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Clifford Assisted Optimal Pass Selection for Quantum Transpilation

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arxiv 2306.15020 v2 pith:EGMWVT4G submitted 2023-06-26 quant-ph cs.ARcs.ET

Clifford Assisted Optimal Pass Selection for Quantum Transpilation

classification quant-ph cs.ARcs.ET
keywords fidelityoptranpassesquantumapplicationtargetdeviceoften
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The fidelity of quantum programs in the NISQ era is limited by high levels of device noise. To increase the fidelity of quantum programs running on NISQ devices, a variety of optimizations have been proposed. These include mapping passes, routing passes, scheduling methods and standalone optimisations which are usually incorporated into a transpiler as passes. Popular transpilers such as those proposed by Qiskit, Cirq and Cambridge Quantum Computing make use of these extensively. However, choosing the right set of transpiler passes and the right configuration for each pass is a challenging problem. Transpilers often make critical decisions using heuristics since the ideal choices are impossible to identify without knowing the target application outcome. Further, the transpiler also makes simplifying assumptions about device noise that often do not hold in the real world. As a result, we often see effects where the fidelity of a target application decreases despite using state-of-the-art optimisations. To overcome this challenge, we propose OPTRAN, a framework for Choosing an Optimal Pass Set for Quantum Transpilation. OPTRAN uses classically simulable quantum circuits composed entirely of Clifford gates, that resemble the target application, to estimate how different passes interact with each other in the context of the target application. OPTRAN then uses this information to choose the optimal combination of passes that maximizes the target application's fidelity when run on the actual device. Our experiments on IBM machines show that OPTRAN improves fidelity by 87.66% of the maximum possible limit over the baseline used by IBM Qiskit. We also propose low-cost variants of OPTRAN, called OPTRAN-E-3 and OPTRAN-E-1 that improve fidelity by 78.33% and 76.66% of the maximum permissible limit over the baseline at a 58.33% and 69.44% reduction in cost compared to OPTRAN respectively.

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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. Comparing and learning figures of merit for quantum circuit compilation

    quant-ph 2026-07 conditional novelty 7.0

    A weighted probability of successful trials (wPST) figure of merit, predicted quickly by an XGBoost model, correlates over 50% better with true execution quality than standard circuit metrics.

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

    quant-ph 2026-07 conditional novelty 6.5

    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.

  3. QuTuner: Feature- and Learning-Guided Optimization Pass Tuning for Quantum Compilers

    quant-ph 2026-07 conditional novelty 6.0

    QuTuner retrieves and ranks full-space quantum optimization pass sequences from a large BO-built dataset using static features plus pass-response embeddings, then refines them with short Bayesian search.