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Narges Alavisamani

I am a Ph.D. student at the School of Computer Science at Georgia Tech. My advisor is Professor Moinuddin Qureshi and my research interests are at the intersection of quantum computing, computer architecture, and machine learning. I received my M.Sc. in Computer Science at Université Paris Diderot joint with École Normale Supérieure and École Polytechnique, Paris, France. Before that, I completed my B.Sc. in Computer Engineering at the University of Tehran, Tehran, Iran.

Publications

Narges Alavisamani, Suhas Vittal, Ramin Ayanzadeh, Poulami Das, Moinuddin Qureshi, Promatch: Extending the Reach of Real-Time Quantum Error Correction with Adaptive Predecoding, International Conference on Architectural Support for Programming Languages and Operating Systems(ASPLOS), 2024.

Ramin Ayanzadeh, Ahmad Mousavi, Narges Alavisamani, Moinuddin Qureshi, Enigma: Privacy-Preserving Execution of QAOA on Untrusted Quantum Computers. arXiv preprint arXiv:2311.13546 (2023).

Sashwat Anagolum, Narges Alavisamani, Poulami Das, Moinuddin Qureshi, Yunong Shi, Élivágar: Efficient Quantum Circuit Search for Classification, International Conference on Architectural Support for Programming Languages and Operating Systems(ASPLOS), 2024.

Ramin Ayanzadeh, Narges Alavisamani, Poulami Das, Moinuddin Qureshi, FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes, International Conference on Architectural Support for Programming Languages and Operating Systems(ASPLOS), 2023.

Narges Alavisamani, Hossein Aghababa, Application of Quantum Gradient Descent as a Learning Algorithm for Factorization Machines, Proceedings of the 10th Hellenic Conference on Artificial Intelligence, 2018.

Amirhossein Moshrefi, Narges Alavisamani, Hossein Aghababa, Knock Detection Improvement Applying Quantum Signal Processing Method in Automotive System, Proceedings of the 10th Hellenic Conference on Artificial Intelligence, 2018.

Research Talks

  1. A Reinforcement-Learning-Based Framework for Designing Robust Quantum Circuit
    Career Workshop for Inclusion and Diversity in Computer Architecture, MICRO 2022, Chicago, US, Oct. 2022
  2. Adversarial Hybrid Quantum-Classical Neural Networks for Quantum Encryption
    University of Edinburgh, Edinburgh, UK, July 2019
  3. Application of Quantum Gradient Descent as a Learning Algorithm for Factorization Machines Laboratoire d’Informatique de Paris 6 (LIP6), Sorbonne University Paris, France, Oct. 2018