受控量子多体系统中的分数化量化递归检测时间
Fractionally Quantized Recurrence Detection Times in Monitored Quantum Many-Body Systems
AI总结：
通过建立相互作用多体自旋系统中递归时间的普适上下界，揭示了递归时间与暗态数目的联系，并在IBM量子计算机上验证了分数化量化现象。
摘要：
递归时间量化了物理系统返回其初始状态所需的时间，在理解复杂系统的可预测性方面起着关键作用。在具有子空间测量的量子系统中，递归时间由Anandan-Aharonov相位决定，产生分数化量化的递归时间。然而，相互作用量子系统中的分数化量化现象尚未被探索。在这里，我们通过建立相互作用多体自旋系统中递归时间的普适下界和上界来填补这一空白。值得注意的是，我们研究了这些界限被接近的情景，揭示了监测下量子过程的速度。在特定情况下，我们的发现表明，复杂多体系统可以有效地映射到具有单个准粒子的动力学系统，从而产生整数量化的递归时间。我们的工作展示了递归时间与系统中暗态数目之间的宝贵联系，从而更深入地理解了希尔伯特空间碎片化、遍历性破缺、测量和相互作用效应之间复杂的相互作用。最后，我们的发现已在IBM量子计算机上实现，揭示了与理论预测一致的共振和分数化量化。这证明了非平衡拓扑分数化量化对噪声的鲁棒性，并突显了其在量子设备基准测试和探测暗态方面的潜在用途。
英文摘要：
Recurrence time quantifies the duration required for a physical system to return to its initial state, playing a pivotal role in understanding the predictability of complex systems. In quantum systems with subspace measurements, recurrence times are governed by Anandan-Aharonov phases, yielding fractionally quantized recurrence times. However, the fractional quantization phenomenon in interacting quantum systems remains unexplored. Here, we address this gap by establishing universal lower and upper bounds for recurrence times in interacting many-body spin systems. Notably, we investigate scenarios where these bounds are approached, shedding light on the speed of quantum processes under monitoring. In specific cases, our findings reveal that the complex many-body system can be effectively mapped onto a dynamical system with a single quasi-particle, leading to integer-quantized recurrence times. Our work demonstrates a valuable link between recurrence times and the number of dark states in the system, thus providing a deeper understanding of the intricate interplay between Hilbert-space fragmentation, ergodicity breaking, measurements, and interaction effects. Finally, our findings have been implemented on an IBM quantum computer, revealing resonances and fractional quantization in agreement with theoretical predictions. This demonstrates the resilience of non-equilibrium topological fractional quantization to noise and highlights its potential use for benchmarking quantum devices and probing dark states.
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。
