Team | NonTrivial | Theory lab of Stefanos Kourtis

Training leading talent at the frontiers of quantum physics and computer science

Discover our group

Stefanos Kourtis

Lead Researcher, Non Trivial
Associate Professor, Departments of Physics and Computer Science,
Institut quantique,
Université de Sherbrooke
Victor Drouin-Touchette

Staff Researcher
Amélie Proulx

Administrator
Sangeeth Das Kallullathil

Postdoctoral Researcher
Benjamin Morrison

Postdoctoral Researcher
Ayana Sarkar

Postdoctoral Researcher
Muhammad Zubair

Postdoctoral Researcher
Aleksandr Berezutskii

PhD student
Julien Drapeau

PhD student
Joseph Gibson

PhD student
Jérémie Gince

PhD student
Ameer Khan

PhD student
Benjamin Lanthier

PhD student
Alexandre Leblanc

PhD student
Martin Schnee

PhD student
Isaac Christ Domchi Kanga

Master's student
Shuka Haddadi

Master's student
Lorraine Tsitsi Majiri

Master's student

Victor Drouin-Touchette

Staff Researcher

Academic background

Postdoc in Quantum Computing at Rutgers University, supervised by Ananda Roy (2022-2023)
PhD in Theoretical Condensed Matter Physics at Rutgers University, supervised by Piers Coleman (2016-2022)
BSc in Mathematics and Physics at Université de Montréal (2013 – 2016)

Research Interests

Analog quantum computing and quantum simulation
Hybrid quantum-classical algorithms for optimization and operations research
Classical numerical methods for the simulation of quantum systems

Publications

Cédrick Perron, Yves Bérubé-Lauzière, and Victor Drouin-Touchette. "Leveraging Analog Neutral Atom Quantum Computers for Diversified Pricing in Hybrid Column Generation Frameworks." 2025. arXiv

Ayana Sarkar, Martin Schnee, Roya Radgohar, Mojde Fadaie, Victor Drouin-Touchette, and Stefanos Kourtis. "Concentration-Free Quantum Kernel Learning in the Rydberg Blockade." (2025). arXiv

Joseph Gibson, Victor Drouin-Touchette, and Stefanos Kourtis. "Quantum Counting in the Rydberg Blockade." (2025). arXiv

Awards & Scholarships

University and Bevier Dissertation Fellowship (Rutgers University, 2022)
Samuel Marateck Scholarship in Quantum Field Theory (Rutgers University, 2020)
Doctoral and Masters Scholarship (Fonds de recherche du Québec en Nature et Technologie)

About Me

My goal is to help define what can be implemented and verified on today’s quantum computers, and to use that knowledge to inform the design of near-term algorithms. I believe that hybrid quantum-classical approaches to practical tasks will only start to match (or outperform) purely classical frameworks if we take close inspiration from state-of-the-art classical methods; this leads naturally to interdisciplinary research. I am particularly fond of analog quantum computing approaches and am keeping a keen eye on digital-analog quantum computing frameworks.

Sangeeth Das Kallullathil

Postdoctoral Researcher

Academic background

Postdoc at University of Toronto, supervised by Prof. Artur Izmaylov (2024-2025)
PhD in Chemistry at Queen’s University, supervised by Prof. Tucker Carrington (2017-2022)
MSc in Chemistry at Indian Institute of Technology Madras, supervised by Prof. Mangalasunder Krishnan (2014-2016)
BSc in Chemistry at Calicut University (2010-2013)

Research Interests

Quantum Machine Learning
Tensor Network
Quantum Computing algorithms
Vibrational Structural Calculation

Publications

Shreyas Malpathak, Sangeeth das Kallullathil, and Artur Izmaylov. “Simulating Vibrational Dynamics on Bosonic Quantum Devices.” Phys. Chem. Lett. 16, 1855−1864, 2025. DOI
Sangeeth das Kallullathil and Tucker Carrington. “Computing vibrational energy levels using a canonical polyadic tensor method with a fixed rank and a contraction tree.” J. Phys. 158, 214102, 2023. DOI
Sangeeth das Kallullathil and Tucker Carrington. “Computing energy levels by solving linear equations using a tensor method with an imposed rank.” J. Phys. 155, 234105, 2021. DOI
Sangeeth das Kallullathil, Shreyas Malpathak, Stephan Fomichev, Ignacio Loaiza Ganem, Artur Izmaylov and Juan Miguel Arrazola. “Trotter simulation of vibrational Hamiltonians on a quantum computer”, 2025. arXiv

