Coherent Optical Quantum Computing-Aided Resource Optimization for Transportation Digital Twin Construction

2025-11-12

AI Summary

This work addresses the cross-regional data resource allocation problem in traffic digital twin construction under data sovereignty regulations. Method: We propose a two-stage stochastic integer programming model centered on metaverse service providers, integrating deterministic operational costs with probabilistic regulatory compliance penalties to enable efficient and compliant data subscription and distribution. We further pioneer the reformulation of this model into Quadratic Unconstrained Binary Optimization (QUBO) form and implement millisecond-scale solving on a 550-qubit coherent Ising machine; concurrently, we design a lightweight data compliance acquisition framework. Contribution/Results: Experiments demonstrate significant speedup over quantum-inspired algorithms (e.g., PyQUBO), achieving several orders-of-magnitude faster solving than Gurobi—albeit with marginally lower solution quality—thereby providing the first empirical validation of feasible quantum-accelerated optimization for medium-scale real-world traffic problems on photonic quantum hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Constructing realistic digital twins for applications such as training autonomous driving models requires the efficient allocation of real-world data, yet data sovereignty regulations present a major challenge. To address this, we tackle the optimization problem faced by metaverse service providers (MSPs) responsible for allocating geographically constrained data resources. We propose a two-stage stochastic integer programming (SIP) model that incorporates reservation and on-demand planning, enabling MSPs to efficiently subscribe and allocate data from specific regions to clients for training their models on local road conditions. The SIP model is transformed into a quadratic unconstrained binary optimization (QUBO) formulation and implemented for the first time at a practical scale on a 550-qubit coherent Ising machine (CIM), representing an exploratory step toward future quantum computing paradigms. Our approach introduces an MSP-centric framework for compliant data collection under sovereignty constraints, a hybrid cost model combining deterministic fees with probabilistic penalties, and a practical implementation on quantum hardware. Experimental results demonstrate that CIM-based optimization finds high-quality solutions with millisecond-scale ($10^3$ second) computation times, significantly outperforming quantum-inspired solvers like PyQUBO. Although classical solvers such as Gurobi can achieve marginally better solution quality, CIM is orders of magnitude faster, establishing a practical paradigm for quantum-enhanced resource management.

Problem

• Optimizing geographically constrained data allocation under sovereignty regulations
• Developing quantum-enhanced resource management for digital twin construction
• Solving two-stage stochastic optimization using coherent Ising machines

Innovation

• Two-stage stochastic integer programming for data allocation
• Quadratic unconstrained binary optimization on quantum hardware
• Coherent Ising machine enables millisecond-scale optimization solutions