Quantum Neural Networks for a Supply Chain Logistics Application

TL;DR

This paper explores a hybrid classical-quantum reinforcement learning approach for vehicle routing in supply chain logistics, demonstrating results comparable to human truck assignment using quantum neural networks with attention mechanisms.

Contribution

It introduces a novel hybrid quantum-classical neural network model with attention mechanisms for vehicle routing, addressing hardware limitations of NISQ devices.

Findings

Results are comparable to human truck assignment.

The approach effectively handles complex demand structures.

Quantum neural networks can be integrated into logistics optimization.

Abstract

Problem instances of a size suitable for practical applications are not likely to be addressed during the noisy intermediate-scale quantum (NISQ) period with (almost) pure quantum algorithms. Hybrid classical-quantum algorithms have potential, however, to achieve good performance on much larger problem instances. We investigate one such hybrid algorithm on a problem of substantial importance: vehicle routing for supply chain logistics with multiple trucks and complex demand structure. We use reinforcement learning with neural networks with embedded quantum circuits. In such neural networks, projecting high-dimensional feature vectors down to smaller vectors is necessary to accommodate restrictions on the number of qubits of NISQ hardware. However, we use a multi-head attention mechanism where, even in classical machine learning, such projections are natural and desirable. We consider data from the truck routing logistics of a company in the automotive sector, and apply our methodology by decomposing into small teams of trucks, and we find results comparable to human truck assignment.