Awards & Scholarships

Institute Merit Scholarship, Indian Institute of Technology Madras (2015)
Best TA award (2022), TA of the year award (2020) – Queen’s University

About Me

I work on quantum machine learning and the broader application of quantum computing to diverse problems. My background includes quantum algorithms, tensor networks, and vibrational calculations, and I enjoy blending physics-driven intuition with computational methods. I also value teaching and mentoring, and I am motivated to transition into the quantum computing industry to help translate these ideas into practical impact.

Benjamin Morrison

Postdoctoral Researcher

Academic background

PhD in Physics at University of New Mexico, supervised by Andrew J. Landahl (2017-2025)
MS in Physics at University of New Mexico, supervised by Andrew J. Landahl (2017-2019)
BA in Mathematics and Physics at Reed College, supervised by Adam Groce and Darrel Schroeter (2013-2017)

Research Interests

Quantum information, computation, and simulation; in particular, error correction, architectures, development tools, and algorithms.

Publications

Benjamin C. A. Morrison and Andrew J. Landahl. "Logical Majorana fermions for fault-tolerant quantum simulation," 2021. arXiv
A. E. Russo, K. M. Rudinger, B. C. A. Morrison, and A. D. Baczewski. "Evaluating energy differences on a quantum computer with robust phase estimation," Phys. Rev. Letters 126.21 (2021), 210501. DOI
B. C. A. Morrison, A. J. Landahl, D. S. Lobser, K. M. Rudinger, A. E. Russo, J. W. Van Der Wall, and P. Maunz. "Just Another Quantum Assembly Language (Jaqal)." 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (pp. 402–408). DOI
Benjamin C. A. Morrison and Adam Groce. "Oracle separations between quantum and non-interactive zero-knowledge classes." Information Processing Letters 154 (2020): 105864. DOI

Awards & Scholarships

James Borders Student Physics Fellowship at Reed College, 2015.

About Me

I take a holistic and interdisciplinary approach to quantum computing, designing tools to support future quantum information systems from low-level control to application-focused algorithms. I want to use this approach to enable the scientific community to make the best possible use of new quantum hardware as it becomes available. I also hope to hone my skills as a mentor and teacher, so I can help share the joy of quantum computing with a broader audience.

Ayana Sarkar

Postdoctoral Researcher

Academic background

Institut Quantique Postdoctoral Fellowship (May, 2023)
PhD in Physics from the Shiv Nadar Institution of Eminence, Delhi-NCR, India supervised by Prof. Santosh Kumar (deceased) (2017-April, 2023)
MSc (Physics) from the National Institute of Technology, Jamshedpur, Jharkhand, India supervised by Dr. Priyanka Maity (2015–2017)
BSc (Hons) Physics at University of Calcutta (2012–2015)

Research Interests

Quantum Machine Learning & Quantum Algorithms
Quantum Many-Body Physics, Quantum Chaos & Quantum Information
Random Matrix Theory

Publications

Jérémie Gince, Jean Michel Pagé, Marco Armenta, Ayana Sarkar and Stefanos Kourtis, "Fermionic Machine Learning", 2024. DOI arXiv
Ayana Sarkar, Martin Schnee, Roya Radgohar, Mojde Fadaie, Victor Drouin-Touchette, Stefanos Kourtis, “Concentration-Free Quantum Kernel Learning in the Rydberg Blockade” (under review). 2025. DOI arXiv

Awards & Scholarships

Kourtis Lab Microgrant (2025)
DST-INSPIRE Fellowship, National Fellowship from the Govt. Of India (2018-2023)
DST-INSPIRE Scholarship, National Scholarship from the Govt. Of India (2012-2017)
JBNSTS Scholarship (Jagadish Bose National Science Talent Search), West Bengal, India (2012)

