Quantum Neural Networks for a Supply Chain Logistics Application

• URL: http://arxiv.org/abs/2212.00576v2
• Date: Fri, 2 Dec 2022 16:11:45 GMT
• Title: Quantum Neural Networks for a Supply Chain Logistics Application
• Authors: Randall Correll (1), Sean J. Weinberg (1), Fabio Sanches (1), Takanori Ide (2) and Takafumi Suzuki (3) ((1) QC Ware Corp Palo Alto, (2) AISIN CORPORATION Tokyo, (3) Aisin Technical Research Center, Tokyo)
• Abstract summary: 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. We find results comparable to human truck assignment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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