Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …

Quantum generative modeling is a growing area of interest for industry-relevant
applications. This work systematically compares a broad range of techniques to guide …

We develop computationally affordable and encoding independent gradient evaluation
procedures for unitary coupled-cluster type operators, applicable on quantum computers …

Finding a concrete use case for quantum computers in the near term is still an open
question, with machine learning typically touted as one of the first fields which will be …

Quantifying how far the output of a learning algorithm is from its target is an essential task in
machine learning. However, in quantum settings, the loss landscapes of commonly used …

Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization
algorithms. This study illustrates their use for the optimization of randomly initialized …

In this paper, we propose the quantum semi-supervised generative adversarial network
(qSGAN). The system is composed of a quantum generator and a classical …

Quantum generative adversarial networks (QGANs), an intersection of quantum computing
and machine learning, have attracted widespread attention due to their potential advantages …

Generative adversarial networks are an emerging technique with wide applications in
machine learning, which have achieved dramatic success in a number of challenging tasks …