Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Shree Hari Sureshbabu"'
Autor:
Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, Marco Pistoia
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Variational quantum algorithms, a popular heuristic for near-term quantum computers, utilize parameterized quantum circuits which naturally express Lie groups. It has been postulated that many properties of variational quantum algorithms can
Externí odkaz:
https://doaj.org/article/17c5158ee3ad48f2b763408f160d3c03
Autor:
Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, Marco Pistoia
Publikováno v:
Quantum, Vol 8, p 1231 (2024)
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challe
Externí odkaz:
https://doaj.org/article/1fa6ad77859848a5b8b7b9f577525d51
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
The critical point and the critical exponents for a phase transition can be determined using the Finite-Size Scaling (FSS) analysis. This method assumes that the phase transition occurs only in the infinite size limit. However, there has been a lot o
Externí odkaz:
https://doaj.org/article/fc55e98481ec44f7b28d3e8f62eec9a9
Publikováno v:
Journal of the American Chemical Society. 143:18426-18445
Quantum machine-learning algorithms have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. Such algorithms have been developed to solve problems like electronic structure calculatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d50947110b6ff19ba72ae85e690b05a
https://doi.org/10.1021/jacs.1c06246
https://doi.org/10.1021/jacs.1c06246
Autor:
Manas Sajjan, Junxu Li, Raja Selvarajan, Shree Hari Sureshbabu, Sumit Suresh Kale, Rishabh Gupta, Vinit Singh, Sabre Kais
Publikováno v:
Chemical Society reviews. 51(15)
Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9195249b15c03daddaeec87cc8e4938
https://pubmed.ncbi.nlm.nih.gov/35849066
https://pubmed.ncbi.nlm.nih.gov/35849066
Dissertation/ Thesis
Optimization and Machine learning (ML) have emerged as two positively disruptive methodologies and have thus resulted in unprecedented applications in several domains of technology. In recent years, ML has forayed into physical sciences and provided
Quantum machine learning algorithms, the extensions of machine learning to quantum regimes, are believed to be more powerful as they leverage the power of quantum properties. Quantum machine learning methods have been employed to solve quantum many-b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74f3c4bd79af4afe3d18abc8f58c424e
http://arxiv.org/abs/2103.02037
http://arxiv.org/abs/2103.02037
Autor:
Prasad Sarangapani, Roberto Grassi, Shree Hari Sureshbabu, Junzhe Geng, Tillmann Kubis, Mark Townsend, Kuang-Chung Wang, Yuanchen Chu, Xinchen Guo
Publikováno v:
Journal of Applied Physics. 128:014302
State-of-the-art industrial semiconductor device modeling is based on highly efficient Drift-Diffusion (DD) models that include some quantum corrections for nanodevices. In contrast, latest academic quantum transport models are based on the non-equil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d7a95707d13e09aa58e43fd62fd3ec5
https://doi.org/10.1063/1.5143833
https://doi.org/10.1063/1.5143833