A review is given on the foundations and applications of non-Hermitian classical and
quantum physics. First, key theorems and central concepts in non-Hermitian linear algebra …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …

Generating high-quality data (eg, images or video) is one of the most exciting and
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …

Generative modeling using samples drawn from the probability distribution constitutes a
powerful approach for unsupervised machine learning. Quantum mechanical systems can …

Abstract We introduce $\texttt {Yao} $, an extensible, efficient open-source framework for
quantum algorithm design. $\texttt {Yao} $ features generic and differentiable programming …

The impressive pace of advance of quantum technology calls for robust and scalable
techniques for the characterization and validation of quantum hardware. Quantum process …

The task of learning a probability distribution from samples is ubiquitous across the natural
sciences. The output distributions of local quantum circuits are of central importance in both …

Quantum state preparation is a vital routine in many quantum algorithms, including solution
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …

While recent breakthroughs have proven the ability of noisy intermediate-scale quantum
(NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the …