Presentation
A Framework for Digital Twins of Future Quantum Clouds
DescriptionQuantum computing has emerged as a transformative technology capable of solving complex problems beyond classical systems' limits. However, present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high error susceptibility, which obstruct the execution of large, complex circuits. The rapid development of quantum processors has led to the proliferation of cloud-based quantum computing services from platforms like IBM, Google, and Amazon, introducing unique challenges in resource allocation, job scheduling, and multi-device orchestration as quantum workloads increase in complexity.
We present a comprehensive digital twin framework for quantum cloud infrastructures designed to model and simulate real quantum cloud systems while addressing distributed computing challenges. Developed in Python using the SimPy discrete-event simulation library, our framework replicates key aspects of quantum cloud environments including detailed quantum device modeling, job lifecycle management, and noise-aware fidelity estimation—making it the first to simulate superconducting gate-based quantum cloud systems at an administrative level with job fidelity. The framework supports distributed scheduling and concurrent execution of quantum jobs on networked quantum processors (QPUs) connected via real-time classical channels. It models circuit decomposition for workloads exceeding individual QPU limits, enabling parallel execution through inter-processor communication. We evaluate four distinct scheduling techniques, including a reinforcement learning-informed model, across metrics including runtime efficiency, fidelity preservation, and communication costs. Our analysis demonstrates how parallelized, noise-aware scheduling can improve computational throughput in distributed quantum infrastructures, providing proof-of-concept that our quantum cloud simulation framework can effectively serve as a digital twin for modeling and implementing practical quantum systems.
We present a comprehensive digital twin framework for quantum cloud infrastructures designed to model and simulate real quantum cloud systems while addressing distributed computing challenges. Developed in Python using the SimPy discrete-event simulation library, our framework replicates key aspects of quantum cloud environments including detailed quantum device modeling, job lifecycle management, and noise-aware fidelity estimation—making it the first to simulate superconducting gate-based quantum cloud systems at an administrative level with job fidelity. The framework supports distributed scheduling and concurrent execution of quantum jobs on networked quantum processors (QPUs) connected via real-time classical channels. It models circuit decomposition for workloads exceeding individual QPU limits, enabling parallel execution through inter-processor communication. We evaluate four distinct scheduling techniques, including a reinforcement learning-informed model, across metrics including runtime efficiency, fidelity preservation, and communication costs. Our analysis demonstrates how parallelized, noise-aware scheduling can improve computational throughput in distributed quantum infrastructures, providing proof-of-concept that our quantum cloud simulation framework can effectively serve as a digital twin for modeling and implementing practical quantum systems.
Event Type
Doctoral Showcase
TimeThursday, 20 November 202511:30am - 11:45am CST
Location230
Archive
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