About Me

I am a theoretical physicist and Assistant Professor at the Decision Analysis and Support Unit, SGH Warsaw School of Economics, and a researcher at the Faculty of Electronics, Military University of Technology (WAT). My work operates at the cutting edge of theoretical physics and data science — I develop mathematical frameworks that translate quantum mechanical formalism into powerful, practical tools for anomaly detection, time series analysis, and financial forecasting.

The cornerstone of my current research is Quantum Information Field Theory (QIFT) — a pioneering framework developed in collaboration with Prof. Jacob Cybulski (Deakin University). QIFT redefines data as fields of relational information, exploiting quantum non-commutativity as a structural advantage. This approach challenges the "phase-deaf" limitations of classical statistics in Hilbert space, a critique I formalized in: The Inverse Born Rule Fallacy (2026).

In the industry, I serve as a Quantum Machine Learning Engineer at finQbit, where I bridge high-level theory with industrial deployment. We have recently published our first flagship research paper from finQbit: Option Pricing on Noisy Intermediate-Scale Quantum Computers (2026). This work demonstrates the practical utility of Quantum Neural Networks (QNNs) in derivative pricing, backed by extensive benchmarks on state-of-the-art quantum hardware including IBM Fez, IQM Garnet, IonQ Forte, and Rigetti Ankaa-3.

My academic trajectory is a journey through complex structures: PhD in particle physics (neutrino oscillations) → Topological analysis of biomolecules (Nature Scientific Reports) → Graph-based intelligence → Applied QML. Driven by a focus on geometric and information-theoretic structures, I am a member of the QPoland.

Research

Primary Framework & Industrial Application

Quantum Information
Field Theory (QIFT)

QIFT reframes data encoding in QML by treating observations as non-commutative Hamiltonian evolution. This overcomes the "phase-deaf" limitations of standard amplitude encoding, a critique formalized in The Inverse Born Rule Fallacy.

Theoretical Foundation

Critique of standard quantum representation paradigms and introduction of the relational information field framework.

arXiv:2602.21350 (Theory)

Industrial Deployment (finQbit)

Option Pricing on NISQ Computers: Our first flagship paper from finQbit. We demonstrate a QNN approach for financial derivatives, benchmarked on IBM Fez, IQM Garnet, and Rigetti Ankaa-3.

arXiv:2604.19832 (Option Pricing)

Research
Trajectory

2007–2013

Particle Physics & Quantum Field Theory

PhD on neutrino oscillations and non-standard interactions. Work on family symmetries and the mathematical structure of quantum fields.

2014–2019

Topological Data Analysis & Biomolecular Geometry

Developed the genus invariant for biomolecules. Published in Nature Scientific Reports (2018).

2020–2025

Graph Data Science & Community-Aware Features

Focus on large networks and community detection. Papers in Social Network Analysis and Mining (2024).

2025 →

Quantum Machine Learning & QIFT

Bridging theory and industry. Active development of QIFT and deployment of industrial-grade QML at finQbit.

Key
Collaborators

Jacob L. Cybulski

Deakin University, Australia

QIFT · QML · Time Series

Rafał Pracht

finQbit

Quantum Finance · QNN · NISQ

Bogumił Kamiński

SGH Warsaw School of Economics

Graph Theory · Decision Analysis

Paweł Prałat

Toronto Metropolitan University

Complex Networks

François Théberge

Tutte Institute

Graph Algorithms

Bartosz Dziewit

University of Silesia

QFT · Family Symmetries

Publications

Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach

S.Z, R. Pracht
•TBA (2026)

The Inverse Born Rule Fallacy: On the Informational Limits of Phase-Locked Amplitude Encoding

S.Z. J. L. Cybulski, B. Dziewit, T. Kulpa
•TBA (2026)

Quantum Data Analysis: From Statistical Points to Quantum Information Fields

S.Z., J. L. Cybulski,
•to appear

Novel approaches to the implementation of quantum reservoir computing models

J. L. Cybulski, S. Wladyka, S.Z.
•to appear

Investigation of Quantum Autoencoder Architectures for Effective Signal and Time Series Denoising

J. L. Cybulski, A. Strąg, J. Zwoniarski, S.Z.
•to appear

RSCUcaller: R Package for analyzing differences in relative synonymous codon usage

M.Mazdziarz, S.Z., Ł.Paukszto, J.Sawicki
•BMC Bioinformatics vol. 26 no. 141 (2025)

Quantum Modelling of Time Series: Expressivity vs. Trainability

J. L. Cybulski, S.Z.
•TBA (2025)

Incorporating Gravity into the Path Integral of Quantum Mechanics Using the Thermodynamics of Spacetime

G. Biehle, C. Ellgen, B. Sabra, S.Z.
•OSF Preprints (2022)

Selected machine learning methods used for credit scoring

M. Wrzosek, D. Kaszynski, K. Przanowski, S.Z.
•Oficyna Wydawnicza SGH (2020)

Variable selection methods

K. Przanowski, S.Z, D. Kaszynski, L. Opinski
•Oficyna Wydawnicza SGH (2020)

Metody selekcji zmiennych w modelach skoringowych

K. Przanowski, S.Z.
•Oficyna Wydawnicza SGH (2020)

Family Symmetries and Multi Higgs Doublet Models

B.Dziewit, J.Holeczek, S.Z., M.Zralek
•Symmetry vol. 12(1) no. 156 (2020)

