Hierarchical Progressive Pauli Noise Modeling with Residual Compensation for Multi-Qubit Quantum Circuits
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917420338249728 |
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| author | Ge, Xiangyu Zhao, Shengmei Wang, Le Zhang, Anqi |
| author_facet | Ge, Xiangyu Zhao, Shengmei Wang, Le Zhang, Anqi |
| contents | Quantum Noise Characterization (QNC) is indispensable for benchmarking and mitigating errors in Noisy Intermediate-Scale Quantum (NISQ) devices. However, traditional Quantum Process Tomography (QPT) suffers from an exponential parameter explosion $O(4^N)$, severely hindering its scalability. In this paper, we propose a Hierarchical Progressive Optimization (HPO) framework to efficiently extract high-order spatial crosstalk in multi-qubit systems. By introducing a mathematically rigorous combinatorial projection mask, the HPO framework strategically freezes foundational low-weight topologies and exclusively isolates high-weight Pauli correlations. This progressive masking mechanism effectively reduces the optimization complexity from $O(4^N)$ to a scalable $O(N \cdot 4^w)$, successfully mitigating the barren plateau phenomenon. Simulations show that our method achieves a remarkable parameter compression rate of 96.3% on a 5-qubit system while maintaining machine precision convergence. Furthermore, to validate its practical utility, we apply the extracted spatial crosstalk model to perform Quantum Error Mitigation (QEM) on a deep-circuit 10-qubit Harrow-Hassidim-Lloyd (HHL) algorithm. Compared to the traditional global depolarizing baseline, the HPO-guided mitigation scheme breaks the unmitigated crosstalk bottleneck, achieving an unprecedented state fidelity recovery from 0.7431 to 0.9381 ($ΔF \approx 19.5\%$). Our work provides a scalable, highly accurate, and indispensable blueprint for modeling and mitigating complex multi-body errors in large-scale quantum algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17326 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hierarchical Progressive Pauli Noise Modeling with Residual Compensation for Multi-Qubit Quantum Circuits Ge, Xiangyu Zhao, Shengmei Wang, Le Zhang, Anqi Quantum Physics Quantum Noise Characterization (QNC) is indispensable for benchmarking and mitigating errors in Noisy Intermediate-Scale Quantum (NISQ) devices. However, traditional Quantum Process Tomography (QPT) suffers from an exponential parameter explosion $O(4^N)$, severely hindering its scalability. In this paper, we propose a Hierarchical Progressive Optimization (HPO) framework to efficiently extract high-order spatial crosstalk in multi-qubit systems. By introducing a mathematically rigorous combinatorial projection mask, the HPO framework strategically freezes foundational low-weight topologies and exclusively isolates high-weight Pauli correlations. This progressive masking mechanism effectively reduces the optimization complexity from $O(4^N)$ to a scalable $O(N \cdot 4^w)$, successfully mitigating the barren plateau phenomenon. Simulations show that our method achieves a remarkable parameter compression rate of 96.3% on a 5-qubit system while maintaining machine precision convergence. Furthermore, to validate its practical utility, we apply the extracted spatial crosstalk model to perform Quantum Error Mitigation (QEM) on a deep-circuit 10-qubit Harrow-Hassidim-Lloyd (HHL) algorithm. Compared to the traditional global depolarizing baseline, the HPO-guided mitigation scheme breaks the unmitigated crosstalk bottleneck, achieving an unprecedented state fidelity recovery from 0.7431 to 0.9381 ($ΔF \approx 19.5\%$). Our work provides a scalable, highly accurate, and indispensable blueprint for modeling and mitigating complex multi-body errors in large-scale quantum algorithms. |
| title | Hierarchical Progressive Pauli Noise Modeling with Residual Compensation for Multi-Qubit Quantum Circuits |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2604.17326 |