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arXiv 2505.02491quant-ph
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量子储层计算中的非马尔可夫性与记忆增强

Non-Markovianity and memory enhancement in Quantum Reservoir Computing

Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC（跨学科物理与复杂系统研究所 (IFISC) 巴利阿里群岛大学-西班牙高等科学研究理事会）

Antonio Sannia, Ricard Ravell Rodríguez, Gian Luca Giorgi, Roberta Zambrini

发表信息：
npj Quantum Information 12, 111 (2026)
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AI总结：

研究量子储层计算中马尔可夫动力学限制长期记忆问题，提出非马尔可夫动力学可克服此局限，通过解析推导和数值模拟证明其优势，引入嵌入方法实现可控转变，确立量子非马尔可夫性为增强记忆关键资源。

摘要：

处理随时间变化的数据时，机器学习模型需要具备记忆过去输入的能力。在量子储层计算中，目前所有架构都依赖马尔可夫动力学，我们证明这会导致过去信息指数衰减，限制长期记忆能力。非马尔可夫动力学可克服此限制，实现更长时间记忆保留。通过解析推导记忆界限并进行数值模拟验证，表明非马尔可夫储层在需要短期和长期相关性共存的任务中表现更优。还引入嵌入方法实现从马尔可夫到非马尔可夫演化的可控转变，为实际应用提供途径。结果表明量子非马尔可夫性是增强量子机器学习架构记忆的关键资源，对量子神经网络有广泛影响。

英文摘要：

Featuring memory of past inputs is a fundamental requirement for machine learning models processing time-dependent data. In quantum reservoir computing, all architectures proposed so far rely on Markovian dynamics, which, as we prove, inherently lead to an exponential decay of past information, thereby limiting long-term memory capabilities. We demonstrate that non-Markovian dynamics can overcome this limitation, enabling extended memory retention. By analytically deriving memory bounds and supporting our findings with numerical simulations, we show that non-Markovian reservoirs can outperform their Markovian counterparts, particularly in tasks that require a coexistence of short- and long-term correlations. We introduce an embedding approach that allows a controlled transition from Markovian to non-Markovian evolution, providing a path for practical implementations. Our results establish quantum non-Markovianity as a key resource for enhancing memory in quantum machine learning architectures, with broad implications in quantum neural networks.

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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