一句话：提出后处理技术 HAMMER，利用错误结果的汉明空间结构重建含噪量子输出分布，平均提升解质量 1.37 倍。
近中期量子计算机受高错误率制约，程序执行产生的正确与错误结果混杂在噪声输出分布中难以区分。作者发现，错误结果并非任意，而是在汉明空间中呈现明确结构：在 IBM 与 Google 量子计算机上的实验表明，出现频率最高的错误结果往往在汉明距离上更接近正确结果。基于这一观察，论文提出 HAMMER（Hamming Reconstruction），一种对输出分布进行后处理的重建技术，使结果分布的保真度更高，从而更准确地推断正确结果。作者在 Google 和 IBM 平台上超过 500 个不同量子电路的实验数据上评估 HAMMER，平均获得 1.37 倍的解质量提升；在 Google 公开的 QAOA 数据集上，HAMMER 还能锐化代价函数景观中的梯度。
Quantum computers with hundreds of qubits will be available soon. Unfortunately, high device error-rates pose a significant challenge in using these near-term quantum systems to power real-world applications. Executing a program on existing quantum systems generates both correct and incorrect outcomes, but often, the output distribution is too noisy to distinguish between them. In this paper, we show that erroneous outcomes are not arbitrary but exhibit a well-defined structure when represented in the Hamming space. Our experiments on IBM and Google quantum computers show that the most frequent erroneous outcomes are more likely to be close in the Hamming space to the correct outcome. We exploit this behavior to improve the ability to infer the correct outcome. We propose Hamming Reconstruction (HAMMER), a post-processing technique that leverages the observation of Hamming behavior to reconstruct the noisy output distribution, such that the resulting distribution has higher fidelity. We evaluate HAMMER using experimental data from Google and IBM quantum computers with more than 500 unique quantum circuits and obtain an average improvement of 1.37x in the quality of solution. On Google’s publicly available QAOA datasets, we show that HAMMER sharpens the gradients on the cost function landscape.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{Tannu0AQ22,
title = {HAMMER: boosting fidelity of noisy Quantum circuits by exploiting Hamming behavior of erroneous outcomes},
author = {Swamit Tannu and Poulami Das and Ramin Ayanzadeh and Moinuddin Qureshi},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507703}
}