A high-bias, low-variance introduction to machine learning for physicists
Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …
research and application. The purpose of this review is to provide an introduction to the core …
Drawing phase diagrams of random quantum systems by deep learning the wave functions
T Ohtsuki, T Mano - Journal of the Physical Society of Japan, 2020 - journals.jps.jp
Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …
beginning to be well accepted. Obtaining and representing the ground and excited state …
Hybrid convolutional neural network and projected entangled pair states wave functions for quantum many-particle states
Neural networks have been used as variational wave functions for quantum many-particle
problems. It has been shown that the correct sign structure is crucial to obtain highly …
problems. It has been shown that the correct sign structure is crucial to obtain highly …
Interpreting machine learning of topological quantum phase transitions
There has been growing excitement over the possibility of employing artificial neural
networks (ANNs) to gain new theoretical insight into the physics of quantum many-body …
networks (ANNs) to gain new theoretical insight into the physics of quantum many-body …
Theoretical calculations for isotope shifts of ions
XQ Qi, PP Zhang, ZC Yan, GWF Drake, AX Chen… - Physical Review A, 2024 - APS
Standard perturbation theory in quantum mechanics is employed to calculate the mass shifts
of 2 S 0 1− 2 S 1 3 and 2 S 1 3− 2 PJ 3 transitions in Be 2+ 7, 9, 10, 11, 12, 14 ions. These …
of 2 S 0 1− 2 S 1 3 and 2 S 1 3− 2 PJ 3 transitions in Be 2+ 7, 9, 10, 11, 12, 14 ions. These …
Networks for nonlinear diffusion problems in imaging
S Arridge, A Hauptmann - Journal of Mathematical Imaging and Vision, 2020 - Springer
A multitude of imaging and vision tasks have seen recently a major transformation by deep
learning methods and in particular by the application of convolutional neural networks …
learning methods and in particular by the application of convolutional neural networks …
Preparing quantum states by measurement-feedback control with Bayesian optimization
The preparation of quantum states is crucial for enabling quantum computations and
simulations. In this work, we present a general framework for preparing ground states of …
simulations. In this work, we present a general framework for preparing ground states of …
A quantum Hopfield neural network model and image recognition
G Liu, WP Ma, H Cao, LD Lyu - Laser Physics Letters, 2020 - iopscience.iop.org
Quantum computing is a new mode that follows the laws of quantum mechanics. It performs
computational tasks based on the control of quantum units. From the view of computable …
computational tasks based on the control of quantum units. From the view of computable …
Hyperfine-induced effects on angular emission of the magnetic-quadrupole line following electron-impact excitation of ions
ZW Wu, ZQ Tian, J Jiang, CZ Dong, S Fritzsche - Physical Review A, 2020 - APS
The electron-impact excitation from the ground state to the excited 1 s 2 p 3/2 P 2 3 energy
level and the subsequent magnetic-quadrupole radiative decay 1 s 2 p 3/2 P 2 3→ 1 s 2 S 0 …
level and the subsequent magnetic-quadrupole radiative decay 1 s 2 p 3/2 P 2 3→ 1 s 2 S 0 …
Effect of the Breit interaction on inner-shell electron-impact excitation and subsequent radiative decay of highly charged berylliumlike ions
ZW Wu, MM Zhao, C Ren, CZ Dong, J Jiang - Physical Review A, 2020 - APS
The inner-shell electron-impact excitation from the ground state to the 1 s 2 s 2 2 p 1/2 J f= 1
excited state and the subsequent electric-dipole 1 s 2 s 2 2 p 1/2 J f= 1→ 1 s 2 2 s 2 J i= 0 …
excited state and the subsequent electric-dipole 1 s 2 s 2 2 p 1/2 J f= 1→ 1 s 2 2 s 2 J i= 0 …