QuSplit: achieving both high fidelity and throughput via job splitting on noisy quantum computers

Abstract

Abstract With the progression into the quantum utility era, computing is shifting toward quantum-centric architectures, where multiple quantum processors collaborate with classical computing resources. Platforms such as IBM Quantum and Amazon Braket exemplify this trend, enabling access to diverse quantum backends. However, efficient resource management remains a challenge, as quantum processors are highly susceptible to noise, which significantly impacts computation fidelity. Additionally, the heterogeneous noise characteristics across different processors add further complexity to scheduling and resource allocation. Existing scheduling strategies typically 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) — among the most promising quantum applications in the NISQ era — we hypothesize that executing the later stages of a job on a high-fidelity quantum processor can significantly improve overall fidelity. To verify this, we use VQE as a case study and develop a Genetic Algorithm-based scheduling framework that incorporates job splitting to optimize fidelity and throughput. Experimental results demonstrate that our approach consistently maintains high fidelity across all jobs while significantly enhancing system throughput. Furthermore, the proposed algorithm exhibits excellent scalability in handling an increasing number of quantum processors and larger workloads, making it a robust and practical solution for emerging quantum computing platforms. To further substantiate its effectiveness, we conduct experiments on a real quantum processor, IBM Strasbourg, which confirm that job splitting improves fidelity and reduces the number of iterations required for convergence.