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The vision of building computational hardware for problem optimization has spurred large efforts in the physics community. In particular, networks of Kerr parametric oscillators (KPOs) are envisioned as simulators for finding the ground…

Strongly interacting spins underlie many intriguing phenomena and applications ranging from magnetism to quantum information processing. Interacting spins combined with motion display exotic spin transport phenomena, such as superfluidity…

The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism. Canonical examples of those states include various spin liquids and spin nematics. However, discerning their existence and…

Statistical Mechanics · Physics 2019-02-20 Jonas Greitemann , Ke Liu , Lode Pollet

Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…

Materials Science · Physics 2022-08-15 Michele Ceriotti

We formulate statistical-mechanical inverse methods in order to determine optimized interparticle interactions that spontaneously produce target many-particle configurations. Motivated by advances that give experimentalists greater and…

Soft Condensed Matter · Physics 2007-05-23 Mikael C. Rechtsman , Salvatore Torquato , Frank H. Stillinger

We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to the maximum margin structured prediction objective, and…

Machine Learning · Statistics 2019-02-11 Krishna Pillutla , Vincent Roulet , Sham M. Kakade , Zaid Harchaoui

We propose a state estimation scheme for spins, using a modified setup of the Stern-Gerlach experiment, in which a beam of neutral spin-1/2 point particles interacts with a quadrupolar magnetic field. The proposed linear inversion…

Quantum Physics · Physics 2021-04-07 Javier Martínez-Cifuentes , K. M. Fonseca-Romero

We present a protocol to selectively decouple, recouple, and engineer effective couplings in mesoscopic dipolar spin networks. In particular, we develop a versatile protocol that relies upon magic angle spinning to perform Hamiltonian…

Quantum Physics · Physics 2019-01-16 Ashok Ajoy , Ulf Bissbort , Dario Poletti , Paola Cappellaro

Spin squeezed states provide a seminal example of how the structure of quantum mechanical correlations can be controlled to produce metrologically useful entanglement. Such squeezed states have been demonstrated in a wide variety of…

Quantum sensors based on spin defect ensembles have seen rapid development in recent years, with a wide array of target applications. Historically, these sensors have used optical methods to prepare or read out quantum states. However,…

We propose a scheme to realize a pseudospin-$1/2$ model of the $^{1}\Sigma(v=0)$ bialkali polar molecules with the spin states corresponding to two sublevels of the first excited rotational level. We show that the effective dipole-dipole…

Quantum Gases · Physics 2015-09-30 Y. Deng , S. Yi

Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical…

Materials Science · Physics 2025-08-26 Antoine Loew , Jonathan Schmidt , Silvana Botti , Miguel A. L. Marques

Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from…

Materials Science · Physics 2022-08-05 Arun Hegde , Elan Weiss , Wolfgang Windl , Habib N. Najm , Cosmin Safta

Hamiltonian parameter estimation is crucial in condensed matter physics, but time and cost consuming in terms of resources used. With advances in observation techniques, high-resolution images with more detailed information are obtained,…

Disordered Systems and Neural Networks · Physics 2019-11-15 Dingchen Wang , Songrui Wei , Anran Yuan , Fanghua Tian , Kaiyan Cao , Qizhong Zhao , Dezhen Xue , Sen Yang

We present a new platform facilitating quantum logic control of polar molecular ions in a segmented ring ion trap, paving the way for precision measurements. This approach focuses on achieving near-unity state preparation and detection, as…

Atomic Physics · Physics 2023-07-27 Yan Zhou , Joshua O. Island , Matt Grau

Spatially nonhomogeneously spin polarized nuclei are proposed as a new mechanism to monitor electron states in a nanostructure, or as a means to createn and, if necessary, reshape such nanostructures in the course of the experiment. We…

Mesoscale and Nanoscale Physics · Physics 2015-06-24 V. Fleurov , V. A. Ivanov , F. M. Peeters , I. D. Vagner

Bipartite and global entanglement are analyzed for the ground state of a system of $N$ spin 1/2 particles interacting via a collective spin-spin coupling described by the Lipkin-Meshkov-Glick (LMG) Hamiltonian. Under certain conditions…

Quantum Physics · Physics 2007-05-23 R. G. Unanyan , C. Ionescu , M. Fleischhauer

A model of dynamic orientation using optimized radiofrequency (RF) irradiation produced perpendicular to the holding field is developed for the spin-1 system required for tensor-polarized fixed-target experiments. The derivation applies to…

Nuclear Theory · Physics 2017-07-25 Dustin Keller

Molecular spintronics is made possible by the coupling between electronic configuration and magnetic po- larization of the molecules. For control and application of the individual molecular states it is necessary to both read and write…

Mesoscale and Nanoscale Physics · Physics 2016-04-27 T. Saygun , J. Bylin , H. Hammar , J. Fransson

Most of the analytical studies on spin glasses are performed by using mean-field theory and renormalization group analysis. Analytical studies on finite-dimensional spin glasses are very challenging. In this short note, a possible exten-…

Disordered Systems and Neural Networks · Physics 2018-01-17 Masayuki Ohzeki , Yuta Kudo , Kazuyuki Tanaka
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