Circuit Folding: Modular and Qubit-Level Workload Management in Quantum-Classical Systems

• URL: http://arxiv.org/abs/2412.18705v1
• Date: Tue, 24 Dec 2024 23:34:17 GMT
• Title: Circuit Folding: Modular and Qubit-Level Workload Management in Quantum-Classical Systems
• Authors: Shuwen Kan, Yanni Li, Hao Wang, Sara Mouradian, Ying Mao,
• Abstract summary: Circuit knitting is a technique that offloads some of the computational burden from quantum circuits. We propose CiFold, a novel graph-based system that identifies and leverages repeated structures within quantum circuits. Our system has been extensively evaluated across various quantum algorithms, achieving up to 799.2% reduction in quantum resource usage.
• Score: 5.6744988702710835
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• Abstract: Quantum computing is an emerging technology that offers exponential speedups for certain problems. At the core of quantum-centric supercomputing is advanced middleware that manages the interaction between quantum hardware and classical computing infrastructure. Circuit knitting is a technique that leverages classical computation to offload some of the computational burden from quantum circuits, enabling them to exceed the capacity of current Noisy Intermediate-Scale Quantum (NISQ) devices. This is done by partitioning large circuits into smaller subcircuits, though at the cost of classical reconstruction and increased sampling overhead. Despite significant advancements in reducing the theoretical costs of circuit knitting, efficiently deploying these techniques across a broad range of quantum algorithms remains a challenge. In this work, we propose CiFold, a novel graph-based system that, at the individual qubit's level, identifies and leverages repeated structures within quantum circuits. By folding these repeated modules in parallel, CiFold constructs a meta-graph that guides the partitioning process, optimizing the cutting strategy through the integration of advanced circuit knitting techniques. Our system has been extensively evaluated across various quantum algorithms, achieving up to 799.2\% reduction in quantum resource usage, demonstrating its scalability and substantial performance improvements over state-of-the-art approaches.

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