通过具有可调量子记忆的近邻自旋链从量子储备池到量子极限学习机
From quantum reservoirs to quantum extreme learning machines through a nearest-neighbor spin chain with tunable quantum memory
- Qilimanjaro Quantum Tech（基利曼哈罗量子科技）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出近邻自旋链架构可在量子储备池计算与量子极限学习机间插值，通过贝叶斯优化调整参数，发现混沌边缘性能最优，时间处理依赖动力学机制而非连接性。
AI中文摘要：
量子储备池计算（QRC）通过在量子系统的循环状态中保留对过去输入的记忆来处理时间序列数据，而量子极限学习机（QELM）则舍弃该记忆，每一步都重置系统，使得仅最新输入决定响应。这两种方法通常被视为独立的计算范式。本文证明，它们是单一架构的两个极限，由输入编码长度（即每一步被新数据覆盖的量子比特数量）连接。当单个量子比特被重新编码时，系统作为标准QRC运行；当整个寄存器被重新编码时，系统作为QELM运行；中间长度则在两者之间插值。被覆盖的量子比特在显式寄存器中保存近期历史，而其余量子比特永不重置，通过其演化的量子状态传递更早的输入，因此在固定系统规模下，编码长度在显式存储和循环存储之间重新分配记忆。在每个编码长度下，通过贝叶斯优化调整储备池哈密顿量和演化时间，研究发现，当任务需要追溯到遥远的过去时，循环量子记忆至关重要；当相关历史较短时，无记忆重置的极限已足够。对于每个任务，最佳储备池都工作在混沌边缘，其性能与相同规模的全连接储备池（具有随机全对耦合）相当，表明时间处理所需的是动力学机制而非连接性。
英文摘要：
Quantum Reservoir Computing (QRC) processes temporal data by retaining a memory of past inputs in the recurrent state of a quantum system, whereas a Quantum Extreme-Learning Machine (QELM) discards that memory, resetting the system at every step so that only the most recent input shapes the response. The two are usually treated as separate computational paradigms. We show that they are the two limits of a single architecture, connected by the input-encoding length, that is, the number of qubits overwritten with fresh data at each step. When a single qubit is re-encoded the system operates as a standard QRC, when the whole register is re-encoded it operates as a QELM, and intermediate lengths interpolate between them. The overwritten qubits hold the recent past in an explicit register, while the remaining qubits are never reset and carry older inputs forward in their evolving quantum state, so the encoding length redistributes memory between explicit and recurrent storage at fixed system size. Tuning the reservoir Hamiltonian and the evolution time with Bayesian optimization at each encoding length, we find that recurrent quantum memory is essential when a task must reach far into the past, and dispensable when the relevant history is short, where the memoryless reset limit already suffices. For every task the best reservoirs operate at the edge of chaos, where they perform as well as a densely connected reservoir with random all-to-all couplings of the same size, indicating that what temporal processing requires is the dynamical regime rather than the connectivity.
