Quantum convolutional neural networks
https://doi.org/10.1038/s41567-019-0648-8
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https://doi.org/10.1038/s41567-019-0648-8
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...
https://doi.org/10.48550/arxiv.2003.04630
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...
https://doi.org/10.1103/physrevx.7.021021
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....
https://doi.org/10.1209/0295-5075/4/4/016
Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glas...
https://doi.org/10.1103/physreva.32.1007
https://doi.org/10.1016/0375-9601(90)90136-c
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...
https://doi.org/10.1103/physrevlett.124.010508
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....
https://doi.org/10.21236/ada248258
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...
https://doi.org/10.48550/arxiv.1906.01563
https://doi.org/10.1016/0166-1280(94)80078-2
From the Book: Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...
We present the first X-ray spectropolarimetric results for Cygnus X-1 in its soft state from a campaign of five IXPE observations conducted during 2023 May–June. Companion multiwavelength data during the campaign are likewise shown. The 2–8...
10.3847/2041-8213/ad58e4
The Five-hundred-meter Aperture Spherical Telescope (FAST) is the world’s largest single-dish radio telescope, and the search for extraterrestrial intelligence (SETI) is one of its five key science objectives. We conducted a targeted search...
10.3847/1538-3881/ae8516
This unique book provides a clear and lucid description of several aspects of astrophysics and cosmology in a language understandable to a physicist or beginner in astrophysics. It presents the key topics in all branches of astrophysics and...
https://doi.org/10.5860/choice.44-2679
Nuclear astrophysics is that branch of astrophysics which helps understanding some of the many facets of the Universe through the knowledge of the microcosm of the atomic nucleus. In the last decades much advance has been made in nuclear as...
https://doi.org/10.1088/0034-4885/62/3/003
https://doi.org/10.5860/choice.27-3349
In this book, a distinguished expert introduces plasma physics from the ground up, presenting it as a comprehensible field that can be grasped largely on the basis of physical intuition and qualitative reasoning, similar to other fields of ...
https://doi.org/10.1515/9780691213354
https://doi.org/10.1007/978-3-319-08051-2
Partial list of topics 1. Planetary geology 2. Geophysics 3. Planetary interiors 4. Planetary atmospheres 5. Pre-main sequence stars 6. Solar physics 7. Solar activity 8. Galaxies (types, structure, chemical evolution) 9. Cosmology and grav...
https://doi.org/10.5860/choice.29-3632