Genus for biomolecules

P. Rubach, S.Z. , B. Jastrzebski, J. I. Sulkowska, P. Sulkowski
•Nucleic Acids Research (2019)

Genus trace reveals the topological complexity and domain structure of biomolecules

S.Z., C. Geary , E. A. Andersen, P. Dabrowski-Tumanski, J.Sulkowska, P.Sulkowski
•Nature Scientific Reports vol. 8 (2018)

Lepton Masses and Mixing in Two-Higgs-Doublet Model

P. Chaber, B. Dziewit, J. Holeczek, M. Richter, M. Zralek, S.Z.
•Physical Review D vol. 98 (2018)

The discrete family symmetries as the possible solution to the flavour problem

B.Dziewit, J.Holeczek, M.Richter, M.Zralek, S.Z.
•Physics of Atomic Nuclei vol. 80 no. 4 (2017)

Texture zeros in neutrino mass matrix

B.Dziewit, J.Holeczek, M.Richter, M.Zralek, S.Z.
•Physics of Atomic Nuclei vol. 80 no. 2 (2017)

The Flavour Problem and the Family Symmetry Beyond the Standard Model

B.Dziewit, J.Holeczek, M.Richter, M.Zralek, S.Z.
•Acta Physica Polonica B vol. 46 no. 11 (2015)

Attempts at Explaining Neutrino Masses and Mixings Using Finite Horizontal Symmetry Groups

B.Dziewit, M.Zralek, S.Z.
•Acta Physica Polonica B vol. 44 no. 11 (2013)

Majorana neutrino mass matrix with CP symmetry breaking

B.Dziewit, M.Zralek, S.Z.
•Acta Physica Polonica B vol. 42 no. 11 (2011)

The method of the likelihood and the Fisher information in the construction of physical models.

E.W.Piotrowski, J.Sladkowski, J.Syska, S.Z.
•Physica Status Solidi B vol. 246 no. 5 (2009)

Neutrino Oscillations in the case of general interactions

J.Syska, M.Zralek, S.Z.
•Acta Physica Polonica B vol. 38 no. 11 (2007)

Monographs

Sztuczna inteligencja w przedsiębiorstwach i gospodarce (AI Spring 2024)

Editors: T. Doligalski, D. Kaszyński•Oficyna Wydawnicza SGH (2025)

Modelowanie dla biznesu. Analityka w czasie rzeczywistym - narzędzia informatyczne i biznesowe

Editors: S. Zajac•Oficyna Wydawnicza SGH (2022)

The Credit scoring in the context of interpretable machine learning. Theory and Practice.

Editors: D. Kaszyński, B. Kamiński, T. Szapiro.•Oficyna Wydawnicza SGH (2020)

Modelowanie dla biznesu. Metody machine learning, modele portfela Consumer Finance, modele rekurencyjne analizy przeżycia, modele skoringowe

Editors: K.Przanowski, S.Zajac•Oficyna Wydawnicza SGH (2020)

Education

University of Silesia in Katowice

Doctor of Philosophy•17.09.2013

Advisor: prof. dr hab Marek Zrałek
Accelerator neutrino oscillations and their non-standard interactions (PL) View

Master of Science•27.06.2007

Advisor: dr hab. Jerzy Król
Some geometrical and topological methods in classical and quantum field theory (PL). View

Bechelor of Science•27.06.2005

Advisor: dr hab. Jacek Syska
Time Series analysis with ARMA and ARIMA processes. Application in SAS. (PL) View

Music School in Rybnik, I and II degree

Musician•2004

Advisor: F. Prus
Accordeon class.

Talks &
Lectures

2025 Invited Talk

Quantum Measurement and Temporal Model Interpretation in QML

Quantum Machine Learning Conference 2025 · Quantum AI Foundation · Online

Discussed practical and theoretical issues of measuring quantum temporal models — interpreting measurement results in the context of time-series analysis and forecasting tasks.

2024 Invited Lecture

Quantum Computing for Business

intoDIGITAL: Economic Sciences in Times of Digital Technology · SGH Warsaw School of Economics

Lecture on possibilities of quantum computers and algorithms for optimisation problems and machine learning, with foundations in quantum physics relevant to AI development.

2024 Conference

Design Considerations for Denoising Quantum Time Series Autoencoder

24th International Conference on Computational Science (ICCS 2024) · Springer LNCS vol. 14837

Presented design decisions for variational quantum time series models and denoising autoencoders — data encoding, ansatz design, measurement interpretation, and deployment on quantum hardware.

2023–2025 Course

Introduction to Quantum Machine Learning

SGH Warsaw School of Economics · Course materials →

Annual graduate course covering quantum circuits, variational algorithms, quantum kernel methods, and NISQ-era QML. Materials and notebooks publicly available on GitHub.

2020–2025 Course

Real-Time Data Analytics

SGH Warsaw School of Economics, Big Data Programme · Course materials →

Lecture and lab series on streaming data architectures, Apache Spark, anomaly detection in real-time systems, and MLOps pipelines. One of the first such courses in Polish academia.

2024–2025 Course

Quantum Technologies

Kozminski University, Warsaw

Invited lecture series on quantum computing foundations and their business applications for graduate students in management and data science programmes.

Full presentations archive →