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arXiv 2603.15744quant-phcond-mat.dis-nncond-mat.stat-mechcond-mat.str-el

测量诱导相变中的后选临界性

Post-selected Criticality in Measurement-induced Phase Transitions

Louisiana State University(路易斯安那州立大学);University of Geneva(日内瓦大学);University of Massachusetts Amherst(马萨诸塞大学阿默斯特分校);City College of New York, City University of New York(纽约市立学院,纽约市立大学);The Pennsylvania State University(宾夕法尼亚州立大学);University College Dublin(都柏林大学学院)

Dolly Nambi, Kabir Khanna, Andrew Allocca, Thomas Iadecola, Ciarán Hickey, Romain Vasseur, Justin H. Wilson

AI总结:

研究通过后选测量改变测量诱导相变的临界性质,发现临界指数和有效中央电荷的变化,以及与随机张量网络纠缠相变的对比。

摘要:

信息论相变,如测量诱导相变(MIPT),表征量子动力学对局部监测的鲁棒性,并自然地以典型测量结果条件化的轨迹来表述,这些轨迹通常只能通过后选来访问。本文通过强制测量来研究显式后选如何改变相变的性质。我们发现后选通过重新加权否则罕见的轨迹从根本上改变了普遍性类别。特别是,我们得到了关联长度指数ν≈2.1,大于标准MIPT的值,并且有效中央电荷c_eff≈-0.4为负数。我们还比较了后选MIPT与随机张量网络(RTN)的纠缠相变,并证明它们的普遍性类别相同。此设置进一步允许具有后选弱测量的时间周期性、平移不变电路。在两种模型中,我们发现至少需要 onsite 维度为3(三态但不是双态)才能诱导相变。

英文摘要:

Information-theoretic phase transitions, such as the measurement-induced phase transition (MIPT), characterize the robustness of quantum dynamics to local monitoring and are naturally formulated in terms of trajectories conditioned on typical measurement outcomes, which are naively accessible only through postselection. Here, we implement forced measurements to investigate how explicit postselection alters the nature of the transition. We find that postselection fundamentally alters the universality class by reweighting trajectories that are otherwise rare. In particular, we obtain a correlation-length exponent $ν\approx 2.1$ larger than that of the standard MIPT and a negative effective central charge $c_\mathrm{eff}\approx -0.35$. We also compare the postselected MIPT to the entanglement transition of random tensor networks, and demonstrate that their universality class is the same. This setup further allows time-periodic, translationally invariant circuits with postselected weak measurements. In both models, we find that an on-site dimension of at least 3 (qutrits but not qubits) is necessary to induce a transition.

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。

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