Quantum Annealing for Sustainable Supply Chains to Reduce Carbon Footprint

Abstract

This investigates Quantum Annealing as a game-changing strategy for dealing with complicated combinatorial optimization issues, with the goal of achieving sustainable supply chains and decreased carbon footprints. The purpose of the research is to harness the potential of Quantum Computing, notably focused on Quadratic Unconstrained Binary Optimization formulations, Ising Model, Maximum Cut Problem, and the Traveling Salesman Problem. The goal of this is to expand the use of Quantum Annealing methods in the logistics, supply chain, and environmental fields. The goal of this study is to use Quantum Annealing's powerful capability to effectively explore large solution spaces as a springboard for developing novel approaches. A quantum-powered paradigm change in supply chain optimization is made possible by the new application of Quantum Annealing to real-world sustainability concerns. This is groundbreaking since it is the first to include quantum algorithms into eco-friendly supply chain planning by going into the details of QUBO, the Ising Model, MAX-CUT, and TSP. It offers a fresh viewpoint on reducing the carbon footprint of supply chains, which in turn encourages more environmentally friendly corporate procedures. It lays the groundwork for future developments in quantum-enabled sustainability and highlights the critical role that cutting-edge technologies play in driving global supply chain environmental conservation initiatives.