AI 中文总结
该研究通过数值模拟发现，量子存储库的环境耦合模式会影响其保留的输入历史，共享弛豫比独立局部损耗保留更多近期记忆，环境耦合是塑造量子存储库记忆的关键设计因素。
AI 中文摘要
对于开放量子存储库而言，系统如何遗忘是其计算方式的一部分。量子储备池计算以固定的量子动力学处理输入流，仅训练线性读出器。耗散会使旧输入逐渐消退，但现有研究通常固定环境过程，仅调整其强度。本文通过数值计算表明，耦合模式（即跃迁连接到独立或共享环境通道）会改变输入历史中哪些部分仍可访问。对有限自旋存储库的配对模拟保持哈密顿量、输入、测量和读出器固定，测试的模式产生不同的任务轮廓，无通用最优方案。与独立局部损耗相比，共享弛豫保留了更多近期输入历史，且当量子比特以不同相对相位贡献于共享衰减通道时，保留的记忆会发生变化。这种排序在系统规模、哈密顿量、输入协议和目标控制中均存在。因此，环境耦合不止是阻尼参数，还是一种设计层面，不仅决定信息消退的速度，还决定计算可用的输入历史内容。
英文摘要
For an open quantum reservoir, how the system forgets is part of how it computes. Quantum reservoir computing processes input streams with fixed quantum dynamics and trains only a linear readout. Dissipation can make old inputs fade, but prior studies commonly fix the environmental process and tune only its strength. Here we show numerically that the coupling pattern, meaning whether transitions connect to separate or shared environmental channels, changes which parts of the input history remain accessible. Paired simulations of finite spin reservoirs keep the Hamiltonian, inputs, measurements, and readout fixed. The tested patterns produce distinct task profiles, with no universal winner. Shared relaxation preserves more recent input history than independent local loss, and the retained memory changes when the qubits contribute with different relative phases to the shared decay channel. This ordering recurs across system sizes, Hamiltonians, input protocols, and targeted controls. Environmental coupling is therefore more than a damping parameter: it is a design layer that shapes not only how quickly information fades, but which input history remains available for computation.
Comments23 pages, 8 figures, 4 tables
