Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909291282169856 |
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| author | Kukliansky, Alon Cincio, Lukasz Younis, Ed Iancu, Costin |
| author_facet | Kukliansky, Alon Cincio, Lukasz Younis, Ed Iancu, Costin |
| contents | Existing numerical optimizers deployed in quantum compilers use expensive $\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\mathcal{O}(2^n)$ circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a trade-off between compilation speed and solution quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation Kukliansky, Alon Cincio, Lukasz Younis, Ed Iancu, Costin Quantum Physics Existing numerical optimizers deployed in quantum compilers use expensive $\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\mathcal{O}(2^n)$ circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a trade-off between compilation speed and solution quality. |
| title | Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2405.12866 |