Sathyawageeswar Subramanian
Sathyawageeswar Subramanian
1851 Research Fellow, University of Cambridge
Verified email at - Homepage
Cited by
Cited by
GRMHD formulation of highly super-Chandrasekhar rotating magnetized white dwarfs: stable configurations of non-spherical white dwarfs
S Subramanian, B Mukhopadhyay
Monthly Notices of the Royal Astronomical Society 454 (1), 752-765, 2015
Quantum algorithm for estimating -Renyi entropies of quantum states
S Subramanian, MH Hsieh
Physical Review A 104 (2), 022428, 2021
Implementing smooth functions of a Hermitian matrix on a quantum computer
S Subramanian, S Brierley, R Jozsa
Journal of Physics Communications 3 (6), 065002, 2019
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
H Yamasaki, S Subramanian, S Sonoda, M Koashi
Advances in Neural Information Processing Systems 33, 2020
Sublinear quantum algorithms for estimating von Neumann entropy
T Gur, MH Hsieh, S Subramanian
QIP 2022 (25th Annual Conference on Quantum Information Processing), 2022
A quantum search decoder for natural language processing
J Bausch, S Subramanian, S Piddock
Quantum Machine Intelligence 3 (1), 16, 2021
Significantly super-Chandrasekhar limiting mass white dwarfs and their consequences
B Mukhopadhyay, U Das, AR Rao, S Subramanian, M Bhattacharya, ...
arXiv preprint arXiv:1611.00133, 2016
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
H Yamasaki, S Subramanian, S Hayakawa, S Sonoda
ICML 2023 (Proceedings of the 40th International Conference on Machineá…, 2023
Do black holes store negative entropy?
K Azuma, S Subramanian
arXiv preprint arXiv:1807.06753, 2018
A remark on the quantum complexity of the Kronecker coefficients
C Ikenmeyer, S Subramanian
arXiv preprint arXiv:2307.02389, 2023
Spectral sparsification of matrix inputs as a preprocessing step for quantum algorithms
S Herbert, S Subramanian
arXiv preprint arXiv:1910.02861, 2019
Information-theoretic generalization bounds for learning from quantum data
M Caro, T Gur, C RouzÚ, DS Franša, S Subramanian
arXiv preprint arXiv:2311.05529, 2023
Constant-time one-shot testing of large-scale graph states
H Yamasaki, S Subramanian
arXiv preprint arXiv:2201.11127, 2022
Quantum Worst-Case to Average-Case Reductions for All Linear Problems
VR Asadi, A Golovnev, T Gur, I Shinkar, S Subramanian
QIP 2023 (26th Annual Conference on Quantum Information Processing), 2023
Quantum Algorithms for Matrix Problems and Machine Learning
S Subramanian
University of Cambridge, 2021
Hybrid quantum-classical algorithm for variational coupled cluster method
S Subramanian, Y Cao
APS March Meeting Abstracts 2019, A27. 002, 2019
Highly magnetized white dwarf as a possible alternate to neutron star to resolve shortcoming of magnetar model
B Mukhopadhyay, AR Rao, U Das, S Subramanian, M Bhattacharya
41st COSPAR Scientific Assembly 41, E1. 4-14-16, 2016
Quantum Computation (L16)
S Subramanian
Fast quantum algorithm for data approximation by optimized random features
H Yamasaki, S Subramanian, S Sonoda, M Koashi
IEICE Technical Report; IEICE Tech. Rep., 0
Black holes and negative information
K Azuma, S Subramanian
IEICE Technical Report; IEICE Tech. Rep., 0
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