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Mohsen Heidari

Assistant Professor

Quantum Computing & Information Science Faculty Fellow

Department of Computer Science

Indiana University, Bloomington

mheidar@iu.edu Google Scholor

I am an Assistant Professor at the Department of Computer Science at Indiana University, Bloomington. I am affiliated with IU Quantum Science and Engineering Center (QSEc) and NSF Center for Science of Information (CSoI). Before joining IU, I was a Visiting Assistant Professor at Purdue University and a Postdoctoral Research Associate at NSF Center for Science of Information (CSoI). I received my Ph.D. in Electrical Engineering (2019) and my M.Sc. in Applied Mathematics (2017) from the University of Michigan.

Research Interests

My research interests lie in quantum computing and algorithms, theoretical machine learning, and classical and quantum information theory.

Current Teaching

Fall 2024: B403 Introduction to Algorithm Design and Analysis

Spring 2024: CSCI-B 490/629 Seminar in Quantum Computing.

Current Students

Alireza Tavakoli (since Fall 2023)
Masih Mozakka (since Fall 2023)
Niloy Deb Roy Mishu (since Fall 2023)
Zahra Honjani (since Spring 2024)

Recent News

Apr. 24: New preprint paper Efficient Gradient Estimation of Variational Quantum Circuits with Lie Algebraic Symmetries.
Aug. 23: New journal paper Expected Worst Case Regret via Stochastic Sequential Covering accepted in Transactions on Machine Learning Research.
Aug. 23: New Pre-print On The Reliability Function of Discrete Memoryless Multiple-Access Channel with Feedback
May. 23: New journal paper Regret Bounds for Log-loss via Bayesian Algorithms accepted in IEEE Transactions on Information Theory.
May. 23: New journal paper Mathematical Modeling of Eicosanoid Metabolism in Macrophage Cells: Cybernetic Framework Combined with Novel Information-Theoretic Approaches accepted in Processes.
Jan 23: Two papers got accepted to AISTATS'23:
- Learning k-qubit Quantum Operators via Pauli Decomposition
- Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression
Jan. 23: Our paper On Non-Interactive Source Simulation via Fourier Transform got accepted in ITW'23.
Sept. 22: Our paper Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm got accepted in NeurIPS'22 and selected for oral presentation.
July 22: Thrilled to announce that our proposal about Learning From Classical and Quantum Data: a Fourier Perspective was funded by NSF.
May 22: New workshop on Quantum Algorithms, Information, and Learning was organized.
April 22: Our journal paper Sufficiently Informative and Relevant Features: An Information-Theoretic and Fourier-Based Characterization got accepted to IEEE Transactions on Information Theory.
April 22: Two papers got accepted to ISIT'22:
- Sequential vs Fixed Design Regrets in Online Learning
- Upper Bounds on the Feedback Error Exponent of Channels With State and Memory
Feb. 22: Our paper Toward Physically Realizable Quantum Neural Networks was accepted and selected for oral presentation in AAAI'22 (~15% acceptance rate).

Oct 21: New paper Faithful Simulation of Distributed Quantum Measurements with Applications in Distributed Rate-Distortion Theory got accepted to IEEE Transactions on Information Theory.
May 21: New paper Finding Relevant Information via a Discrete Fourier Expansion got accepted to ICML'21.
Apr 21: New paper A Theoretical Framework for Learning from Quantum Data got accepted to ISIT'21.
Apr 21: New paper Information Sufficiency via Fourier Expansion got accepted to ISIT'21.
Apr 21: Gave a talk about Learning from Classical and Quantum data: A Fourier Perspective, at
- Purdue CS Theory Seminar
- University at Albany, SUNY
- Imperial College London