About Me

I am passionate about developing novel quantum algorithms, both in algorithm research and quantum machine learning, by leveraging insights from quantum many-body physics. I often draw on my background in quantum chaos and random matrix theory to study these systems in depth. My long-term goal is to apply hybrid knowledge of physics and quantum algorithms to address problems of broad societal importance. Alongside my research, I enjoy teaching and mentoring students, which I see as a deeply rewarding part of my work. Looking ahead, I also envision an entrepreneurial path, with the aspiration of founding a physics-driven artificial intelligence company.

Muhammad Zubair

Postdoctoral Researcher

Academic background

PhD in Physics at Concordia University, supervised by Panagiotis Vasilopoulos (2018-2022)

Research Interests

Quantum computing: Develop the quantum algorithms to solve computationally intractable problems for classical supercomputers such as combinatorial optimization problems, portfolio optimization etc.
Geometrical and topological machine learning
Study quantum phase transition of Ising model via quantum algorithms
Study the Quantum Hall physics in novel 2D quantum systems such as graphene, topological insulators etc.

Publications

Chang, M. Zubair, J. L. Cheng and W.-K. Tse, “Second Harmonic Generation in Topological Insulators under Magnetic Field”, 2025. DOI
Zubair, P. Vasilopoulos, and M. Tahir, Valley-controlled transport in graphene/ WSe₂ heterostructures under an off-resonant polarized light”, 2022. DOI
Zubair, P. Vasilopoulos, and M. Tahir, “Influence of interface induced valley-Zeeman and spin-orbit couplings on transport in heterostructures of graphene on WSe₂”, 2020. DOI
Zubair, M. Bahrami, and P. Vasilopoulos, “Transport in armchair graphene nanoribbons and in ordinary waveguides”, 2019. DOI

Awards & Scholarships

Mitacs Accelerate grant (IT48297) at Universite de Sherbrooke (2026-2027)
Research Associate grant (0.1 million $ CND) at Concordia, Canada (2018-2021)
Concordia University conference and Exposition award (2019 and 2020)
Student conference travel support Faculty of Arts and Science at Concordia (2019 and 2020)
Concordia international tuition award of excellence (2018-2020)
Concordia University graduate fellowship (2018-2020)
Concordia University merit scholarship (2020-2021)
Concordia University accelerator award (2020-21)
MS award to study at Quaid-i-Azam University, Pakistan (2012-2014) (Secure 1^st position in MS + Vice chancellor’s medal)
Merit based scholarship during BS at University of Sargodha, Pakistan (2007-2011)

Patent:

Zubair, R. Radgohar, A. J. Awan, and S. Kourtis, “Transmitter and Method for Determining Perturbation Vector”, US App. PCT/SE2025/051170, filed December 19, 2025.

About Me

My research interests not only focus on theoretical condensed matter physics, but also its interaction between information and computational approaches. Thus, my goal is to not only develop quantum algorithms but also apply them to solve the NP hard combinatorial optimization problems. In addition, I enjoy my role as teaching computational physics courses at both undergraduate and graduate levels and mentoring the students in their research.

Aleksandr Berezutskii

PhD student

Academic background

MSc in Physics and Mathematics at Moscow Institute of Physics and Technology and Skolkovo Institute of Physics and Technology, supervised by Prof. Jacob Biamonte (2017–2019)
BSc in Physics and Mathematics at Moscow Institute of Physics and Technology (2013–2017)

Research Interests

Tensor Networks
Quantum Error Correction

Publications

Aleksandr Berezutskii. "mdopt: A code-agnostic tensor-network decoder for quantum error-correcting codes.", 2025. DOI
Aleksandr Berezutskii, Minzhao Liu et al. "Tensor networks for quantum computing," 2025. DOI
Aleksandr Berezutskii, Ilia Luchnikov, Aleksey Fedorov. "Simulating quantum circuits using the multi-scale entanglement renormalization ansatz.", 2025. DOI
Alena Termanova, Artem Melnikov, Egor Mamenchikov, Nikita Belokonev, Sergey Dolgov, Aleksandr Berezutskii, Roman Ellerbrock, Christopher Mansell, Michael Perelshtein. "Tensor quantum programming.", 2024. DOI

