Evaluating noises of boson sampling with statistical benchmark methods

• URL: http://arxiv.org/abs/2510.00056v2
• Date: Mon, 13 Oct 2025 04:00:07 GMT
• Title: Evaluating noises of boson sampling with statistical benchmark methods
• Authors: Yang Ji, Yongjin Ye, Qiao Wang, Shi Wang, Jie Hou, Yongzheng Wu, Zijian Wang, Bo Jiang,
• Abstract summary: It is important to know the noise levels in order to cautiously demonstrate the quantum computational advantage.<n>Based on those statistical benchmark methods such as the correlators and the clouds, we quantify noises of photon partial distinguishability and photon loss compensated by dark counts.<n>Our results indicate that the statistical benchmark methods can also work in the task of evaluating noises of boson sampling.
• Score: 14.827343113391665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The lack of self-correcting codes hiders the development of boson sampling to be large-scale and robust. Therefore, it is important to know the noise levels in order to cautiously demonstrate the quantum computational advantage or realize certain tasks. Based on those statistical benchmark methods such as the correlators and the clouds, which are initially proposed to discriminate boson sampling and other mockups, we quantificationally evaluate noises of photon partial distinguishability and photon loss compensated by dark counts. This is feasible owing to the fact that the output distribution unbalances are suppressed by noises, which are actually results of multi-photon interferences. This is why the evaluation performance is better when high order correlators or corresponding clouds are employed. Our results indicate that the statistical benchmark methods can also work in the task of evaluating noises of boson sampling.

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