Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Quantum reservoir networks combine the intelligence of neural networks with the potential of
quantum computing in a single platform. This platform operates on the architecture of …

Quantum machine learning aims to execute machine learning algorithms in quantum
computers by utilizing powerful laws like superposition and entanglement for solving …

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of
machine learning approaches in adversarial settings and develo** techniques …

Quantum entanglement is one of the essential resources involved in quantum information
processing tasks. However, its detection for usage remains a challenge. The Bell-type …

We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …

Data compression can be achieved by reducing the dimensionality of high-dimensional but
approximately low-rank datasets, which may in fact be described by the variation of a much …

Quantum properties, such as entanglement and coherence, are indispensable resources in
various quantum information processing tasks. However, there still lacks an efficient and …

The ability to witness nonlocal correlations lies at the core of foundational aspects of
quantum mechanics and its application in the processing of information. Commonly, this is …

In this work, we develop a supervised learning model for implementing robust quantum
control in composite-pulse systems, where the training parameters can be either phases …