量子储备计算范式中的信息存储、 scrambling与损失
Storage, Scrambling, and Loss of Information in Quantum Reservoir Computing
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中文总结 AI 辅助
本研究针对典型量子储备计算（QRC）协议，引入过程张量导出的经典-量子态，提取诊断量表征信息的非局域 scrambling与损失，分析其与QRC性能的关联并探讨未来研究方向。
中文摘要 AI 辅助
量子储备计算（QRC）平台对给定时间序列处理任务的适用性，与其计算基底及设计的动力学特性密切相关。信息被注入该基底、经其处理后被读取，最终传入经训练以执行特定任务的线性读出层。本研究中，我们针对文献中典型QRC协议，引入了一种由过程张量导出的经典-量子态，该过程张量代表了此过程的动力学部分。利用该对象，物理子系统与过去输入子集间的互信息可被表示为Holevo量，随后我们将其用于数值研究常用QRC平台中基底的信息饱和、过去输入的 fading记忆以及注入信息的局部可访问性。接着，我们提取了两个诊断量，用于表征信息在基底内的非局域 scrambling及从基底的信息损失，并在哈密顿参数和测量强度下将其与QRC性能进行比较。最后，我们探讨了本研究引入的框架为QRC程序的研究与扩展开辟的未来方向。
英文摘要
The performance of a quantum reservoir computer in temporal processing tasks depends on how its driven quantum substrate retains information about past inputs, distributes it across physical degrees of freedom, and loses it through environmental dissipation and measurement feedback. We formulate these processes using a classical-quantum state obtained by restricting a reservoir process tensor to classical input encoding and single-time readout. Conditional subsystem Holevo quantities describe information about selected input histories and bound its accessibility to measurements on subsystems of the reservoir. In a six-qubit all-to-all transverse-field Ising reservoir, we find that the total stored information changes relatively little across Hamiltonian parameters, while its spatial distribution and temporal decay vary strongly. Effective diagnostics of these two behaviours identify different regions of high information-processing capacity for linear and higher-degree temporal tasks. Measurement-induced dephasing can improve noiseless task performance when it increases forgetting rates without strongly reducing information delocalisation. The framework separates storage from subsystem accessibility and provides a common description of information flow in driven quantum learning systems.
发表机构
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB–CSIC（复杂系统与交叉物理研究所（IFISC），巴利阿里群岛大学-西班牙国家研究委员会）
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