We introduce a quantum-inspired algorithm for graph coloring problems (GCPs) that utilizes
qudits in a product state, with each qudit representing a node in the graph and …

Binary neural networks, ie, neural networks whose parameters and activations are
constrained to only two possible values, offer a compelling avenue for the deployment of …

Combinatorial optimization problems are widespread but inherently challenging due to their
discrete nature. The primary limitation of existing methods is that they can only access a …

Predicting software defects early in the development process not only enhances the quality
and reliability of the software but also decreases the cost of development. A wide range of …

Local quantum annealing (LQA), an iterative algorithm, is designed to solve combinatorial
optimization problems. It draws inspiration from QA, which utilizes adiabatic time evolution to …

In practical applications, achieving perfect transmission of electromagnetic waves despite
reflections from a complex medium segment in waveguide systems is of great significance …

From fundamental sciences to economics and industry, discrete optimization problems are
ubiquitous. Yet, their complexity often renders exact solutions intractable, necessitating the …

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to
obtain approximations of the ground states of Ising models. The quadratic unconstrained …

In this work, we present the design and plan of Quantum machine learning (QML) course in
a computer science (CS) University program at senior undergraduate level/first year …

How to quickly solve the problem of high-order unconstrained binary optimization (HUBO)
has attracted much attention, because of its importance and wide-range applications. Here …