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1. Comparison of evolving interfaces, triple points, and quadruple points for discrete and diffuse interface methods

   https://doi.org/10.1016/j.commatsci.2022.111632  

   Eren, Erdem ; Runnels, Brandon ; Mason, Jeremy ( October 2022 , Computational Materials Science)
2. The S-integral points on the projective line minus three points via finite covers and Skolem's method

   https://doi.org/10.1007/978-3-030-80914-0_21  

   Poonen, Bjorn ( January 2021 , Arithmetic Geometry, Number Theory, and Computation)

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   Krattenthaler, Christian ; Thibon, Jean-Yves (Ed.)
4. Detecting Quantum Critical Points of Correlated Systems by Quantum Convolutional Neural Network Using Data from Variational Quantum Eigensolver

   https://doi.org/10.3390/quantum4040042  

   Wrobel, Nathaniel ; Baul, Anshumitra ; Tam, Ka-Ming ; Moreno, Juana ( December 2022 , Quantum Reports)

   Machine learning has been applied to a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems, for classifying phase transitions. The recently proposed quantum convolutional neural network (QCNN) provides a new framework for using quantum circuits instead of classical neural networks as the backbone of classification methods. We present the results from training the QCNN by the wavefunctions of the variational quantum eigensolver for the one-dimensional transverse field Ising model (TFIM). We demonstrate that the QCNN identifies wavefunctions corresponding to the paramagnetic and ferromagnetic phases of the TFIM with reasonable accuracy. The QCNN can be trained to predict the corresponding ‘phase’ of wavefunctions around the putative quantum critical point even though it is trained by wavefunctions far away. The paper provides a basis for exploiting the QCNN to identify the quantum critical point. 

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5. Effect of dipolar interaction on exceptional points in synthetic layered magnets

   https://doi.org/10.1063/5.0049011  

   Jeffrey, T. ; Zhang, W. ; Sklenar, J. ( May 2021 , Applied Physics Letters)