Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908413162684416 |
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| author | Wang, Shuangbao Paul Mao, Jianzhou Sakk, Eric |
| author_facet | Wang, Shuangbao Paul Mao, Jianzhou Sakk, Eric |
| contents | This paper discusses the compilation, optimization, and error mitigation of quantum algorithms, essential steps to execute real-world quantum algorithms. Quantum algorithms running on a hybrid platform with QPU and CPU/GPU take advantage of existing high-performance computing power with quantum-enabled exponential speedups. The proposed approximate quantum Fourier transform (AQFT) for quantum algorithm optimization improves the circuit execution on top of an exponential speed-ups the quantum Fourier transform has provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15760 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms Wang, Shuangbao Paul Mao, Jianzhou Sakk, Eric Quantum Physics Machine Learning This paper discusses the compilation, optimization, and error mitigation of quantum algorithms, essential steps to execute real-world quantum algorithms. Quantum algorithms running on a hybrid platform with QPU and CPU/GPU take advantage of existing high-performance computing power with quantum-enabled exponential speedups. The proposed approximate quantum Fourier transform (AQFT) for quantum algorithm optimization improves the circuit execution on top of an exponential speed-ups the quantum Fourier transform has provided. |
| title | Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2506.15760 |