Quantum annealers like those manufactured by D-Wave Systems are designed to find high quality solutions to optimization problems that are typically hard for classical computers. They utilize quantum effects like tunneling to evolve toward low-energy states representing solutions to optimization problems. However, their analog nature and limited control functionalities present challenges to correcting or mitigating hardware errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing (QA). In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is well-suited for currently available noisy intermediate-scale quantum annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being more flexible and compatible with a wider range of hardware connectivity patterns. In comparisons against standard QA without error mitigation on larger problem instances that could not be handled by previous methods, RBM consistently gets better energies and ground state probabilities across parameterized problem sets.

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通过基于复制的错误缓解来提高量子退火精度** 本文的初步版本出现在 2024 年意大利伊斯基亚举行的第 21 届 ACM 国际计算前沿会议的会议记录中。当前版本包括对之前关于错误缓解的工作的扩展分析量子计算，与解决链式问题有关的新部分，特别是最大集团问题，并且还重做或扩展了所有实验。


D-Wave Systems 制造的量子退火器旨在为经典计算机通常难以解决的优化问题找到高质量的解决方案。他们利用隧道效应等量子效应向低能态演化，代表优化问题的解决方案。然而，它们的模拟性质和有限的控制功能给纠正或减轻硬件错误带来了挑战。随着量子计算向应用迈进，有效的误差抑制是一个重要的研究目标。我们提出了一种基于并行量子退火 (QA) 的新方法，称为基于复制的缓解 (RBM)。在 RBM 中，代表相同逻辑量子位的物理量子位分散在硬件中嵌入的问题的不同副本中。这减轻了硬件偏差，与当前退火器中有限的量子位连接兼容，并且非常适合当前可用的嘈杂的中等规模量子退火器。我们的实验分析表明，RBM ​​提供的解决方案质量与以前的方法相当，同时更加灵活且与更广泛的硬件连接模式兼容。与之前方法无法处理的较大问题实例上没有错误缓解的标准 QA 相比，RBM ​​在参数化问题集中始终获得更好的能量和基态概率。