Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces
make it possible to explore and investigate the design and development of quantum …

Hyperparameter optimization (HPO) and neural architecture search (NAS) of machine
learning (ML) models are in the core implementation steps of AI-enabled systems. With multi …

Abstract Job Shop Scheduling is a combinatorial optimization problem of particular
importance for production environments where the goal is to complete a production task in …

The differences in performance among binary-integer encodings in an Ising machine, which
can solve combinatorial optimization problems, are investigated. Many combinatorial …

Quantum computing (QC) is anticipated to provide a speedup over classical approaches for
specific problems in optimization, simulation, and machine learning. With the advances in …

Optimisation algorithms designed to work on quantum computers or other specialised
hardware have been of research interest in recent years. Commercial solvers that use …

Classical computing has borne witness to the development of machine learning. The
integration of quantum technology into this mix will lead to unimaginable benefits and be …

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many
applications. Various digital annealers, dynamical Ising machines, and quantum/photonic …

In the field of spintronics, there is a strong demand for barrier materials in magnetic tunnel
junctions (MTJs) having high tunnel magnetoresistance (TMR) and low resistance area …

In the last decade, public and industrial research funding has moved quantum computing
from the early promises of Shor's algorithm through experiments to the era of noisy …