Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …

We initiate the study of utilizing Quantum Langevin Dynamics (QLD) to solve optimization
problems, particularly those non-convex objective functions that present substantial …

Drug discovery and development is a highly complex and costly endeavor, typically
requiring over a decade and substantial financial investment to bring a new drug to market …

This study reevaluates criticism of the Korean government's R&D investment strategy, which
was considered an'R&D cartel'and was cited as a reason for the Korean government's R&D …

Logistic regression, the Support Vector Machine (SVM), and least squares are well-studied
methods in the statistical and computer science community, with various practical …

Abstract The Eisert–Wilkens–Lewenstein (EWL) game can be used to solve the quantum
prisoner's dilemma is investigated. It is assumed that the states of the players are polarized …

Quantum $ k $-minimum finding is a fundamental subroutine with numerous applications in
combinatorial problems and machine learning. Previous approaches typically assume …

In the realm of algorithmic economics, voting systems are evaluated and compared by
examining the properties or axioms they satisfy. While this pursuit has yielded valuable …

As a basic optimization technique, linear programming has found wide applications in many
areas. In this paper, we propose an improved quantum algorithm for solving a linear …