Hooman Karamnejad

  • Personal Info

    Full name:

    Hooman Karamnejad (/huˈmæn/ /kæræmˌneˈʒɑd/)

    Email:

    hooman.krmnjd [at] iasbs.ac.ir

    Personal Email:

    hoomania [at] protonmail.com

    Homepage:

    https://hooman.page

    Linkedin:

    https://linkedin.com/in/hooman-krmnjd

    Github:

    https://github.com/hoomania

  • Personal Statement

    My research journey lies at the intersection of quantum physics and machine learning. As an M.Sc. student in Condensed Matter Physics at IASBS, I explored phase transitions in the extended Bose-Hubbard and Kitaev ladder models using tensor network simulations and machine learning tools. These experiences not only deepened my understanding of computational quantum physics but also honed my ability to explore the intersection between quantum many-body systems and neural networks.

    I aim to leverage machine learning and optimization techniques to address challenging problems in quantum many-body physics. My long-term goal is to take incremental steps toward developing interpretable models that can uncover emergent behaviors in strongly correlated quantum systems and contribute to the theoretical backbone of quantum science.

  • Education

    Master of Science:

    Condensed Matter Physics from IASBS, Zanjan, Iran (Nov 2021 – Sep 2024)

    Thesis: Tensor Neural Networks for Capturing Quantum Phase Transitions

    Supervisor: Dr. Saeed S. Jahromi

    Code:

    https://github.com/hoomania/msc_thesis

    Thesis Qualification: Excellent (Best Possible)

    Bachelor of Science

    Physics from Kerman University, Kerman, Iran (Oct 2016 – March 2021)

    Project: Application of Maximum Likelihood Estimation in Reverse Problems

    Supervisor: Dr. Mohammad Shojai Baghini

    Code:

    https://github.com/hoomania/MLE

    Project Qualification: 19.75 (max is 20)

  • Publication

    Kitaev honeycomb antiferromagnet in a field: quantum phase diagram for general spin

    Saeed S. Jahromi, Max Hörmann, Patrick Adelhardt, Sebastian Fey, Hooman Karamnejad, Roman Orus, Kai Phillip Schmidt

    Communications Physics 7, 319 (2024) [Full Text], [arXiv:2111.06132]

  • Poster

    Machine Learning Phase Transition Detecting for Classical Ising Model

    28th Annual IASBS Meeting on Condensed Matter Physics (2023)

    Kitaev Ladder Phase Diagram Using Machine Learning

    6th Iranian Conference on Computational Physics (2023)

  • Personal Project

    QuaTenNet

    Rust package providing essential tools for working with tensor networks in computational quantum physics.

    Code:

    https://github.com/hoomania/qua_ten_net

    Crate:

    https://crates.io/crates/qua_ten_net

  • Certificate

    Qiskit Quantum Computing and Programming (2022)

    Certificate No. QBronze86-59

  • Skill

    Programming & Tools:

    Python, Rust, C++, Octave, Bash, Git, Slurm, Linux

    Machine Learning:

    PyTorch, Scikit-Learn, CNNs, Autoencoders, RBMs, PCA, Anomaly Detection

    Quantum Simulations:

    Tensor Networks, Monte Carlo, infinite Time Evolving Block Decimation (iTEBD) [Source Code]

    Database & Web Programming:

    MySql, MongoDB, Neo4j, PHP, Laravel, Vue.js, JQuery, Tailwind

  • Research Interest

    • Neural Quantum State
    • Tensor Networks
    • Quantum Algorithm
    • Topological Quantum Phase of Matter
    • Neuromorphic Computing
    • Spiking Quantum Neural Networks

  • Language

    • Farsi (Native)
    • English (Professional working proficiency)