Quantum Computing in Optimization: A New Paradigm for Logistics and Supply Chains

Abstract

The explosive growth of the globalization of the logistics networking and the supply chains has underscored that more mature optimization must be in use to deal with the scale and complexity (Kumar & Singh, 2021). The existing computing paradigms are only capable of earning their abilities to carry out NP-Hard optimization effectively to create bottlenecks in efficiency and scalability (Bertsimas & Dunn, 2017). Quantum computing offers a possible advantage over the usual combinatorial optimization algorithms the potential of both quantum parallelism and quantum entanglement to operate on a quicker timescale than classical algorithms in solving combinatorial optimization tasks (Farhi et al., 2014). The research question of interest here is what quantum computing, namely quantum annealing and variational quantum algorithm are capable of doing to steer the next revolution in the optimization of logistical processes such as vehicle routing, the work of the warehouses, and not least the equilibrium of supply and demand. In the continuation of previous works exploring the possibilities of quantum machine learning to the industrial context (Orus et al., 2019), the study implements quantum-inspired models to explore their competence level compared to the newer AI models, including deep reinforcement learning and metaheuristics (Kingma and Ba, 2014). The results indicate that the quantum-based approaches might demonstrate a promising performance of real-life logistics problems since they have the potential of obtaining the convergence speedup and computational time improvement on specific problem instances (Ajagekar et al., 2020). The implications of these observations are significant as far as the future of sustainable and resilient supply chains is concerned owing to the fact that this observation reflects the transformational abilities of the hybrid quantum-classical systems (Preskill, 2018). 

Keywords: quantum computing, supply chains optimization, logistics optimization, quantum annealing, variational methods, machine learning, optimizations combinatorielle