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arXiv 2202.04474quant-ph
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面向量子器件的马尔可夫噪声建模与参数提取框架

Markovian Noise Modelling and Parameter Extraction Framework for Quantum Devices

Dean Brand, Ilya Sinayskiy, Francesco Petruccione

AI总结:

提出一种与硬件无关的马尔可夫噪声建模与参数提取框架,可在基准测试中实时、可靠地同时校准多个量子硬件参数,并在 IBM Quantum 上验证。

摘要:

近年来,含噪声中等规模量子(NISQ)计算机已被广泛用作量子动力学的测试平台。本工作为在评估器件性能的基准流程中对量子系统的马尔可夫噪声和动力学进行建模,提供了一种新的、与硬件无关的框架。作为一个易于理解的示例,该框架在 IBM Quantum 计算机上展示了其应用与性能。该框架通过一个简化流程同时提取多个校准参数,比以往研究的校准实验和层析流程更加可靠。此外,该方法允许在一个综合流程中对量子计算机的若干硬件参数进行实时校准,从而提供对每台器件性能的定量认识,以便在未来的量子电路中加以考虑。本文提出的框架还有一个额外优势,即在提取参数时能够突出量子比特对之间的一致性,因此与一次性评估整个器件相比,其校准过程的计算开销更低。

英文摘要:

In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.

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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