TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916003505504256 |
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| author | Hasanat, Mohammad Abrarul Ludmir, Jason Patel, Tirthak Roy, Rohan Basu |
| author_facet | Hasanat, Mohammad Abrarul Ludmir, Jason Patel, Tirthak Roy, Rohan Basu |
| contents | Quantum processors are being integrated into HPC ecosystems as co-processors, where compilation of quantum circuits into hardware-executable form determines both output fidelity and runtime. Current compilers use a fixed pass sequence and ignore the fact that optimal pass selection varies with circuit, hardware, and noise conditions. We present TuniQ, a reinforcement learning-based system that selects compilation passes at each pipeline stage, adapting to circuit, backend, and current noise profile. TuniQ introduces several novel design components like a dual-encoder for stage-aware representation, shaped rewards for cross-stage credit assignment, and dynamic action masking for valid compilation. Evaluated across diverse quantum workloads on multiple IBM Quantum Cloud processors, TuniQ improves fidelity and reduces compilation time over the state-of-the-art IBM Qiskit transpiler, generalizes across backends without retraining, and scales strongly to utility-scale circuits with growing advantage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11375 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency Hasanat, Mohammad Abrarul Ludmir, Jason Patel, Tirthak Roy, Rohan Basu Quantum Physics Emerging Technologies Quantum processors are being integrated into HPC ecosystems as co-processors, where compilation of quantum circuits into hardware-executable form determines both output fidelity and runtime. Current compilers use a fixed pass sequence and ignore the fact that optimal pass selection varies with circuit, hardware, and noise conditions. We present TuniQ, a reinforcement learning-based system that selects compilation passes at each pipeline stage, adapting to circuit, backend, and current noise profile. TuniQ introduces several novel design components like a dual-encoder for stage-aware representation, shaped rewards for cross-stage credit assignment, and dynamic action masking for valid compilation. Evaluated across diverse quantum workloads on multiple IBM Quantum Cloud processors, TuniQ improves fidelity and reduces compilation time over the state-of-the-art IBM Qiskit transpiler, generalizes across backends without retraining, and scales strongly to utility-scale circuits with growing advantage. |
| title | TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2605.11375 |