Srinivasan Arunachalam
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
14
Venues
8
Active years
2019–2026
Best venue rank
A*
Where they publish
Papers
14 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COLT | Learning depth-3 circuits via quantum agnostic boosting. | Srinivasan Arunachalam, Arkopal Dutt, Alexandru Gheorghiu, Michael de Oliveira |
| 2026 | STOC | Learning Stabilizer Structure of Quantum States. | Srinivasan Arunachalam, Arkopal Dutt |
| 2025 | STOC | Polynomial-Time Tolerant Testing Stabilizer States. | Srinivasan Arunachalam, Arkopal Dutt |
| 2025 | STOC | Testing and Learning Structured Quantum Hamiltonians. | Srinivasan Arunachalam, Arkopal Dutt, Francisco Escudero Gutirrez |
| 2025 | STACS | Generalized Inner Product Estimation with Limited Quantum Communication. | Srinivasan Arunachalam, Louis Schatzki |
| 2024 | ICALP | Learning Low-Degree Quantum Objects. | Srinivasan Arunachalam, Arkopal Dutt, Francisco Escudero Gutirrez, Carlos Palazuelos |
| 2022 | STOC | Positive spectrahedra: invariance principles and pseudorandom generators. | Srinivasan Arunachalam, Penghui Yao |
| 2021 | FOCS | Quantum learning algorithms imply circuit lower bounds. | Srinivasan Arunachalam, Alex B. Grilo, Tom Gur, Igor C. Oliveira, Aarthi Sundaram |
| 2021 | QCE | Simpler (Classical) and Faster (Quantum) Algorithms for Gibbs Partition Functions. | Srinivasan Arunachalam, Vojtech Havlcek, Giacomo Nannicini, Kristan Temme, Pawel Wocjan |
| 2020 | FOCS | Sample-efficient learning of quantum many-body systems. | Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara, Mehdi Soleimanifar |
| 2020 | ICML | Quantum Boosting. | Srinivasan Arunachalam, Reevu Maity |
| 2020 | STACS | Improved Bounds on Fourier Entropy and Min-Entropy. | Srinivasan Arunachalam, Sourav Chakraborty, Michal Kouck, Nitin Saurabh, Ronald de Wolf |
| 2019 | ICALP | Two New Results About Quantum Exact Learning. | Srinivasan Arunachalam, Sourav Chakraborty, Troy Lee, Manaswi Paraashar, Ronald de Wolf |
| 2019 | SODA | Optimizing quantum optimization algorithms via faster quantum gradient computation. | Andrs Gilyn, Srinivasan Arunachalam, Nathan Wiebe |