Zobrazeno 1 - 10
of 191
pro vyhledávání: '"Sengupta Anirvan M"'
Autor:
Kaplan, Daniel, Zhang, Adam, Blawat, Joanna, Jin, Rongying, Cava, Robert J., Oudovenko, Viktor, Kotliar, Gabriel, Sengupta, Anirvan M., Xie, Weiwei
The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expe
Externí odkaz:
http://arxiv.org/abs/2412.13012
Autor:
Simard, Olivier, Dawid, Anna, Tindall, Joseph, Ferrero, Michel, Sengupta, Anirvan M., Georges, Antoine
Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To fulfill their prospects, quantum simulators must be fully controllabl
Externí odkaz:
http://arxiv.org/abs/2412.12019
Many-body systems which saturate the quantum bound on chaos are attracting interest across a wide range of fields. Notable examples include the Sachdev-Ye-Kitaev model and its variations, all characterised by some form or randomness and all to all co
Externí odkaz:
http://arxiv.org/abs/2407.13617
Autor:
Zang, Jiawei, Medvidović, Matija, Kiese, Dominik, Di Sante, Domenico, Sengupta, Anirvan M., Millis, Andrew J.
Characterizing complex many-body phases of matter has been a central question in quantum physics for decades. Numerical methods built around approximations of the renormalization group (RG) flow equations have offered reliable and systematically impr
Externí odkaz:
http://arxiv.org/abs/2403.15372
Autor:
Moghadas, Emin, Dräger, Nikolaus, Toschi, Alessandro, Zang, Jiawei, Medvidović, Matija, Kiese, Dominik, Millis, Andrew J., Sengupta, Anirvan M., Andergassen, Sabine, Di Sante, Domenico
Publikováno v:
Eur. Phys. J. Plus 139, 700 (2024)
Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly exciting breakthroughs are enabling the computation of non-zero temperature
Externí odkaz:
http://arxiv.org/abs/2402.13030
Learning dynamics from repeated observation of the time evolution of an open quantum system, namely, the problem of quantum process tomography is an important task. This task is difficult in general, but, with some additional constraints could be tra
Externí odkaz:
http://arxiv.org/abs/2309.12631
A normative approach called Similarity Matching was recently introduced for deriving and understanding the algorithmic basis of neural computation focused on unsupervised problems. It involves deriving algorithms from computational objectives and eva
Externí odkaz:
http://arxiv.org/abs/2309.16687
Autor:
Bahroun, Yanis, Sridharan, Shagesh, Acharya, Atithi, Chklovskii, Dmitri B., Sengupta, Anirvan M.
While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result, there has been a growing interest in developing alternative biological
Externí odkaz:
http://arxiv.org/abs/2308.02427
Autor:
Lipshutz, David, Bahroun, Yanis, Golkar, Siavash, Sengupta, Anirvan M., Chklovskii, Dmitri B.
An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate their compatibility with anatomical and physiological observations. S
Externí odkaz:
http://arxiv.org/abs/2302.10051
Autor:
Golkar, Siavash, Tesileanu, Tiberiu, Bahroun, Yanis, Sengupta, Anirvan M., Chklovskii, Dmitri B.
Predictive coding has emerged as an influential normative model of neural computation, with numerous extensions and applications. As such, much effort has been put into mapping PC faithfully onto the cortex, but there are issues that remain unresolve
Externí odkaz:
http://arxiv.org/abs/2210.15752