Provable bounds for noise-free expectation values computed from noisy samples
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866910679531782144 |
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| author | Barron, Samantha V. Egger, Daniel J. Pelofske, Elijah Bärtschi, Andreas Eidenbenz, Stephan Lehmkuehler, Matthis Woerner, Stefan |
| author_facet | Barron, Samantha V. Egger, Daniel J. Pelofske, Elijah Bärtschi, Andreas Eidenbenz, Stephan Lehmkuehler, Matthis Woerner, Stefan |
| contents | In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the implications for optimization and machine learning applications. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the Conditional Value at Risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on a real quantum computer involving up to 127 qubits. The results show a strong alignment with theoretical predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_00733 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Provable bounds for noise-free expectation values computed from noisy samples Barron, Samantha V. Egger, Daniel J. Pelofske, Elijah Bärtschi, Andreas Eidenbenz, Stephan Lehmkuehler, Matthis Woerner, Stefan Quantum Physics Emerging Technologies In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the implications for optimization and machine learning applications. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the Conditional Value at Risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on a real quantum computer involving up to 127 qubits. The results show a strong alignment with theoretical predictions. |
| title | Provable bounds for noise-free expectation values computed from noisy samples |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2312.00733 |