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arXiv 2602.00610quant-ph
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在Rydberg原子阵列中实现实用的量子共振计算

Practical Quantum Reservoir Computing in Rydberg Atom Arrays

University of Science and Technology of China(中国科学技术大学);Hefei National Laboratory, University of Science and Technology of China(合肥国家实验室,中国科学技术大学)

Dong-Sheng Liu, Qing-Xuan Jie, Chang-Ling Zou, Xi-Feng Ren, Guang-Can Guo

AI总结:

本文比较了单步和多步量子共振计算架构在Rydberg原子阵列中的性能,发现单步架构在面对动态相位和退相干时更稳健,而多步架构因采样噪声导致信息处理能力下降。

摘要:

量子共振计算(QRC)是一种有前景的量子机器学习框架,适用于近期量子平台。然而,不同QRC架构在现实约束下的性能尚待探索。本文对单步QRC(SS-QRC)和多步QRC(MS-QRC)架构在Rydberg原子阵列中的性能进行了比较研究。我们证明,尽管MS-QRC的性能高度依赖于物质的动态相位和退相干,SS-QRC表现出更大的鲁棒性。通过使用随机测量工具箱来减轻测量开销,我们发现采样噪声会破坏MS-QRC所需的收敛性属性。这导致MS-QRC的信息处理能力(IPC)显著降低,在非线性时间序列基准上性能下降。相比之下,SS-QRC在时间和非时间任务中均保持高IPC和准确性。我们的结果表明,由于其对系统配置和统计噪声的抗性,SS-QRC是近期实际应用的首选候选。

英文摘要:

Quantum reservoir computing (QRC) is a promising quantum machine learning framework for near-term quantum platforms, yet the performance of different QRC architectures under realistic constraints remains largely unexplored. Here, we provide a comparative numerical study of single-step-QRC (SS-QRC) and multi-step-QRC (MS-QRC) architectures implemented on a Rydberg atom array. We demonstrate that while MS-QRC performance is highly sensitive to the underlying dynamical phase of matter and decoherence, SS-QRC exhibits greater robustness. Using the randomized measurement toolbox to mitigate measurement overhead, we reveal that sampling noise undermines the convergence property required for MS-QRC. This leads to a significant reduction in the information processing capacity (IPC) of MS-QRC, deteriorating its performance on nonlinear time-series benchmarks. In contrast, SS-QRC maintains high IPC and accuracy across both temporal and non-temporal tasks. Our results suggest SS-QRC as a preferred candidate for near-term practical applications due to its resilience to system configurations and statistical noise.

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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