Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum
Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum
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DOI：
10.1145/3579371.3589043
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
S. Stein;N. Wiebe;Yufei Ding;James Ang;A. Li
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文献类型：
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作者：
S. Stein;N. Wiebe;Yufei Ding;James Ang;A. Li
近年来，量子计算技术发展迅速，新技术不断探索，错误率降低，量子处理器的量子比特容量不断增长。然而，如果不增加相应的噪音水平，短期量子算法仍然无法推导出来，从而导致不小的错误结果。量子纠错（原位错误减轻）和量子错误减轻（后诱导错误减轻）是量子算法场景中有前途的研究领域，旨在减轻量子错误。IBM最近发表了一篇文章，指出量子错误缓解是量子计算有用性的途径。最近的一项工作，即HAMMER，证明了存在一个潜在的结构，关于电路后感应错误时，映射到汉明频谱。然而，他们假设错误只发生在本地集群，而我们观察到，在较高的平均汉明距离，这种结构下降福尔斯了。在这项工作中，我们表明，这样的相关结构不仅是本地的，而且扩展了某些非本地聚类模式，这些模式可以通过泊松分布模型精确地描述，该模型采用输入电路，设备运行时状态（即，校准统计）和量子位拓扑。使用这个量子误差表征模型，我们开发了一个迭代算法在生成的贝叶斯网络状态图的归纳后的错误缓解。由于对误差分布潜在结构和所提出的迭代方法进行了更精确的建模，我们的Q-Beep方法提供了最先进的性能，并且可以使用16个实际IBMQ量子处理器将Bernstein-Vazirani电路的电路执行保真度提高234.6%，QAOA解决方案质量平均提高71.0%。对于其他基准测试，如QASMBench中的基准测试，保真度提高了17.8%。Q-Beep是一种轻量级的后处理技术，可以离线和远程执行，使其成为量子供应商采用和提供更可靠的电路感应结果的有用工具。Q-Beep在www.example.com上维护
Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processors' qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors. IBM recently published an article stating that Quantum Error Mitigation is the path to quantum computing usefulness. A recent work, namely HAMMER, demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they assumed that errors occur solely in local clusters, whereas we observe that at higher average Hamming distances this structure falls away. In this work, we show that such a correlated structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the proposed iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, a fidelity improvement of up to 17.8% is attained. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to adopt and provide more reliable circuit induction results. Q-Beep is maintained at github.com/pnnl/qbeep