2015
DOI: 10.48550/arxiv.1507.02041
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Quantum Intermittency for Sparse CMV Matrices with an Application to Quantum Walks on the Half-Line

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Cited by 3 publications
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“…Additionally, the process of averaging can transform dynamical quantities in particularly pleasant ways. For example, the exponential averages considered in this paper recast the a's into averages of the Poisson kernel against spectral measures; this point of view is explored in more detail in [9]. To be completely proper, the normalizing factor in (2.2) should really be 1 − e −2/K so that a is also a probability distribution on Z.…”
Section: Precise Statementsmentioning
confidence: 99%
“…Additionally, the process of averaging can transform dynamical quantities in particularly pleasant ways. For example, the exponential averages considered in this paper recast the a's into averages of the Poisson kernel against spectral measures; this point of view is explored in more detail in [9]. To be completely proper, the normalizing factor in (2.2) should really be 1 − e −2/K so that a is also a probability distribution on Z.…”
Section: Precise Statementsmentioning
confidence: 99%
“…One can use spectral methods to deduce bounds on spreading [31,32]. On the other hand, estimates of a dynamical nature can be turned into estimates on spectral quantities, for example, quantitative regularity of the spectral measures [30].…”
Section: Introductionmentioning
confidence: 99%
“…Extended CMV matrices arise as a natural playground for spectral theoretic techniques, as they furnish canonical unitary analogs of Schrödinger operators and Jacobi matrices. Moreover, they are interesting in their own right, since they naturally arise in connection with quantum walks in one dimension [3,6,7,8], the classical Ising model [9,16], and orthogonal polynomials on the unit circle [36,37]. More specifically, given a sequence α = (α n ) n∈Z with α n ∈ D = {z ∈ C : |z| < 1}, the corresponding extended CMV matrix is given by E = LM, where…”
Section: Introductionmentioning
confidence: 99%