publications

2026

1. 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
   }

2. 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
   }

3. 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
   }

2025

1. 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
2. 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
   }

3. 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
   }

2024

1. 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
2. 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
3. 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

2023

1. 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
2. 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

2021

1. 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.