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The densest state of matter in the universe is uniquely realized inside central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the longstanding problems in nuclear…

Nuclear Theory · Physics 2020-03-18 Yuki Fujimoto , Kenji Fukushima , Koichi Murase

It is demonstrated that the candidate tetraquark states $Z_b(10610)$, $Z_b(10650)$, $Z_c(3900)$, and $Z_c(4025)$ are coupled channel cusp effects. The model explains in a natural way the masses and quantum numbers of the putative states and…

High Energy Physics - Phenomenology · Physics 2015-03-05 E. S. Swanson

Nonclassicality, defined in the quantum optical sense, serves as a resource for photon-based quantum technologies. Therefore, certifying the nonclassicality of a quantum state is crucial for gauging its potential for quantum advantage.…

In this article, we systematically discuss the decay patterns of the hidden-strange hadronic molecular state $N(2270)$ which is assumed as an S-wave $K^\ast\Sigma^\ast$ shallow bound state with its possible quantum numbers $J^P$ which are…

High Energy Physics - Phenomenology · Physics 2025-09-11 Di Ben , Shu-Ming Wu

Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signals from NMR spectroscopy often have low signal-to-noise…

Signal Processing · Electrical Eng. & Systems 2024-05-21 Zihao Zou , Shirin Shoushtari , Jiaming Liu , Jialiang Zhang , Patrick Judge , Emilia Santana , Alison Lim , Marcus Foston , Ulugbek S. Kamilov

Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in quantum technologies. One such method, known as Separable Neural Network Quantum States (SNNS), employs…

Quantum Physics · Physics 2021-06-15 Cillian Harney , Mauro Paternostro , Stefano Pirandola

Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable…

Machine Learning · Computer Science 2021-08-31 Daniel Schwalbe-Koda , Aik Rui Tan , Rafael Gómez-Bombarelli

We study heavy hadron spectroscopy near open bottom thresholds. We employ B and B* mesons as effective degrees of freedom near the thresholds, and consider meson exchange potentials between them. All possible composite states which can be…

High Energy Physics - Phenomenology · Physics 2012-07-18 Shunsuke Ohkoda , Yasuhiro Yamaguchi , Shigehiro Yasui , Kazutaka Sudoh , Atsushi Hosaka

The general approach taken when training deep learning classifiers is to save the parameters after every few iterations, train until either a human observer or a simple metric-based heuristic decides the network isn't learning anymore, and…

Machine Learning · Computer Science 2021-11-17 J. K. Terry , Mario Jayakumar , Kusal De Alwis

Without any means of interpretation, neural networks that predict molecular properties and bioactivities are merely black boxes. We will unravel these black boxes and will demonstrate approaches to understand the learned representations…

Machine Learning · Computer Science 2019-03-19 Kristina Preuer , Günter Klambauer , Friedrich Rippmann , Sepp Hochreiter , Thomas Unterthiner

The discovery of hadronic states beyond the conventional two-quark meson and three-quark baryon picture in the last two decades is one of the most amazing accomplishments in fundamental physics research. We review the experimental progress…

High Energy Physics - Experiment · Physics 2023-08-08 Chang-Zheng Yuan

As neural networks are known to efficiently represent classes of tensor-network states as well as volume-law-entangled states, identifying which properties determine the representational capabilities of neural quantum states (NQS) remains…

Nuclear Theory · Physics 2026-03-31 James W. T. Keeble , Alessandro Lovato , Caroline E. P. Robin

The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck. Methods of machine learning have been used in adjacent fields for effective feature…

Disordered Systems and Neural Networks · Physics 2020-08-10 A Berezutskii , M Beketov , D Yudin , Z Zimborás , J Biamonte

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn…

Computer Vision and Pattern Recognition · Computer Science 2014-02-20 Christian Szegedy , Wojciech Zaremba , Ilya Sutskever , Joan Bruna , Dumitru Erhan , Ian Goodfellow , Rob Fergus

Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state…

Quantum Physics · Physics 2025-05-08 Manuel Hagelüken , Marco F. Huber , Marco Roth

The bound state problem of $D^0\bar{D}^{\ast0}$ ($\bar{D}^0{D}^{\ast0}$) is relevant to the molecular interpretation of the X(3872). We investigated this problem in a chiral quark model by solving the resonating group method equation. We…

High Energy Physics - Phenomenology · Physics 2009-05-07 Yan-Rui Liu , Zong-Ye Zhang

This paper investigates the learnability of the nonlinearity property of Boolean functions using neural networks. We train encoder style deep neural networks to learn to predict the nonlinearity of Boolean functions from examples of…

Machine Learning · Computer Science 2025-02-04 Sriram Ranga , Nandish Chattopadhyay , Anupam Chattopadhyay

After Y(4630) is discovered, theorists have given various explanations. We find that if Y(4630) is interpreted as the D-wave resonant state of $\Lambda_c \bar {\Lambda}_c$ system, the particle mass, decay width and all quantum numbers are…

High Energy Physics - Phenomenology · Physics 2023-02-22 Xiao-Hui Mei , Zhuo Yu , Mao Song , Jian-You Guo , Gang Li , Xuan Luo

The identification of nonclassical features of multiphoton quantum states represents a task of the utmost importance in the development of many quantum photonic technologies. Under realistic experimental conditions, a photonic quantum state…

We report on further evidence that the Y(3940) and the recently observed Y(4140) are heavy hadron molecule states with quantum numbers J(PC) = 0(++). The Y(3940) state is considered to be a superposition of D*(+)D*(-) and D*(0) barD*(0),…

High Energy Physics - Phenomenology · Physics 2009-09-23 Tanja Branz , Thomas Gutsche , Valery E. Lyubovitskij
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