Scalable Circuit Cutting and Scheduling in a Resource-constrained and Distributed Quantum System
<|reference_start|>Scalable Circuit Cutting and Scheduling in a Resource-constrained and Distributed Quantum System: Despite quantum computing's rapid development, current systems remain limited in practical applications due to their limited qubit count and quality. Various technologies, such as superconducting, trapped ions, and neutral atom quantum computing technologies are progressing towards a fault tolerant era, however they all face a diverse set of challenges in scalability and control. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. Future demonstrations hope to use quantum channels to couple systems, however current demonstrations can leverage classical communication with circuit cutting techniques. This involves cutting large circuits into smaller subcircuits and reconstructing them post-execution. However, existing cutting methods are hindered by lengthy search times as the number of qubits and gates increases. Additionally, they often fail to effectively utilize the resources of various worker configurations in a multi-node system. To address these challenges, we introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs and utilizes a community-based, bottom-up approach to cut circuits according to resource constraints, e.g., qubit counts, on each worker. FitCut also includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 3.88 times on the worker side, achieving a system-wide improvement of 2.86 times.<|reference_end|>
@article{kan2024scalable,
    title={Scalable Circuit Cutting and Scheduling in a Resource-constrained and
  Distributed Quantum System},
    author={Shuwen Kan, Zefan Du, Miguel Palma, Samuel A Stein, Chenxu Liu, Wenqi
  Wei, Juntao Chen, Ang Li, Ying Mao},
    journal={arXiv preprint arXiv:2405.04514},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04514},
    primaryClass={quant-ph cs.DC}
}