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Sagnik Chatterjee

8 papers hereh-index 12847 citations46 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author1
  • last author1

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • quant-ph4
  • astro-ph.HE3
  • gr-qc1
same name
  • Sagnik Chatterjee — 8 papers, h 2
  • Sagnik Chatterjee — 5 papers, h 3
  • Sagnik Chatterjee — 3 papers, h 1
  • Sagnik Chatterjee — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202024
most citedAnalyzing the speed of sound in neutron star with machine learning

15 citations · 33 across the 8 of their papers we have counts for

collaborators
Showing quant-phShow all

4 papers · 1 filter

quant-ph2023

Quantum Solutions to the Privacy vs. Utility Tradeoff

Sagnik Chatterjee, Vyacheslav Kungurtsev

In this work, we propose a novel architecture (and several variants thereof) based on quantum cryptographic primitives with provable privacy and security guarantees regarding membe…

quant-ph2022

Efficient Quantum Agnostic Improper Learning of Decision Trees

Sagnik Chatterjee, Tharrmashastha SAPV, Debajyoti Bera

The agnostic setting is the hardest generalization of the PAC model since it is akin to learning with adversarial noise. In this paper, we give a poly$(n,t,{\frac{1}{\varepsilon}})…

quant-ph2021★ 1 cited

Quantum Boosting using Domain-Partitioning Hypotheses

Debajyoti Bera, Rohan Bhatia, Parmeet Singh Chani +1

Boosting is an ensemble learning method that converts a weak learner into a strong learner in the PAC learning framework. Freund and Schapire designed the Godel prize-winning algor…

quant-ph2020★ 1 cited

Applying the Quantum Alternating Operator Ansatz to the Graph Matching Problem

Sagnik Chatterjee, Debajyoti Bera

The Quantum Alternating Operator Ansatz (QAOA+) framework has recently gained attention due to its ability to solve discrete optimization problems on noisy intermediate-scale quant…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.