About Me
Hi, my name’s Cenk, and I’m a Ph.D. candidate in quantum computing. I love working on multidisciplinary projects, and in fact, this is what brought me to quantum computing. I enjoy working with people from all around the world and am always looking for new collaboration opportunities to broaden my horizons. Below is a non-exhaustive list of my current research interests. Feel free to reach out via your favorite platform.
Research Interests:
- Quantum machine learning (QML); trainability and expressive capacity of QML models, barren plateaus, warm starting.
- Generative learning with QML
- Geometric machine learning; representation theory, category theory
- Understanding hardware noise to maximize utility of current hardware
- Physics simulations with Quantum Computing
- High Energy Physics applications of Quantum Computing
Experience
Los Alamos National Laboratory
Research Fellow
Summer 2023
Equivariance and representation theory within the context of GQML in the presence of hardware noise.
Efficient learning response functions of analog quantum devices from output distributions.
Designed and ran experiments on neutral atom quantum computers.
Supervisors: Andrey Lokhov, Marco Cerezo
Generative learning using QML algorithms such as QCBMs and QBMs.
Geometric quantum machine learning for classification.
Performance and behaviour of QML algorithms in the presence of hardware noise.
Warm start and ansatz design methods for QML algorithms.
QML for high energy physics problems, such as particle track reconstruction and jet event generation.
Supervisor: Prof. Dr. Karl Jansen
IBM Qiskit
Intern
Fall 2021
Worked on analyzing the performance of Qiskit’s qiskit-machine-learning python package as part of the Qiskit Advocate Mentorship Program.
Improved the speed of the QuantumKernel module 10 fold.
Supervisor: Anton Dekusar
METU IVMER & gluoNNet
Researcher
2019 - 2021
Quantum algorithms for high energy physics problems.
Trainability and expressive power of Quantum Neural Networks (QNNs).
CERN openlab & gluoNNet
Researcher
Summer 2019
QML algorithms for track reconstruction for large hadron collider (LHC) experiments at CERN.
Development of QML methods using Qiskit and Tensorflow.
CERN (AMS-02) & METU
Researcher
2018 - 2019
Statistical data analysis of cosmic ray data collected by AMS-02 located on ISS.
Time dependent analysis of He flux to investigate correlation with solar activity.
Education
Humboldt University of Berlin
Ph.D. in Physics
2021 - present
Tentative thesis title: Quantum Machine Learning and its applications in High Energy Physics.
Supervisor: Prof. Dr. Karl Jansen
Middle East Technical University (METU)
M.Sc. in Physics
2019 - 2021
Thesis title: Hybrid Graph Neural Networks for Particle Track Reconstruction at Large Hadron Collider (LHC).
Supervisor: Prof. Dr. M. Bilge Demirköz
Middle East Technical University (METU)
B.Sc. in Physics
2015 - 2019
Specialization and research in experimental high energy physics.
Middle East Technical University (METU)
B.Sc. in Electrical & Electronics Engineering
2014 - 2018
Specialization in robotics, control theory and signal processing.
Supervision / Teaching
- Gave a tutorial on QML using Pennylane at QClass23/24 organized by QWorld (Winter 2023-2024).
- Supervised an undergraduate student on introduction to variational quantum algorithms as part of the DESY Ukrainian Winter School (Winter 2022-2023).
- Supervised a graduate student on expressivity of parametrized quantum circuits as part of Qiskit Advocate Mentorship Program (Fall 2022).
- Supervised a graduate student on introduction to variational quantum algorithms as part of the DESY Summer School (Summer 2022).
- Supervised two undergraduate students to work on quantum convolutional neural networks (2021).
- Supervised an undergraduate for his thesis project on quantum kernel methods (2021).
- Gave online lectures to support the graduate course SPECIAL TOPICS IN COMPUTER ENGINEERING: QUANTUM COMPUTING at Middle East Technical University Computer Engineering department (Spring 2020).
- Gave a workshop on introduction to quantum computing at Ted University in Turkey (February 2020).
Presentations
- (Oral) Symmetry breaking in geometric quantum machine learning in the presence of noise. Quantum Computing Workshop, Munich, Germany (January 2024)
- (Poster) Equivariant quantum neural networks in the NISQ era. QTML23, Geneva, Switzerland (November 2023)
- (Poster) Classical splitting of parametrized quantum circuits. QIP23, Ghent, Belgium (February 2023)
- (Poster) Classical splitting of parametrized quantum circuits. QTML22, Naples, Italy (November 2022)
- (Poster) Classical splitting of parametrized quantum circuits. Quantum Technologies for High Energy Physics conference, Geneva, Switzerland (November 2022) (best poster award)
- (Poster) Quantum Graph Neural Networks for Track Reconstruction in Particle Physics and Beyond. QTML20, remote, (November 2020)
- (Oral) A Quantum Graph Network Approach to Particle Track Reconstruction, Connecting the Dots workshop, remote (April 2020)
- (Oral) A Quantum Graph Neural Network Approach to Particle Track Reconstruction. Geneva, Switzerland (January 2020)
Publications in Quantum Computing
- C. Tüysüz, S.Y. Chang, M. Demidik, K. Jansen, S. Vallecorsa, M. Grossi, “Symmetry breaking in geometric quantum machine learning in the presence of noise”, arXiv: 2401.10293 (2024).
- A.D. Meglio et al. “Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group”, arXiv: 2307.03236 (2023).
- X. Wang, Y. Chai, M. Demidik, X. Feng, K. Jansen, C. Tüysüz, “Symmetry enhanced variational quantum imaginary time evolution”, arXiv: 2307.13598 (2023).
- A. Crippa, L. Funcke, T. Hartung, B. Heinemann, K. Jansen, A. Kropf, S. Kühn, F. Meloni, D. Spataro, C. Tüysüz, Y. C. Yap, “Quantum Algorithms for Charged Particle Track Reconstruction in the LUXE Experiment”, Comput Softw Big Sci 7, 14 (2023).
- C. Tüysüz, G. Clemente, A. Crippa, T. Hartung, S. Kühn, and K. Jansen, “Classical Splitting of Parametrized Quantum Circuits”, Quantum Mach. Intell. 5, 34 (2023).
- K. Borras, S. Y. Chang, L. Funcke, M. Grossi, T. Hartung, K. Jansen, D. Kruecker, S. Kühn, F. Rehm, C. Tüysüz, and S. Vallecorsa, “Impact of quantum noise on the training of quantum Generative Adversarial Networks”, J. Phys.: Conf. Ser. 2438 012093 (2022).
- C. Tüysüz, C. Rieger, K. Novotny, B. Demirköz, D. Dobos, K. Potamianos, S. Vallecorsa, J.R. Vlimant, and R. Forster, “Hybrid quantum classical graph neural networks for particle track reconstruction”, Quantum Machine Intelligence 3, 29 (2021).
- C. Rieger, C. Tüysüz, K. Novotny, S. Vallecorsa, B. Demirköz, K. Potamianos, D. Dobos and J.R. Vlimant, “Embedding of particle tracking data using hybrid quantum-classical neural networks”, EPJ Web of Conferences (Vol. 251, p. 03065 (2021).
- C. Tüysüz, F. Carminati, B. Demirköz, D. Dobos, F.Fracas, K. Novotny, K. Potamianos, S. Vallecorsa and Vlimant, J.R., “Particle track reconstruction with quantum algorithms”, EPJ Web of Conferences (Vol. 245, p. 09013) (2020).