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arXiv 2102.03282quant-phcs.LGstat.ML
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Effects of quantum resources on the statistical complexity of quantum circuits

Harvard University(哈佛大学);Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)(新加坡科技研究局高性能计算研究院);Zhejiang Sci-Tech University(浙江理工大学);Zhejiang University(浙江大学)

Kaifeng Bu, Dax Enshan Koh, Lu Li, Qingxian Luo, Yaobo Zhang

英文摘要:

We investigate how the addition of quantum resources changes the statistical complexity of quantum circuits by utilizing the framework of quantum resource theories. Measures of statistical complexity that we consider include the Rademacher complexity and the Gaussian complexity, which are well-known measures in computational learning theory that quantify the richness of classes of real-valued functions. We derive bounds for the statistical complexities of quantum circuits that have limited access to certain resources and apply our results to two special cases: (1) stabilizer circuits that are supplemented with a limited number of T gates and (2) instantaneous quantum polynomial-time Clifford circuits that are supplemented with a limited number of CCZ gates. We show that the increase in the statistical complexity of a quantum circuit when an additional quantum channel is added to it is upper bounded by the free robustness of the added channel. Finally, we derive bounds for the generalization error associated with learning from training data arising from quantum circuits.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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