Concept learning of parameterized quantum models from limited measurements
黃柏崴先生 - 新加坡量子科技研究中心
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2021-01-01 21:56:00 ~
2021-02-01 12:00:00See event details for additional info.
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成功大學前沿量子科技研究中心2樓演講廳
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Concept learning of parameterized quantum models from limited measurements
黃柏崴先生 - 新加坡量子科技研究中心
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日期 |
2024-09-12 |
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時間 |
11:00-12:00 |
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地點 |
Meeting Room, 2F, QFort, NCKU |
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領域 |
Quantum Information Science |
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講者 |
黃柏崴先生 - 新加坡量子科技研究中心 |
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題目 |
Concept learning of parameterized quantum models from limited measurements |
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摘要 |
Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels. While learning-theoretic frameworks establish the sample complexity and the number of measurement shots per sample required for learning such statistical quantities, the interplay between these two variables has not been adequately quantified before. In this work, we take the probabilistic nature of quantum measurements into account in classical modelling and discuss these quantities under a single unified learning framework. We provide provable guarantees for learning parameterized quantum models that also quantify the asymmetrical effects and interplay of the two variables on the performance of learning algorithms. These results show that while increasing the sample size enhances the learning performance of classical machines, even with single-shot estimates, the improvements from increasing measurements become asymptotically trivial beyond a constant factor. We further apply our framework and theoretical guarantees to study the impact of measurement noise on the classical surrogation of parameterized quantum circuit models. Our work provides new tools to analyse the operational influence of finite measurement noise in the classical learning of quantum systems. |
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