Ammar Daskin

Contact

Ammar Daşkın, Ph.D.
Associate Professor, Dept. of Computer Engineering
Istanbul Medeniyet University, Istanbul, Türkiye
Email: adaskin25@gmail.com

Resume (pdf)

Profiles

Google Scholar · ORCID · Web of Science · Scopus · GitHub

Latest posts on adaskin.substack.com
this site: adaskin.github.io

Summary

I am a researcher in quantum computation, algorithms, and machine learning, with applications in data science, bioinformatics, and chemistry. My work spans numerical methods for mapping computations to quantum circuits, quantum algorithms for matrix computations and optimization, processing high-dimensional data on quantum hardware, and testing quantum machine learning models for both near-term (NISQ) and fault-tolerant architectures.

Research Interests

  • Quantum Machine Learning & Quantum Neural Networks
  • Quantum Algorithms & Combinatorial Optimization
  • Quantum Circuit Design, Simulation & Compilation
  • Error-Resilient Quantum Computation
  • Quantum Bioinformatics
  • Numerical Linear Algebra & Parallel Computing & High Performance Computing

Education

  • Ph.D., Computer Science — Purdue University, West Lafayette, IN, USA (2014)
    Dissertation: Quantum Circuit Design Methods and Applications
    Advisors: Prof. Ananth Grama & Prof. Sabre Kais

  • M.Sc., Computer Science — Purdue University, West Lafayette, IN, USA (2011)

  • B.Sc., Computer Engineering — Erciyes University, Kayseri, Türkiye (2007)

Teaching

I have taught a wide range of foundational and advanced computer science courses, including data mining, system programming, operating systems, data structures and algorithms, compiler design, discrete mathematics, and introductory programming across various languages.

As the landscape of computing rapidly evolves, my recent teaching efforts are dedicated to redesigning course syllabi to emphasize hands-on AI applications and the core computational infrastructures required to build and scale modern AI systems.

See full course list and lecture notes on the Teaching page.

Recent Publications

  • Daskin, A. Quantum distortion model for running variational quantum algorithms without error corrections. Discov. Quantum Sci. 2, 2 (2026).
  • Daskin, A. Quantum RNNs and LSTMs Through Entangling and Disentangling Power of Unitary Transformations. ICAART 2026.
  • Daskin, A. Error analysis of quantum operators written as a linear combination of permutations. Quantum Inf Process 24, 149 (2025).
  • Daskin, A. A unifying primary framework for quantum graph neural networks from quantum graph states. Eur. Phys. J. Spec. Top. 234, 6279–6288 (2025).

Full publication list →

Professional Service

  • Co-organizer: Special Sessions on Quantum Cybernetics and Machine Learning, IEEE SMC 2019 & 2020
  • Panelist: TÜBİTAK
  • Reviewer: IEEE TPAMI, IEEE TNNLS, Scientific Reports, Quantum Information Processing, Quantum Machine Intelligence, ACM Computing Surveys, and others
  • Memberships: IEEE Computer Society, IEEE SMC Quantum Cybernetics, ACM