In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

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

Several architectures have been proposed for quantum neural networks (QNNs), with the
goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling …

Quantum computing is envisaged as an evolving paradigm for solving computationally
complex optimization problems with a large-number factorization and exhaustive search …

Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to
their ability to improve performance. However, verifying the success of open QW is …

Abstract Machine learning (ML) methods have recently been increasingly widely used in
quantum chemistry. While ML methods are now accepted as high accuracy approaches to …

The spin-1/2 Heisenberg model on the pyrochlore lattice is an iconic frustrated three-
dimensional spin system with a rich phase diagram. Besides hosting several ordered …

Matrix quantum mechanics plays various important roles in theoretical physics, such as a
holographic description of quantum black holes, and it underpins the only practical …

Abstract The Internet of Things (IoT) is a global network of millions of devices connected in
wireless that exchange data. Multiple data are aiming to be observed through a single …