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Hardware-aware Circuit Cutting and Distributed Qubit Mapping for Connected Quantum Systems

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arxiv 2412.18458 v1 pith:XNK5SP6L submitted 2024-12-24 cs.DC quant-ph

Hardware-aware Circuit Cutting and Distributed Qubit Mapping for Connected Quantum Systems

classification cs.DC quant-ph
keywords quantumdistributedqubitcircuitdismapfidelityhardwaremapping
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Quantum computing offers unparalleled computational capabilities but faces significant challenges, including limited qubit counts, diverse hardware topologies, and dynamic noise/error rates, which hinder scalability and reliability. Distributed quantum computing, particularly chip-to-chip connections, has emerged as a solution by interconnecting multiple processors to collaboratively execute large circuits. While hardware advancements, such as IBM's Quantum Flamingo, focus on improving inter-chip fidelity, limited research addresses efficient circuit cutting and qubit mapping in distributed systems. This project introduces DisMap, a self-adaptive, hardware-aware framework for chip-to-chip distributed quantum systems. DisMap analyzes qubit noise and error rates to construct a virtual system topology, guiding circuit partitioning, and distributed qubit mapping to minimize SWAP overhead and enhance fidelity. Implemented with IBM Qiskit and compared with the state-of-the-art, DisMap achieves up to a 20.8\% improvement in fidelity and reduces SWAP overhead by as much as 80.2\%, demonstrating scalability and effectiveness in extensive evaluations on real quantum hardware topologies.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting

    quant-ph 2026-07 conditional novelty 6.0

    A heuristic circuit-cutting framework combining METIS, tabu search, and quadratic assignment reports faster runtimes and fewer cuts than Qiskit's add-on on tested benchmarks.

  2. Optimizing Inter-chip Coupler Link Placement for Modular and Chiplet Quantum Systems

    quant-ph 2025-09 conditional novelty 6.0

    InterPlace selects inter-chip coupler placements in modular quantum systems via a multi-objective cost model, cutting SWAPs and inter-chip operations by up to 33.3% and boosting simulated fidelity by up to 53.0%.

  3. Quantum Circuit Partitioning For Effective Utilization of Quantum Resources

    quant-ph 2026-04 unverdicted novelty 4.0

    Partitioning helps larger highly connected circuits like GHZ with up to 55% error reduction via a custom method but degrades performance for brickwork circuits at scale.