Autonomic computing investigates how systems can achieve (user) specified “control”
outcomes on their own, without the intervention of a human operator. Autonomic computing …

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 …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

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

It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …

Recently, several quantum machine learning algorithms have been proposed that may offer
quantum speed-ups over their classical counterparts. Most of these algorithms are either …

Integrated photonics will play a key role in quantum systems as they grow from few-qubit
prototypes to tens of thousands of qubits. The underlying optical quantum technologies can …

The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Integration of advanced nanogenerator technology with conventional textile processes
fosters the emergence of textile‐based nanogenerators (NGs), which will inevitably promote …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …