mariapapanikolaoulabs.com / research
Research
Quantum Computing · Quantum Machine Learning · AI
mariapapanikolaoulabs.com / research
Quantum Computing · Quantum Machine Learning · AI
Who I Am
I am a Quantum Computing Scientist and Quantum Machine Learning Researcher at Quantum Neural Technologies (QN⊗T) SA, with a BSc in Physics (University of Patras) and an MSc in Data Science & Machine Learning from the National Technical University of Athens (NTUA). My work sits at the intersection of quantum algorithms, variational quantum circuits, and classical machine learning — with a focus on problems where quantum methods offer genuine advantage.
My research spans hybrid quantum-classical architectures for combinatorial optimisation and protein folding, quantum randomness certification, and Physics-Informed Neural Networks for engineering problems. I have hands-on experience with IBM Quantum hardware, PennyLane, PyTorch, and Microsoft Azure Quantum.
Research Interests
Quantum Algorithms & VQAs
VQE, QAOA, variational circuits for optimisation and quantum chemistry.
Quantum Machine Learning
Hybrid quantum-classical models, quantum kernels, QNNs.
Quantum Cryptography & Randomness
QKD protocols, device-independent randomness certification.
Physics-Informed Neural Networks
PINNs for Sturm-Liouville problems, soil-structure interaction.
Academic Work
Independent Researcher · Zenodo · DOI: 10.5281/zenodo.20760399
A deterministic field theory modelling spacetime as a relativistic viscous fluid on a dynamical Lorentzian manifold. A single action principle unifies gravity, electromagnetism (with structurally guaranteed massless photon via gauge-invariant derivative), and quantum mechanics — recovering the Schrödinger equation in the non-relativistic limit via the Madelung transformation, where the quantum potential emerges from the medium's gradient energy. The viscous sector (Israel–Stewart relaxation) renders the system hyperbolic and causal; total energy is exactly conserved while wave energy decreases monotonically via viscous entropy production. The framework also provides a candidate superdeterminism mechanism via non-factorization of the Gauss-constrained initial-data surface, offering a loophole to Bell's theorem without ad hoc assumptions.
National Technical University of Athens (NTUA) · MSc Data Science & Machine Learning · ECTS: 90
Developed and benchmarked hybrid quantum-classical approaches to the protein folding problem using Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). Implemented circuits on IBM quantum hardware and evaluated performance against classical baselines, with rigorous noise analysis and error mitigation.
COMPDYN 2023 · DOI: 10.7712/120123.10610.20582 · Quantum Neural Technologies (QN⊗T) SA
Applied PINNs to solve the Sturm-Liouville boundary value problem arising in soil-pile interaction dynamics. The approach handles inhomogeneous soil profiles without the geometric constraints of FEM, using the number of nodes as a convergence criterion — a novel contribution of this work. Presented at COMPDYN 2023.
Wigner Research Center for Physics · Internship Research Report
Developed the theoretical background for a protocol generating maximally entangled qubits between Alice and Bob. Decomposed the required unitary matrices for IBM Quantum implementation, constructed the circuits, analysed error distributions, and identified equivalent gate sets with fewer gates and lower error rates. Applied error mitigation techniques throughout.
MSc Course: Data Science & Machine Learning · National Technical University of Athens (NTUA) · github.com/Bio-Data-Analysis-Team2-DSLM/Code
Built an explainable ML pipeline for detecting and classifying depression (unipolar/bipolar) using wrist actigraph motor activity data from 60 patients. Implemented a 1D-CNN for automatic feature extraction from raw actigraph signals alongside handcrafted research-based features, with XGBoost as the primary explainable classifier. Addressed significant class imbalance via data augmentation, and compared against logistic regression baselines. Dataset: Depresjon (ACM MM '18).
MSc Course: Distributed Systems · National Technical University of Athens (NTUA) · github.com/tolios/NoobCash
Designed and implemented a simplified cryptocurrency system from scratch in Python, covering the full blockchain stack: peer-to-peer networking, proof-of-work mining, transaction validation, and consensus. Configurable parameters include node count, block capacity, and mining difficulty. Experiments were conducted using pre-generated transaction sets to benchmark throughput and consistency across distributed nodes.
University of Patras · BSc Physics · Grade: 10/10
Theoretical and computational study of dynamical decoupling sequences as a technique for suppressing decoherence in quantum encoded states. Explored how tailored pulse sequences preserve quantum information over time in the presence of environmental noise.
Current Work
At Quantum Neural Technologies, I contribute to multiple active research lines aimed at bringing quantum computing methods into practical use for industry.
Protocols for generating and certifying true randomness using quantum mechanical principles, with applications in cryptography and secure key generation.
Building a VQA-specialised simulator and platform at QN⊗T SA that allows users and researchers to define and run VQE problems with custom ansatzes — without low-level circuit implementation overhead. The platform targets researchers needing flexible ansatz design for combinatorial optimisation and quantum chemistry problems.
Investigating ensemble prediction for protein folding using Variational Quantum Algorithm and Quantum-Enhanced Monte Carlo sampling. The approach leverages quantum superposition to explore conformational energy landscapes more efficiently than classical methods, with ensemble averaging to improve prediction robustness.
Credentials
AI Problem Solving & Advanced Estimation Algorithms
Certified Digital Technology Professional (CDTP)
Introduction to AI & Prediction and Estimation Algorithms
National Technical University of Athens
Academic Journey
From theoretical physics to quantum hardware to machine learning — a trajectory built around understanding nature at the deepest level and applying it computationally.
Tools & Frameworks
Aug 2022 – Present
Quantum Computing Scientist
Quantum Neural Technologies (QN⊗T) SA · Athens, Greece
Feb 2023 – Present
Quantum Machine Learning Researcher
Quantum Neural Technologies (QN⊗T) SA · Microsoft Azure, VQAs
Sep 2022 – Jul 2024
MSc — Data Science & Machine Learning
National Technical University of Athens (NTUA) · ECTS: 90
Oct 2021 – Jan 2022
Quantum Computing Scientist — Internship
Wigner Research Center for Physics · Budapest, Hungary
Oct 2017 – Oct 2021
BSc — Physics
University of Patras · Grade: 8/10 · ECTS: 240
2017 · Academic Distinctions
National university entrance exams (Πανελλήνιες): 18,728 pts — Mathematics 19.3/20 (top 1%), Physics 19.7/20. Reached the 3rd stage (Archimedes) of the Hellenic Mathematical Society competition.
Get in Touch
Whether you are a researcher, a PhD supervisor, or an industry partner exploring quantum-classical methods — I am open to conversations about research collaborations, joint projects, and opportunities.