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arXiv:2511.09760 · IEEE International Conference on Collaborative Advances in Software and Computing (CASCON)

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

Huixiang Zhang, Mahzabeen Emu

Published Nov 10, 2025• 0 citations
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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.

DOI
10.1109/cascon66301.2025.00088

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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 (10310^3103 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.
Metaverse can unleash the potentials of Internet of Sense (IoS) communication by intertwining objects and environment between physical world and parallel virtual world. In order to digitally experience smell or taste and navigate effortlessly in virtual reality, optimal resource allocation to strengthen sensing data based infrastructure system is a critical research challenge. The Metaverse Infrastructure Service Providers (MISPs) tap into data marketplace and subscribe to resources in advance for fulfilling the needs of data consumers and users. The demand of the data based services being uncertain, non-optimal subscription schemes may lead to unwanted resource wastage or shortage. Thus, we propose a Stochastic Integer Programming (SIP) model with two phase reservation and on-demand plans for optimal resource allocation in data marketplace. Further along this line, we strive to predict the demand by leveraging Quantum Neural Networks (QNN) that is able to learn with fewer historical data in comparison to classical machine/deep learning paradigms. Extensive simulation results justify that QNN as a supporting model can significantly reduce the computational complexities of SIP formulation. This research can contribute to reduce Metaverse resource fabrication costs, upgrade the profit margin for MISPs by increasing data based service sales revenue, provide real-time resource management decisions, and overall make real impacts in the virtual world.
Metaverse can unleash the potentials of Internet of Sense (IoS) communication by intertwining objects and environment between physical world and parallel virtual world. In order to digitally experience smell or taste and navigate effortlessly in virtual reality, optimal resource allocation to strengthen sensing data based infrastructure system is a critical research challenge. The Metaverse Infrastructure Service Providers (MISPs) tap into data marketplace and subscribe to resources in advance for fulfilling the needs of data consumers and users. The demand of the data based services being uncertain, non-optimal subscription schemes may lead to unwanted resource wastage or shortage. Thus, we propose a Stochastic Integer Programming (SIP) model with two phase reservation and on-demand plans for optimal resource allocation in data marketplace. Further along this line, we strive to predict the demand by leveraging Quantum Neural Networks (QNN) that is able to learn with fewer historical data in comparison to classical machine/deep learning paradigms. Extensive simulation results justify that QNN as a supporting model can significantly reduce the computational complexities of SIP formulation. This research can contribute to reduce Metaverse resource fabrication costs, upgrade the profit margin for MISPs by increasing data based service sales revenue, provide real-time resource management decisions, and overall make real impacts in the virtual world.
Metaverse refers to the intersection of parallel virtual worlds with their physical counterparts by allowing users to interact with virtual people, objects, and environments. Resource allocation in various aspects of Metaverse domains, called as MetaSlices hereinafter, is a crucial optimization research problem. To serve this purpose, we consider a MetaSlice framework with the notion of sharing resources among common functions and enable placing time-sensitive services at the edge of multi-tier architecture in proximity to users. Unfortunately, the classical Integer Linear Programming is inappropriate for such heavily constrained optimization problem due to the extensive running time and memory. Hence, we model a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation to simultaneously optimize resources and secure Quality of Service for MetaSlices as a paradigm shift towards quantum computing. Furthermore, we propose to employ a hybrid classical-quantum WSQA to optimize resource under uncertainty, offer ultra-low running time, and increase service acceptance rate/scalability in resource-hungry and dynamic Metaverse system. Extensive simulation results demonstrate that WSQA outperforms other classical and standalone quantum annealing approaches, even with the limited availability of qubits (quantum resources). Thus, this research paves the way to decrease massive resource fabrication costs and upgrade profit margin for Metaverse Internet Service Providers, while simultaneously providing real-time services for Metaverse users.
Metaverse refers to the intersection of parallel virtual worlds with their physical counterparts by allowing users to interact with virtual people, objects, and environments. Resource allocation in various aspects of Metaverse domains, called as MetaSlices hereinafter, is a crucial optimization research problem. To serve this purpose, we consider a MetaSlice framework with the notion of sharing resources among common functions and enable placing time-sensitive services at the edge of multi-tier architecture in proximity to users. Unfortunately, the classical Integer Linear Programming is inappropriate for such heavily constrained optimization problem due to the extensive running time and memory. Hence, we model a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation to simultaneously optimize resources and secure Quality of Service for MetaSlices as a paradigm shift towards quantum computing. Furthermore, we propose to employ two hybrid classical-quantum strategies, Warm Start and Cold Start Quantum Annealing to optimize resource under bandwidth uncertainty, offer ultra-low running time, and increase service acceptance rate/scalability in a resource-hungry and dynamic Metaverse system.
Efficient management of water resource systems requires solving complex optimization problems involving nonlinear dynamics, large datasets, and multiple operational constraints. Traditional optimization techniques such as linear programming, dynamic programming, and evolutionary algorithms have been widely applied in water resource management; however, these methods often face computational challenges when applied to large-scale hydrological systems. This paper proposes a hybrid quantum-classical optimization framework for water resource management using topological qubit models. The water allocation problem is formulated as a network-based optimization model and transformed into a quadratic unconstrained binary optimization (QUBO) problem suitable for quantum optimization algorithms. A hybrid computational approach combining classical hydrological simulation with quantum optimization is developed to improve computational efficiency. Simulation results demonstrate that the proposed model achieves faster convergence and lower operational cost compared with classical optimization methods. The study shows that quantum computing provides a promising computational approach for solving complex water resource optimization problems. Future work will focus on implementing the proposed model on real quantum hardware and integrating real-world hydrological datasets.
Abstract Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space–time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.