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LightSABRE: A Lightweight and Enhanced SABRE Algorithm

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arxiv 2409.08368 v1 pith:YGSLEWK3 submitted 2024-09-12 quant-ph cs.ET

classification quant-phcs.ET
keywords lightsabrecircuitsquantumalgorithmsabreqiskitdelivershardware
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce LightSABRE, a significant enhancement of the SABRE algorithm that advances both runtime efficiency and circuit quality. LightSABRE addresses the increasing demands of modern quantum hardware, which can now accommodate complex scenarios, and circuits with millions of gates. Through iterative development within Qiskit, primarily using the Rust programming language, we have achieved a version of the algorithm in Qiskit 1.2.0 that is approximately 200 times faster than the implementation in Qiskit 0.20.1, which already introduced key improvements like the release valve mechanism. Additionally, when compared to the SABRE algorithm presented in Li et al., LightSABRE delivers an average decrease of 18.9\% in SWAP gate count across the same benchmark circuits. Unlike SABRE, which struggles with scalability and convergence on large circuits, LightSABRE delivers consistently high-quality routing solutions, enabling the efficient execution of large quantum circuits on near-term and future quantum devices. LightSABRE's improvements in speed, scalability, and quality position it as a critical tool for optimizing quantum circuits in the context of evolving quantum hardware and error correction techniques.

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Forward citations

Cited by 9 Pith papers

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

  1. Comparing and learning figures of merit for quantum circuit compilation

    quant-ph 2026-07 conditional novelty 7.0 of 10

    ML models that fuse circuit structure with device coherence data predict weighted PST far more accurately than classical gate-count FoMs, enabling better circuit selection inside compilers.

  2. Harvest: Resource-Aware Quantum Compilation for Magic State Protocols

    quant-ph 2026-08 conditional novelty 6.0 of 10

    Harvest co-optimizes placement, routing, scheduling, and magic-state supply for lattice-surgery quantum programs, reporting up to 17.8x speedup over sequential execution and reclaiming up to 72% of unused magic-state patches.

  3. Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting

    quant-ph 2026-07 conditional novelty 6.0 of 10

    Bitflip-gauge warm-start QAOA that aligns the ansatz with amplitude-damping noise improves 100-qubit Ising approximation ratios over non-gauge iterative warm-start at no extra circuit cost.

  4. MLIR for Quantum Beyond Gate Cancellation: Quantum Circuit Mapping Reimagined

    quant-ph 2026-07 accept novelty 6.0 of 10

    An MLIR-native A* qubit-routing pass outperforms QMAP and TKET on SWAP count and runtime and integrates into an open MLIR quantum compiler.

  5. Quantum Circuit Pruning: Improving Fidelity via Compilation-Aware Circuit Approximation

    quant-ph 2026-01 conditional novelty 6.0 of 10

    A routing-aware pruning rule removes two-qubit gates whose routing cost exceeds their rotation's worst-case fidelity impact, improving NISQ circuit fidelity in simulation.

  6. Leveraging Phase Polynomials for Quantum Circuit Optimization

    cs.PL 2025-06 conditional novelty 6.0 of 10

    A quantum circuit optimizer, PhasePoly, co-optimizes phase and output parity matrices and merges phase-polynomial blocks across gate barriers, reducing total gates by 34.9% and CNOT gates by 28.5% on average.

  7. 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.

  8. Efficient Circuit Transpilation of Commuting Gates on 2D Grids

    quant-ph 2026-07 accept novelty 5.5 of 10

    Greedy, problem-dependent SWAP-layer sequences on 2D grids roughly halve QAOA circuit depth and CZ count for sparse MaxCut and MIS graphs, improving hardware approximation ratios by up to ~6–9%.

  9. A High-Performance Multilevel Framework for Quantum Layout Synthesis

    quant-ph 2025-05 conditional novelty 5.0 of 10

    ML-SABRE, a multilevel layout synthesis framework built on the LightSABRE heuristic, cuts SWAP count by 45-65% and improves compilation speed by 2.5-3x on quantum benchmarks.

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