Academic Articles & Research Papers

Quantum Computing in the NISQ era and beyond

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in q...

Statistical Mechanics of Neural Networks

A neural network is a large, highly interconnected assembly of simple elements. The elements, called neurons, are usually two-state devices that switch from one state to the other when their input exceeds a specific threshold value. In this...

Memristor devices for neural networks

Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...

Recurrent Neural Networks for Prediction

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...

Quantum convolutional neural networks

Neural Networks: A Review from a Statistical Perspective

This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and encourages cross-disciplinary research in the directions most likely to bear fruit. The areas...

Lagrangian Neural Networks

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the...

Quantum Entanglement in Neural Network States

Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...

Maximum Storage Capacity in Neural Networks

The upper storage capacity of a neural network for patterns of fixed magnetization m is calculated. The optimal capacity increases with the correlation m 2 between the patterns....

Chaotic neural networks

Discovering Physical Concepts with Neural Networks

Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture afte...

Pattern Recognition and Neural Networks

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously...

Introduction to Neural Networks.

1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks....

Hamiltonian Neural Networks

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models th...

Galactic outflows and star-formation quenching: a JWST perspective.

On the Dual Nature of Atmospheric Escape

Planetary atmospheres cannot remain hydrostatic at all altitudes because they approach finite density at infinite radius, implying infinite mass. Classical treatments address this in two directions: either retain a hydrostatic structure whi...

The Messy Nature of Fiber Spectra: Star–Quasar Pairs Masquerading as Dual Type 1 AGNs

Theoretical studies predict that the most significant growth of supermassive black holes (SMBHs) occurs in late-stage mergers, coinciding with the manifestation of dual active galactic nuclei (AGNs), and both major and minor mergers are exp...

Advances and perspectives for receiver function imaging of the Earth's internal discontinuities and velocity structures

Studying the structures, properties and origins of the Earth's internal discontinuities is an important part in the efforts to understand the physical and chemical properties of the layered Earth, as well as to explore the dynamic processes...

Electroweak monopoles and the electroweak phase transition

Abstract We consider an isolated electroweak monopole solution within the Standard Model with a nonlinear Born–Infeld extension of the hypercharge gauge field. Monopole (and dyon) solutions in such an extension are regular and their masses ...

Multiwavelength Study of Five Distant Transient Gamma-Ray AGNs with z > 1

The multiwavelength emissions, especially gamma-rays, of active galactic nuclei (AGNs) are essential for studying the physical properties of jets emanating from supermassive black holes at galaxy centers. However, for high-redshift AGNs, it...