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1. Air entrainment by breaking waves: AIR ENTRAINMENT BY BREAKING WAVES

   https://doi.org/10.1002/2017GL072883  

   Deike, Luc; Lenain, Luc; Melville, W. Kendall (April 2017, Geophysical Research Letters)
2. Entanglement-breaking superchannels

   https://doi.org/10.22331/q-2020-07-16-299  

   Chen, Senrui; Chitambar, Eric (July 2020, Quantum)

   In this paper we initiate the study of entanglement-breaking (EB) superchannels. These are processes that always yield separable maps when acting on one side of a bipartite completely positive (CP) map. EB superchannels are a generalization of the well-known EB channels. We give several equivalent characterizations of EB supermaps and superchannels. Unlike its channel counterpart, we find that not every EB superchannel can be implemented as a measure-and-prepare superchannel. We also demonstrate that many EB superchannels can be superactivated, in the sense that they can output non-separable channels when wired in series.We then introduce the notions of CPTP- and CP-complete images of a superchannel, which capture deterministic and probabilistic channel convertibility, respectively. This allows us to characterize the power of EB superchannels for generating CP maps in different scenarios, and it reveals some fundamental differences between channels and superchannels. Finally, we relax the definition of separable channels to include ( p , q ) -non-entangling channels, which are bipartite channels that cannot generate entanglement using p - and q -dimensional ancillary systems. By introducing and investigating k -EB maps, we construct examples of ( p , q ) -EB superchannels that are not fully entanglement breaking. Partial results on the characterization of ( p , q ) -EB superchannels are also provided. 

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3. Holographic entanglement negativity and replica symmetry breaking

   https://doi.org/10.1007/JHEP06(2021)024  

   Dong, Xi; Qi, Xiao-Liang; Walter, Michael (June 2021, Journal of High Energy Physics)

   A bstract Since the work of Ryu and Takayanagi, deep connections between quantum entanglement and spacetime geometry have been revealed. The negative eigenvalues of the partial transpose of a bipartite density operator is a useful diagnostic of entanglement. In this paper, we discuss the properties of the associated entanglement negativity and its Rényi generalizations in holographic duality. We first review the definition of the Rényi negativities, which contain the familiar logarithmic negativity as a special case. We then study these quantities in the random tensor network model and rigorously derive their large bond dimension asymptotics. Finally, we study entanglement negativity in holographic theories with a gravity dual, where we find that Rényi negativities are often dominated by bulk solutions that break the replica symmetry. From these replica symmetry breaking solutions, we derive general expressions for Rényi negativities and their special limits including the logarithmic negativity. In fixed-area states, these general expressions simplify dramatically and agree precisely with our results in the random tensor network model. This provides a concrete setting for further studying the implications of replica symmetry breaking in holography. 

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4. Symmetry-Breaking Charge Separation and Null Aggregates

   https://doi.org/10.1021/acs.jpcc.3c06785  

   Spano, Frank C. (January 2024, The Journal of Physical Chemistry C)
5. Inverse Problems, Deep Learning, and Symmetry Breaking

   Tayal, Kshitij; Lai, Chieh-Hsin; Manekar, Raunak; Kumar, Vipin; Sun, Ju (July 2020, ICML workshop on ML Interpretability for Scientific Discovery)

   In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such physical systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental difficulty in deploying the emerging end-to-end deep learning approach. Using the generalized phase retrieval problem as an illustrative example, we show that careful symmetry breaking on training data can help remove the difficulty and significantly improve the learning performance. We also extract and highlight the underlying mathematical principle of the proposed solution, which is directly applicable to other inverse problems. A full-length version of this paper can be found at https://arxiv.org/abs/2003.09077. 

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