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Benchmarking Quantum Circuit Transformation with QKNOB Circuits

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arxiv 2301.08932 v2 pith:S4NTJI5Q submitted 2023-01-21 quant-ph

classification quant-ph
keywords qknobquantumcircuittransformationalgorithmscircuitsbenchmarkconstruction
verification ladder T0 review T1 audit T2 compute T3 formal

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Current superconducting quantum devices impose strict connectivity constraints on quantum circuit execution, necessitating circuit transformation before executing quantum circuits on physical hardware. Numerous quantum circuit transformation (QCT) algorithms have been proposed. To enable faithful evaluation of state-of-the-art QCT algorithms, this paper introduces QKNOB (Qubit mapping Benchmark with Known Near-Optimality), a novel benchmark construction method for QCT. QKNOB circuits have built-in transformations with near-optimal (close to the theoretical optimum) SWAP count and depth overhead. QKNOB provides general and unbiased evaluation of QCT algorithms. Using QKNOB, we demonstrate that SABRE, the default Qiskit compiler, consistently achieves the best performance on the 53-qubit IBM Q Rochester and Google Sycamore devices for both SWAP count and depth objectives. Our results also reveal significant performance gaps relative to the near-optimal transformation costs of QKNOB. Our construction algorithm and benchmarks are open-source.

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Cited by 2 Pith papers

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

  1. Assessing Quantum Layout Synthesis Tools via Known Optimal-SWAP Cost Benchmarks

    quant-ph 2025-02 conditional novelty 6.0 of 10

    QUBIKOS is the first benchmark set with provably optimal non-zero SWAP counts, showing current quantum layout synthesis tools are far from optimal.

  2. Improving and benchmarking NISQ qubit routers

    quant-ph 2025-02 conditional novelty 4.0 of 10

    A SABRE heuristic that keeps only the basic and decay terms outperforms lookahead-based routers in fidelity for larger NISQ devices under a thermal relaxation noise model.

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