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arXiv:2212.00576 · Advanced Quantum Technologies

Quantum Neural Networks for a Supply Chain Logistics Application

Randall Correll, Sean J. Weinberg, Fabio Sanches, Takanori Ide, Takafumi J. Suzuki

Published May 18, 2023• 20 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1002/qute.202200183

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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.
Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance which can have many variables and unwieldy objective functions. As a consequence, there is a growing body of literature that tests quantum algorithms on miniaturized versions of problems that arise in an operations research setting. Rather than taking this approach, we investigate a problem of substantial commercial value, multi-truck vehicle routing for supply chain logistics, at the scale used by a corporation in their operations. Such a problem is too complex to be fully embedded on any near-term quantum hardware or simulator; we avoid confronting this challenge by taking a hybrid workflow approach: we iteratively assign routes for trucks by generating a new binary optimization problem instance one truck at a time. Each instance has [Formula: see text] quadratic binary variables, putting it in a range that is feasible for NISQ quantum computing, especially quantum annealing hardware. We test our methods using simulated annealing and the D-Wave Hybrid solver as a place-holder in wait of quantum hardware developments. After feeding the vehicle routes suggested by these runs into a highly realistic classical supply chain simulation, we find excellent performance for the full supply chain. Our work gives a set of techniques that can be adopted in contexts beyond vehicle routing to apply NISQ devices in a hybrid fashion to large-scale problems of commercial interest.
Quantum computing and machine learning have potential for symbiosis. However, in addition to the hardware limitations from current devices, there are still basic issues that must be addressed before quantum circuits can usefully incorporate with current machine learning tasks. We report a strategy for such an integration in the context of attention models used for reinforcement learning. Agents that implement attention mechanisms have successfully been applied to certain cases of combinatorial routing problems by first encoding nodes on a graph and then sequentially decoding nodes until a route is selected. We demonstrate that simple quantum circuits can be used in place of classical attention head layers while maintaining performance. Our method modifies attention mechanisms by replacing key and query vectors for every node with quantum states that are entangled before being measured. The resulting hybrid classical-quantum agent is tested in the context of vehicle routing problems where its performance is competitive with the original classical approach. We regard our model as a prototype that can be scaled up and as an avenue for further study on the role of quantum computing in reinforcement learning.
Abstract The vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, the use of a previously‐introduced qubit encoding scheme is explored to reduce the number of qubits, to evaluate the effectiveness of Noisy Intermediate‐Scale Quantum (NISQ) devices when applied to industry relevant optimization problems. A quantum variational approach is applied to a testbed of multiple VRPTW instances ranging from 11 to 3964 routes. These intances are formulated as quadratic unconstrained binary optimization (QUBO) problems based on realistic shipping scenarios. The results are compared with standard binary‐to‐qubit mappings after executing on simulators as well as various quantum hardware platforms, including IBMQ, AWS (Rigetti), and IonQ. These results are benchmarked against the classical solver, Gurobi. The approach can find approximate solutions to the VRPTW comparable to those obtained from quantum algorithms using the full encoding, despite the reduction in qubits required. These results suggest that using the encoding scheme to fit larger problem sizes into fewer qubits is a promising step in using NISQ devices to find approximate solutions for industry‐based optimization problems, although additional resources are still required to eke out the performance from larger problem sizes.
This work studies hybrid quantum-classical neural networks for learning routing heuristics. Specifically, this paper asks whether small quantum neural networks can replace parameter-heavy modules inside a competitive attention-based routing model while maintaining solution quality. For the capacitated vehicle routing problem, encoder feed-forward replacement emerges as the most promising design: it reduces the number of model parameters by 56.6% while keeping the hybrid model close to the classical neural baseline at small and medium instance sizes, although the gap grows for larger instances. This work also compares to classical routing algorithms, which remain highly competitive and often superior on the fixed Euclidean test sets. Our results therefore do not indicate quantum advantage or solver dominance, but identify encoder feed-forward replacement as a viable hybrid-module compression strategy for neural combinatorial optimization.
Supply chain and logistics operations involve combinatorial optimisation problems -- vehicle routing, warehouse assignment, inventory scheduling, and network flow -- whose complexity scales exponentially with problem size, making exact classical solvers impractical for real-world instances. Quantum optimisation algorithms offer potential speedups through quantum parallelism and interference. This paper proposes the Quantum Supply Chain Optimisation Framework (QSCOF), evaluating QAOA, VQE, and quantum annealing across five canonical logistics problems on NISQ hardware and fault-tolerant models. Evaluated on 20 benchmark instances spanning 20-200 nodes, QSCOF demonstrates QAOA achieving 93.8% solution quality for 50-node vehicle routing on current hardware, quantum annealing delivering 28x speedup for 100-node warehouse assignment, and a Quantum Logistics Utility Index (QLUI) of 0.871. Quantum advantage thresholds and a phased adoption roadmap are provided.
The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown promise, yet classical models rely on large multi-layer perceptrons (MLPs) that are parameter-heavy and memory-bound. We propose a Quantum Graph Attention Network (Q-GAT) within a DRL framework, where parameterized quantum circuits (PQCs) replace conventional MLPs at critical readout stages. The hybrid model maintains the expressive capacity of graph attention encoders while reducing trainable parameters by more than 50%. Using proximal policy optimization (PPO) with greedy and stochastic decoding, experiments on VRP benchmarks show that Q-GAT achieves faster convergence and reduces routing cost by about 5% compared with classical GAT baselines. These results demonstrate the potential of PQC-enhanced GNNs as compact and effective solvers for large-scale routing and logistics optimization.