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We investigate the collective non-Markovian dynamics of two fully excited two-level atoms coupled to a one-dimensional waveguide in the presence of delay. We demonstrate that analogous to the well-known superfluorescence phenomena, where an inverted atomic ensemble synchronizes to enhance its emission, there is a “subfluorescence” effect that synchronizes the atoms into an entangled dark state depending on the interatomic separation. The phenomenon can lead to a two-photon bound state in the continuum. Our results are pertinent to long-distance quantum networks, presenting a mechanism for spontaneous entanglement generation between distant quantum emitters. Published by the American Physical Society2024
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This content will become publicly available on December 1, 2025
Mpemba-Like Sensory Withdrawal Effect
Biological sensors rely on the temporal dynamics of ligand concentration for signaling. The sensory performance is bounded by the distinguishability between the sensory state transition dynamics under different environmental protocols. This work presents a comprehensive theory to characterize arbitrary transient sensory dynamics of biological sensors. Here the sensory performance is quantified by the Kullback-Leibler (KL) divergence between the probability distributions of the sensor's stochastic paths. We introduce a novel benchmark to assess a sensor's transient sensory performance arbitrarily far from equilibrium. We identify a counterintuitive phenomenon in multistate sensors: while an initial exposure to high ligand concentration may hinder a sensor's sensitivity towards a future concentration up-shift, certain sensors may show a boost in sensitivity if the initial high concentration exposure is followed by a transient resetting at a low concentration environment. The boosted performance exceeds that of a sensor starting from an initially low concentration environment. This effect, reminiscent of a drug withdrawal effect, can be explained by the Markovian dynamics of the multistate sensor, similar to the Markovian Mpemba effect. Moreover, an exhaustive machine learning study of four-state sensors reveals a tight connection between the sensor's performance and the structure of the Markovian graph of its states. Published by the American Physical Society2024
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- Award ID(s):
- 2145256
- PAR ID:
- 10575280
- Publisher / Repository:
- APS
- Date Published:
- Journal Name:
- PRX Life
- Volume:
- 2
- Issue:
- 4
- ISSN:
- 2835-8279
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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