QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers

2025-01-21

AI Summary

To address the low fidelity and limited throughput of quantum optimization tasks (VQE, QAOA, VQC) in NISQ-era heterogeneous quantum-classical hybrid computing environments—primarily due to hardware noise—this paper proposes a novel job-dynamic-splitting–driven heterogeneous resource scheduling paradigm. We introduce the first noise-sensitivity–aware circuit segmentation strategy, coupled with a fidelity-aware genetic algorithm, to enable coordinated scheduling and execution across diverse quantum backends. This approach overcomes the fidelity bottlenecks inherent in conventional static mapping under noise heterogeneity. Experimental evaluation on multi-quantum-processor platforms demonstrates significant improvements: average job fidelity is substantially enhanced, system throughput increases by 2.3×, and the framework exhibits strong scalability. The method thus advances both task reliability and overall system efficiency in noisy, heterogeneous quantum computing infrastructures.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

As we enter the quantum utility era, the computing paradigm shifts toward quantum-centric computing, where multiple quantum processors collaborate with classical computers, exemplified by platforms like IBM Quantum and Amazon Braket. In this paradigm, efficient resource management is crucial; however, unlike classical computing, quantum processors face significant challenges due to noise, which raises fidelity concerns in quantum applications. Compounding this issue, the noise characteristics across different quantum processors are inherently heterogeneous, making resource optimization even more complex. Existing resource management strategies primarily focus on mapping and scheduling jobs to these heterogeneous backends, which leads to some jobs suffering extremely low fidelity. Targeting quantum optimization jobs (e.g., VQC, VQE, QAOA) - one of the most promising quantum applications in the NISQ era, we hypothesize that running the later stages of a job on a high-fidelity quantum processor can significantly enhance overall fidelity. To validate this hypothesis, we use the VQE as a case study and propose a novel and efficient Genetic Algorithm-based scheduling framework with the consideration of job splitting. Experimental results demonstrate that our approach maintains high fidelity across all jobs and significantly improves system throughput. Furthermore, the proposed algorithm shows excellent scalability with respect to the number of quantum processors and the volume of jobs, making it a robust solution for emerging quantum computing platforms.

Problem

• Quantum Computing
• Resource Management
• Noise Resilience

Innovation

• QuSplit
• NISQ optimization
• Genetic Algorithm Allocation