arXiv subjects

Ioannis Theodonis

Publications and source records attributed to Ioannis Theodonis.

5 recordsLinked to original sources

arXiv2026

QuantumChain: Blockchain-Backed Quantum Federated Learning for Financial Fraud Detection

Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning (QFL) framework that combines hybrid quantum-classical neural networks, encrypted federated aggregation, blockchain-based auditability, and quantum-secure communication. Each client trains a local hybrid model in which a variational quantum circuit is embedded between classical neural layers, while model updates are protected through homomorphic encryption, threshold secret sharing, and QKD-based keying. A permissioned blockchain records aggregation events and supports reputation-weighted trust among participants. We evaluate QuantumChain on financial transaction data using a compact, size-matched classical baseline to isolate the effect of the quantum layer. Results show that the HQNN achieves comparable accuracy while improving fraud-class recall in most settings, reaching 94.6% recall compared with 93.2% for the classical model. The Deep QLayer improves performance in full-data settings, suggesting that added circuit depth helps recover representational capacity when the shallow circuit becomes limited. Mixed-state simulations further show that the recall trend persists under non-ideal quantum evolution. In federated deployment with 10 heterogeneous clients, global accuracy increases from 97.7% to 98.8% over five rounds before stabilizing. These results show that QuantumChain can integrate depth-aware hybrid quantum models into a secure federated fraud-detection pipeline while maintaining stable global convergence.

quant-ph↗

arXiv2025

Time Series Analysis of DECAL Sensor Noise for the Generation of Truly Random Numbers

We explore here the stochastic behavior of the DECAL sensor's noise output, and we evaluate its potential application as a true random number generator (TRNG) using time series analysis. The main objectives are twofold: first, to characterize the intrinsic noise properties of the DECAL sensor in the absence of external stimuli, and second, to determine the feasibility of employing the sensor as a source of randomness. The collected sensor data are examined through statistical and time series methodologies, and subsequently modeled using an auto-regressive integrated moving average (ARIMA) process. This modeling approach enables the transformation of the sensor's raw noise into a Gaussian white noise sequence, which serves as the basis for generating random bits. The resulting random numbers are subjected to a series of statistical tests for randomness, including the NIST test suite. Our findings indicate that the method produces statistically sound random numbers. However, the rate of bit generation is relatively low, limiting its practicality for real-time TRNG applications under the current configuration. Despite this limitation, the results suggest that time series modeling presents a promising framework for extracting randomness from the DECAL sensor, and that with further optimization, the sensor could serve as a reliable and effective TRNG.

hep-ex↗

arXiv2023

Financial Fraud Detection using Quantum Graph Neural Networks

Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for new approaches to improve detection rates. In this paper, we propose a novel approach for detecting financial fraud using Quantum Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can process graph-structured data and leverage the power of Quantum Computing (QC) to perform computations more efficiently than classical neural networks. Our approach uses Variational Quantum Circuits (VQC) to enhance the performance of the QGNN. In order to evaluate the efficiency of our proposed method, we compared the performance of QGNNs to Classical Graph Neural Networks using a real-world financial fraud detection dataset. The results of our experiments showed that QGNNs achieved an AUC of $0.85$, which outperformed classical GNNs. Our research highlights the potential of QGNNs and suggests that QGNNs are a promising new approach for improving financial fraud detection.

quant-ph↗

arXiv2007

Enhancing the spin-transfer torque through proximity of quantum well states

We predict that the spin-transfer, $T_{i,||}$, and field-like, $T_{i,\bot}$, components of the {\it local} spin torque are dramatically enhanced in double-barrier magnetic tunnel junctions. The {\it spin-mixing} enhancement is due to the energetic proximity of majority and minority quantum well states (QWS) of different quantum numbers within the bias window. $T_{i,||}$ exhibits a switch-on and switch-off step-like bias behavior when spin polarized QWS enter the bias window or exit the energy band, while $T_{i,\bot}$, changes sign between switch-on biases. The {\it net} $T_{\bot}$ exhibits an anomalous angular behavior due to the bias interplay of the bilinear and biquadratic effective exchange couplings.

cond-mat.mes-hall↗

arXiv2006

Anomalous Bias Dependence of Spin Torque in Magnetic Tunnel Junctions

We predict an anomalous bias dependence of the spin transfer torque parallel to interface, $T_{||}$, in magnetic tunnel junctions (MTJ), which can be selectively tuned by the exchange splitting. It may exhibit a sign reversal {\it without} a corresponding sign reversal of the bias or even a quadratic bias dependence. We demonstrate that the underlying mechanism is the interplay of spin currents for the ferromagnetic (antiferromagnetic) configurations, which vary linearly (quadratically) with bias, respectively, due to the symmetric (asymmetric) nature of the barrier. The spin transfer torque perpendicular to interface exhibits a quadratic bias dependence.

cond-mat.other↗