Adiabatic Quantum Computing for Logistic Transport Optimization

TL;DR

This paper explores the application of adiabatic quantum computing, specifically quantum annealing, to solve complex vehicle routing problems in logistics, proposing hybrid classical-quantum solutions for near-term implementation.

Contribution

It introduces a hybrid quantum-classical approach to vehicle routing problems, demonstrating potential advantages of adiabatic quantum computing in logistics optimization.

Findings

Quantum annealing shows promise for vehicle routing problems.

Hybrid solutions outperform classical methods in certain scenarios.

Analysis suggests near-term applicability in logistics cases.

Abstract

Current world trade is based and supported in a strong and healthy supply chain, where logistics play a key role in producing and providing key assets and goods to keep societies and economies going. Current geopolitical and sanitary challenges faced in the entire world have made even more critical the role of logistics and increased demands for tuning transport function to keep the supply chain up and running. The challenge is only increasing and growing for the future, thus tackling transport optimization provides both business and social value. Optimization problems are ubiquitous and they present a challenge due to its complexity, where they're typically NP-hard problems. Quantum Computing is a developing field, and the Quantum Annealing approach has proven to be quite effective in its applicability and usefulness to tackle optimization problems. In this work we treat the Vehicle Routing Problem, which is also a variation of a famous optimization problem known as the Traveling Salesman Problem. We aim to tackle the vehicle optimization problem from the last mile logistic scenario application, with a perspective from the classical and quantum approaches, and providing a solution which combines both, also known as hybrid solution. Finally, we provide the results of the analysis and proposal for the consideration of applications in a near term business case scenario.