Arkopal Dutt

IBM Quantum, Cambridge, MA

I am currently a research scientist at IBM Quantum, Cambridge, MA. My research interests lie in quantum learning theory and quantum algorithms. I completed my PhD at MIT in the Quanta Lab at the Center of Ultracold Atoms, 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

Testing and learning structured quantum Hamiltonians

Srinivasan Arunachalam, Arkopal Dutt, and Francisco Escudero Gutierrez

arXiv preprint arXiv:2411.00082, 2024

In STOC’25

PDF
Polynomial-time tolerant testing stabilizer states

Srinivasan Arunachalam, and Arkopal Dutt

arXiv preprint arXiv:2408.06289, 2024

In STOC’25

PDF
Power of Sequential Protocols in Hidden Quantum Channel Discrimination

Sho Sugiura, Arkopal Dutt, William J. Munro, and 2 more authors

Phys. Rev. Lett., Jun 2024

PDF
Learning Low-Degree Quantum Objects

Srinivasan Arunachalam, Arkopal Dutt, Francisco Escudero Gutiérrez, and 1 more author

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 1 more author

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, and 4 more authors

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 1 more author

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.