Awards & Scholarships

Unitary Foundation — Microgrant Winner (mdopt, 2023)
QHack (IBM/Google challenge) — 2^nd/1^st place (2022)
Université de Sherbrooke — Exemption Scholarship for International Students (2020)

About Me

I am interested in the interplay of quantum computing and modern classical computational methods, such as tensor networks, differentiable programming and machine learning. I use these techniques to solve hard problems. Open-source software enthusiast.

Alexandre Leblanc

PhD student

Academic background

MSc in Theoretical Physics at Université de Sherbrooke, supervised by Pr. Claude Bourbonnais and Pr. Genny Chitov (2022 – 2024)
BSc in Physics at Université de Sherbrooke (2018 - 2021)
Graduate Micro-program in scientific interaction at Université de Sherbrooke (2022 – 2024)

Research Interests

Quantum Algorithms
Quantum Hamiltoninan Simulation
Digital-Analog Quantum Computing

Publications

Valerio Faraoni, Pierre-Antoine Graham, Alexandre Leblanc. “Critical solutions of nonminimally coupled scalar field theory and first-order thermodynamics of gravity”, 2022. Phys. Rev. D 106, 084008 DOI
Valerio Faraoni, Sonia Jose, Alexandre Leblanc. “Curious case of the Buchdahl-Land-Sultana-Wyman-Ibañez-Sanz spacetime”, 2022. Phys. Rev. D DOI
Sonia Jose, Alexandre Leblanc, Valerio Faraoni. “When can we compute analytically lookback time, age of the universe, and luminosity distance?”, 2022. The European Physical Journal C DOI
Valerio Faraoni, Alexandre Leblanc. “Disformal mappings of spherical DHOST geometries”, 2021. JCAP DOI

Awards & Scholarships

IQuCode Scholarships 2026
Desjardins Foundation Scholarship 2022
RECSUS Scholarship 2022
REMDUS Scholarship 2022
AGES Scholarship 2022

About Me

I am a PhD student in Quantum Computing with a Master’s degree in Theoretical Physics. My research focuses on the development of efficient algorithms for Hamiltonian simulation, and exploring the digital-analog quantum computing (DAQC) paradigm. By leveraging the strengths of both gate-based operations and native hardware interactions, I work on optimizing quantum algorithms to enhance simulation performance on near-term and fault-tolerant architectures.

Martin Schnee

PhD student

Academic background

MSc in Quantum Physics at Université de Sherbrooke, supervised by Stefanos Kourtis (2020-2022)
BSc in Physics at Université de Sherbrooke (2019)

Research Interests

Quantum many-body dynamics
Rydberg-atom quantum simulators
Quantum scars
Entanglement growth
Tensor Network simulations
Quantum Kernel Learning

Publications

Ayana Sarkar, Martin Schnee, Roya Radgohar, Mojde Fadaie, Victor Drouin-Touchette, and Stefanos Kourtis. "Concentration-free kernel learning in the Rydberg blockade", 2025. arXiv
Martin Schnee, Roya Radgohar, and Stefanos Kourtis. "Unconventional early-time relaxation in the Rydberg chain", 2024. arXiv

Awards & Scholarships

About Me

My research aims to understand better the phenomenon of quantum many-body scarring arising Rydberg-atom simulators, which defies the natural tendency of complex systems to thermalize. For example: Are there specific features of this phenomenon that are easy to access in today’s experiments? What is the structure of quantum correlations that is building up as this phenomenon unfolds? With this understanding, I am interested in exploring whether we can leverage this exotic phenomenon as a ressource for some quantum computation. For example: can we use it to build quantum kernel algorithms for data classification which do not lose their performance as the quantum computer size is increased (as was usually the case so far)? I investigate these questions mainly using numerical tools involving exact diagonalization and tensor network methods to simulate quantum dynamics.