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Condensed Matter > Disordered Systems and Neural Networks

Title: Unscrambling of single-particle wave functions in systems localized through disorder and monitoring

Abstract: In systems undergoing localization-delocalization quantum phase transitions due to disorder or monitoring, there is a crucial need for robust methods capable of distinguishing phases and uncovering their intrinsic properties. In this work, we develop a process of finding a Slater determinant representation of free-fermion wave functions that accurately characterizes localized particles, a procedure we dub "unscrambling". The central idea is to minimize the overlap between envelopes of single-particle wave functions or, equivalently, to maximize the inverse participation ratio of each orbital. This numerically efficient methodology can differentiate between distinct types of wave functions: exponentially localized, power-law localized, and conformal critical, also revealing the underlying physics of these states. Furthermore, we apply this approach to a more challenging problem involving disordered monitored free fermions in one dimension, where the unscrambling process unveils the presence of a conformal critical phase and a localized area-law quantum Zeno phase. Importantly, our method can also be extended to free fermion systems without particle number conservation, which we demonstrate by estimating the phase diagram of $\mathbb{Z}_2$-symmetric disordered monitored free fermions. Our results unlock the potential of utilizing single-particle wave functions to gain valuable insights into the localization transition properties in systems such as monitored free fermions and disordered models.
Comments: 11 pages, 11 figures. Comments are welcome
Subjects: Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Quantum Physics (quant-ph)
Cite as: arXiv:2403.10725 [cond-mat.dis-nn]
  (or arXiv:2403.10725v2 [cond-mat.dis-nn] for this version)

Submission history

From: Marcin Szyniszewski [view email]
[v1] Fri, 15 Mar 2024 23:16:44 GMT (814kb,D)
[v2] Sat, 6 Apr 2024 21:22:14 GMT (814kb,D)

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