficiently perform computations in an intractably large Hilbert …