AI 中文总结
通过非平衡热力学框架，揭示驱动开放量子系统在量子临界区的计算峰值源于能隙闭合导致的谱共振，并导出连续时间处理中预测能力与信息耗散及不可逆功之间的基本权衡。
AI 中文摘要
量子储层计算为处理复杂时间序列数据提供了一个框架，但其基本的计算和能量极限尚未解决。在这里，我们建立了一个非平衡热力学框架，将驱动开放量子系统的宏观预测性能与其微观能量成本联系起来。通过将Holevo容量映射到Bogoliubov-Kubo-Mori几何流形上，我们解析地证明了量子临界区域内的计算峰值源于严格的谱共振：能隙的闭合迫使储层的跃迁频率与混沌驱动对齐。为了评估相关的热力学成本，我们引入了量子信息耗散来量化储层结构保留的非预测历史数据，推导出连续时间处理的广义Landauer界限。这揭示了一个基本的热力学权衡：解锁最佳预测能力的临界共振本质上最大化了信息耗散和用于环境擦除的不可逆功。此外，相干分解表明，动态量子相干性在不要求额外机械功的情况下严格增强了预测能力。这些发现确立了量子学习设备的终极能量极限，为设计节能的量子神经形态硬件提供了理论原理。
英文摘要
Quantum reservoir computing provides a framework for processing complex temporal data, yet its fundamental computational and energetic limits remain unresolved. Here, we establish a non-equilibrium thermodynamic framework that links the macroscopic predictive performance of driven open quantum systems to their microscopic energetic costs. By mapping Holevo capacities onto the Bogoliubov-Kubo-Mori geometric manifold, we analytically prove that the computational peak within the quantum critical region originates from a spectral resonance: the closing of the intrinsic energy gap forces the reservoir's internal transition frequencies to align with the chaotic drive. To evaluate the associated thermodynamic costs, we introduce quantum informational dissipation to quantify the non-predictive historical data retained by the reservoir. This allows us to derive a generalized Landauer bound for continuous temporal processing, which reveals a fundamental thermodynamic trade-off: the critical resonance that maximizes predictive capacity simultaneously maximizes informational dissipation and the irreversible work required for environmental erasure. Furthermore, coherence decomposition demonstrates that quantum coherences amplify predictive capacity without demanding additional mechanical work. These findings establish the fundamental energetic limits of quantum learning devices, providing theoretical principles for designing energy-efficient quantum neuromorphic hardware.
Comments8+18 pages, 2+2 figures
