Arkopal Dutt

arkopal [at] ibm [dot] com

I am currently a research scientist at IBM Research, Cambridge, MA. My research interests are primarily in quantum learning theory and quantum algorithms. Most of my recent research has focused on revealing stabilizer structure in quantum states using tools from additive combinatorics and quantum algorithms for differential equations.

I completed my PhD at MIT in the Quanta Lab at the Research Laboratory of Electronics, advised by Isaac Chuang. In the past, I was an undergraduate at IIT Bombay, and I have interned at IBM, Los Alamos National Laboratory, and Space Science Engineering Center.

RESUME

selected publications

Tomography of quantum states with bounded extent

Srinivasan Arunachalam, and Arkopal Dutt

2026

arXiv Bib

@article{ad2026tomoextent,
  title = {Tomography of quantum states with bounded extent},
  author = {Arunachalam, Srinivasan and Dutt, Arkopal},
  year = {2026},
  dcat = preprint,
  selected = true
}

Simulating dynamics of RLC circuits with a quantum differential-algebraic equations solver

Arkopal Dutt, Anirban Chowdhury, Kristan Temme, and Hari Krovi

2026

arXiv Bib

@article{dutt2026rlc,
  title = {Simulating dynamics of {RLC} circuits with a quantum differential-algebraic equations solver},
  author = {Dutt, Arkopal and Chowdhury, Anirban and Temme, Kristan and Krovi, Hari},
  year = {2026},
  dcat = preprint,
  selected = true
}

Learning Stabilizer Structure of Quantum States

Srinivasan Arunachalam, and Arkopal Dutt

In 58th Annual ACM Symposium on Theory of Computing, Salt Lake City, UT, USA, 2026

✎ Invited talk at Theory Seminar, UIUC
✎ Invited talk at Theory Seminar, UChicago .

arXiv Bib

@inproceedings{ad2025structure,
  author = {Arunachalam, Srinivasan and Dutt, Arkopal},
  title = {Learning Stabilizer Structure of Quantum States},
  year = {2026},
  isbn = {9798400725364},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3798129.3800900},
  doi = {10.1145/3798129.3800900},
  booktitle = {58th Annual ACM Symposium on Theory of Computing},
  pages = {1949–1959},
  numpages = {11},
  keywords = {Quantum learning, additive combinatorics, decompositions, stabilizer states},
  location = {Salt Lake City, UT, USA},
  series = {STOC '26},
  note = {&#9998; Invited talk at <a href="https://theoryincs.web.illinois.edu/#calendar"><i>Theory Seminar, UIUC </i></a> <br> &#9998; Invited talk at <a href="http://theory.cs.uchicago.edu//"><i>Theory Seminar, UChicago </i></a>.},
  dcat = conference,
  selected = true
}

Learning depth-3 circuits via quantum agnostic boosting

Srinivasan Arunachalam, Arkopal Dutt, Alexandru Gheorghiu, and Michael Oliveira

To appear in COLT’26, 2025

✎ Talk at QIP’26 (Riga).

arXiv PDF

Algorithmic Polynomial Freiman-Ruzsa Theorems

Srinivasan Arunachalam, Davi Castro-Silva, Arkopal Dutt, and Tom Gur

arXiv:2509.02338, 2025

Talk at QIP’26 (Riga).

arXiv Bib

@article{acsdg2025apfr,
  title = {Algorithmic Polynomial Freiman-Ruzsa Theorems},
  author = {Arunachalam, Srinivasan and Castro-Silva, Davi and Dutt, Arkopal and Gur, Tom},
  journal = {arXiv:2509.02338},
  year = {2025},
  note = {Talk at <a href="https://qip2026.lu.lv/"><i>QIP'26 (Riga)</i></a>.},
  dcat = conference,
  selected = true
}

Testing and learning structured quantum Hamiltonians

Srinivasan Arunachalam, Arkopal Dutt, and Francisco Escudero Gutierrez

arXiv:2411.00082, 2024

In STOC’25; Contributed talk at TQC’25; Accepted to CIMP.

PDF

Polynomial-Time Tolerant Testing Stabilizer States

Srinivasan Arunachalam, and Arkopal Dutt

In 57th Annual ACM Symposium on Theory of Computing, Prague, Czechia, 2025

Talk at QIP’26 (Riga).

arXiv Bib

@inproceedings{ad2024tolerant,
  author = {Arunachalam, Srinivasan and Dutt, Arkopal},
  title = {Polynomial-Time Tolerant Testing Stabilizer States},
  year = {2025},
  isbn = {9798400715105},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3717823.3718277},
  doi = {10.1145/3717823.3718277},
  booktitle = {57th Annual ACM Symposium on Theory of Computing},
  pages = {1234–1241},
  numpages = {8},
  keywords = {Gowers norm, Learning, Stabilizer states, Testing},
  location = {Prague, Czechia},
  series = {STOC '25},
  dcat = conference,
  note = {Talk at <a href="https://qip2026.lu.lv/"><i>QIP'26 (Riga)</i></a>.},
  selected = true
}

Power of Sequential Protocols in Hidden Quantum Channel Discrimination

Sho Sugiura, Arkopal Dutt, William J. Munro, Sina ğ\filu, and Isaac L. Chuang

Phys. Rev. Lett., Jun 2024

PDF
Learning Low-Degree Quantum Objects

Srinivasan Arunachalam, Arkopal Dutt, Francisco Escudero Gutiérrez, and Carlos Palazuelos

In 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024), Jun 2024

PDF
Optimal Algorithms for Learning Quantum Phase States

Srinivasan Arunachalam, Sergey Bravyi, Arkopal Dutt, and Theodore J. Yoder

In 18th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2023), Jun 2023

PDF
Active learning of quantum system Hamiltonians yields query advantage

Arkopal Dutt, Edwin Pednault, Chai Wah Wu, Sarah Sheldon, John Smolin, Lev Bishop, and 1 more author

Phys. Rev. Res., Jul 2023

PDF
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics

Arkopal Dutt, Andrey Lokhov, Marc D Vuffray, and Sidhant Misra

In Proceedings of the 38th International Conference on Machine Learning, 18–24 jul 2021

Abs PDF

The usual setting for learning the structure and parameters of a graphical model assumes the availability of independent samples produced from the corresponding multivariate probability distribution. However, for many models the mixing time of the respective Markov chain can be very large and i.i.d. samples may not be obtained. We study the problem of reconstructing binary graphical models from correlated samples produced by a dynamical process, which is natural in many applications. We analyze the sample complexity of two estimators that are based on the interaction screening objective and the conditional likelihood loss. We observe that for samples coming from a dynamical process far from equilibrium, the sample complexity reduces exponentially compared to a dynamical process that mixes quickly.