Quantum Approximate Optimization Algorithm for the Max-Cut Problem : JavaScript Programming Language Implementation

Abstract

In this paper, we present the implementation of the Quantum Approximate Optimization Algorithm (QAOA) for the Max-Cut problem using the JavaScript programming language. The Max-Cut issue, which involves partitioning the vertices of a graph into two subsets such that the number of edges between the subsets is maximized, is a well-known NP-hard difficulty with numerous practical applications, including network design and resource allocation. The implementation of QAOA in JavaScript is a significant step towards integrating quantum computing with modern web technologies, thus broadening access to quantum algorithms among software developers. Quantum algorithm implementation leverages the principles of quantum mechanics, such as superposition and entanglement, to approximate solutions to combinatorial optimization issues. The quantum.js framework, developed in the context of this research, facilitates the construction and manipulation of quantum circuits in a web environment. The framework includes functions for building quantum circuits, optimizing the parameters of the QAOA algorithm, and visualizing the resulting quantum states. By enabling the execution of quantum algorithms in a web-based setting, this work demonstrates the potential for utilizing quantum computing capabilities within popular web development environments. The results highlight the efficiency of QAOA in providing approximate solutions to the Max-Cut, offering a promising alternative to classical optimization methods. Future work will focus on enhancing the framework by adding cloud-based quantum computing capabilities, expanding the documentation, incorporating additional quantum-hybrid algorithms, and improving the user interface of the associated web application.