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🔖 Machine Learning Spatial Geometry from Entanglement Features

Yi-Zhuang You, Zhao Yang, Xiao-Liang Qi

🔗 https://arxiv.org/pdf/1709.01223

📌 ABSTRACT
Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).
⚡️ Modern physics provides a precise language to capture the way things #scale: the so-called #renormalization_group. This mathematical formalism allows us to go systematically from the small to the large. The essential step is taking averages. For example, instead of looking at the behavior of individual atoms that make up matter, we can take little cubes, say 10 atoms wide on each side, and take these cubes as our new building blocks. One can then repeat this averaging procedure.

#Renormalization theory describes in detail how the properties of a physical system change if one increases the length scale on which the observations are made. A famous example is the electric charge of particles that can increase or decrease depending on quantum interactions. A sociological example is understanding the behavior of groups of various sizes starting from individual behavior. Is there wisdom in crowds, or do the masses behave less responsibly?

Most interesting are the two endpoints of the renormalization process: the infinite large and infinite small. Here things will typically simplify because either all details are washed away, or the environment disappears. Both the largest and the smallest structures of the universe are astonishingly #simple. It is here that we find the two “standard models,” of #particle_physics and #cosmology.

🔗 https://www.quantamagazine.org/to-solve-the-biggest-mystery-in-physics-join-two-kinds-of-law-20170907/
Geoffrey West on Computational Biology, Complexity, and a Unified…
Demetri Kofinas
🔊 Geoffrey West on Computational Biology, Complexity, and a Unified Theory of Sustainability

🔗 goo.gl/Whi7J8
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University of British Columbia Murphy lab: PDF and Graduate students wanted, automated in vivo imaging and optogenetics in mouse cortex. http://www.neuroscience.ubc.ca/faculty/murphy_jobs.html
🤷‍♂ Summary

Nonlinear dynamics--or chaos theory, as it is commonly called--has been studied for more than a century. But as Stark and Hardy explain in their Perspective, chaos has only recently become useful in applications such as microwave ovens, production lines, and biomedicine. The authors chart the history of nonlinear dynamics from the 1960s and argue that the recent progress with practical problems is due to a sea change in the field that led to a synergy between hypothesis-driven and data-driven approaches.


http://science.sciencemag.org/content/301/5637/1192