Quantum Neural Networks for a Supply Chain Logistics Application

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. One such hybrid algorithm on a problem of substantial importance: vehicle routing for supply chain logistics with multiple trucks and complex demand structure is investigated. Reinforcement learning with neural networks with embedded quantum circuits is used. 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, a multi-head attention mechanism is used where, even in classical machine learning, such projections are natural and desirable. Data from the truck routing logistics of a company in the automotive sector is considered, and the methodology is applied by decomposing into small teams of trucks and results are found comparable to human truck assignment.

Advanced Cardiovascular Health in a Quantum AI-driven Healthcare Framework

“…Quantum Key Distribution (QKD) can provide substantially improved Encryption and Security capabilities to create secure communication channels thereby protecting patient information during transit and in storage [34], [35], [36]. Lastly, QC can also prove critical in optimizing the logistics of healthcare [37], [38] by improving resource allocation, scheduling, and supply chain management etc., leading to efficiency and cost-effectiveness of healthcare delivery.…”