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

TL;DR

This paper presents a quantum computing approach to optimize data resource allocation for digital twin construction in autonomous driving, demonstrating rapid solutions on a 550-qubit quantum device.

Contribution

It introduces a novel two-stage stochastic integer programming model transformed into a QUBO, implemented on a large-scale quantum device for the first time in this context.

Findings

CIM-based optimization achieves millisecond-scale computation times.

Quantum approach outperforms quantum-inspired solvers in speed.

Classical solvers find slightly better solutions but are much slower.

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.