Related papers: Machine learning as an improved estimator for magn…
Magnetization curves of the ferrimagnetic mixed spin-(1/2,S) Heisenberg chains are calculated with the help of density-matrix renormalization group method for several quantum spin numbers S=1, 3/2, 2 and 5/2. It is shown that the…
In this paper, we applied a deep neural network to study the issue of knowledge transferability between statistical mechanics models. The following computer experiment was conducted. A convolutional neural network was trained to solve the…
Can a machine learn Machine Learning? This work trains a machine learning model to solve machine learning problems from a University undergraduate level course. We generate a new training set of questions and answers consisting of course…
Nowadays, methods and techniques of Machine Learning and Deep Learning are being used in various scientific areas. They help to automatize calculations without losing in quality. In this paper the applying of convolutional neural network…
The heat capacity $\mathcal{C}$ of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, $\mathcal{C}$ is limited by a quadratic scaling with the number of…
A method for estimation of reversible and irreversible susceptibilities of initial magnetization curves has been developed. It deals only with the energy necessary for magnetizing and demagnetizing the sample, not with the nature of the…
We propose an all optical ultrafast method to highly magnetize general quantum magnets using a circularly polarized terahertz laser. The key idea is to utilize a circularly polarized laser and its chirping. Through this method, one can…
We present an experimental demonstration of closed-loop quantum parameter estimation in which real-time feedback is used to achieve robustness to modeling uncertainty. By performing broadband estimation of a magnetic field acting on…
In this work we analyze the magnetization properties of an antiferromagnetic Kagom\'e stripe lattice, motivated by the recent synthesis of materials exhibiting this structure. By employing a variety of techniques that include numerical…
We utilise the graphon--a continuous mathematical object which represents the limit of convergent sequences of dense graphs--to formulate a general, continuous description of quantum spin systems in thermal equilibrium when the average…
We study the occurrence of plateaux and jumps in the magnetization curves of a class of frustrated ladders for which the Hamiltonian can be written in terms of the total spin of a rung. We argue on the basis of exact diagonalization of…
Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality candidate materials are known. In this work, we…
Due to a ferromagnetic in-chain coupling between Co$^{3+}$ ions at trigonal sites, chains Co$_2$O$_6$ are considered as large rigid spin moments. The antiferromagnetic Ising model on the triangular lattice is applied to describe an…
In this paper, we consider some frustrated spin models for which the ground states are known exactly. The concurrence, a measure of the amount of entanglement can be calculated exactly for entangled spin pairs. Quantum phase transitions…
The magnetization process of the $S=1/2$ distorted diamond spin chain with ferromagnetic interactions is investigated using the numerical diagonalization of finite-size clusters. The level spectroscopy analysis applied for the model with…
We investigate the efficient learning of magnetic phases using artificial neural networks trained on synthetic data, combining computational simplicity with physics-informed strategies. Focusing on the diluted Ising model, which lacks an…
Accessing the thermodynamic-limit properties of strongly correlated quantum matter requires simulations on very large lattices, a regime that remains challenging for numerical methods, especially in frustrated two-dimensional systems. We…
We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors. We propose a Deep Learning framework to resolve the differences between the true dynamics of the…
Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged…
We present an algorithm for finding ground states of two dimensional spin glass systems based on ideas from matrix product states in quantum information theory. The algorithm works directly at zero temperature and defines an approximate…