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Dimitris Syvridis

Publications and source records attributed to Dimitris Syvridis.

10 recordsLinked to original sources
arXiv2026

A Scalable Cloud-Orchestrated and Service-Oriented Multi-Domain QKD Network with PQC Integration

Quantum key distribution (QKD) offers unconditional security but existing QKD networks remain difficult to scale across heterogeneous infrastructures and administrative domains due to vendor-specific interfaces, trusted-node constraints, and limited interoperability. This work presents a flexible multi-domain and multi-site quantum-secure network architecture integrating vendor-agnostic QKD, SDN orchestration, and cloud-managed trust services. Communication is based on Zero Trust Network Access protocols featuring multi-level authentication mechanisms building upon post-quantum cryptography (PQC) signature and key encapsulation algorithms. The system is deployed on a real-world testbed with domains incorporating QKD nodes from 3 vendors, as well as domains without QKD infrastructure elements. Experimental results show that PQC and SDN overhead remain relatively low even on constrained devices, with the main bottleneck being QKD key retrieval and vendor-specific key streaming limitations. The proposed framework extends quantum-safe key transport beyond native QKD boundaries while preserving flexibility, interoperability, and compatibility with existing infrastructures.

cs.CR↗
arXiv2026

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited. Using the EuroSAT-MS dataset, we perform a systematic one-vs-one evaluation across all class pairs under a controlled experimental protocol, comparing classical baselines (logistic regression, SVMs, neural networks) with VQCs employing both linear readout and quantum-kernel SVM strategies. Our results show that, while VQCs with linear readout do not outperform strong classical baselines such as RBF-SVM, the same trained quantum feature map can significantly improve performance when reused within a kernel-based decision framework. A qubit-count sweep further reveals saturation effects consistent with the mismatch between exponential Hilbert space dimension and linear parameter scaling. Overall, our findings highlight that the effectiveness of quantum models depends critically on the interplay between representation and readout, and that meaningful gains may arise from combining learned quantum feature maps with classical decision mechanisms rather than seeking direct replacement of classical models.

quant-ph↗
arXiv2026

Quantum-Inspired Unitary Pooling for Multispectral Satellite Image Classification

Multispectral satellite imagery poses significant challenges for deep learning models due to the high dimensionality of spectral data and the presence of structured correlations across channels. Recent work in quantum machine learning suggests that unitary evolutions and Hilbert-space embeddings can introduce useful inductive biases for learning. In this work, we show that several empirical advantages often attributed to quantum feature maps can be more precisely understood as consequences of geometric structure induced by unitary group actions and the associated quotient symmetries. Motivated by this observation, we introduce a fully classical pooling mechanism that maps latent features to complex projective space via a fixed-reference unitary action. This construction effectively collapses non-identifiable degrees of freedom, leading to a reduction in the dimensionality of the learned representations. Empirical results on multispectral satellite imagery show that incorporating this quantum-inspired pooling operation into a convolutional neural network improves optimization stability, accelerates convergence, and substantially reduces variance compared to standard pooling baselines. These results clarify the role of geometric structure in quantum-inspired architectures and demonstrate that their benefits can be reproduced through principled geometric inductive biases implemented entirely within classical deep learning models.

quant-ph↗
arXiv2025

Feature Ranking in Credit-Risk with Qudit-Based Networks

In finance, predictive models must balance accuracy and interpretability, particularly in credit risk assessment, where model decisions carry material consequences. We present a quantum neural network (QNN) based on a single qudit, in which both data features and trainable parameters are co-encoded within a unified unitary evolution generated by the full Lie algebra. This design explores the entire Hilbert space while enabling interpretability through the magnitudes of the learned coefficients. We benchmark our model on a real-world, imbalanced credit-risk dataset from Taiwan. The proposed QNN consistently outperforms LR and reaches the results of random forest models in macro-F1 score while preserving a transparent correspondence between learned parameters and input feature importance. To quantify the interpretability of the proposed model, we introduce two complementary metrics: (i) the edit distance between the model's feature ranking and that of LR, and (ii) a feature-poisoning test where selected features are replaced with noise. Results indicate that the proposed quantum model achieves competitive performance while offering a tractable path toward interpretable quantum learning.

quant-ph↗
arXiv2025

Evaluating Relayed and Switched Quantum Key Distribution (QKD) Network Architectures

We evaluate the performance of two architectures for network-wide quantum key distribution (QKD): Relayed QKD, which relays keys over multi-link QKD paths for non-adjacent nodes, and Switched QKD, which uses optical switches to dynamically connect arbitrary QKD modules to form direct QKD links between them. An advantage of Switched QKD is that it distributes quantum keys end-to-end, whereas Relayed relies on trusted nodes. However, Switched depends on arbitrary matching of QKD modules. We first experimentally evaluate the performance of commercial DV-QKD modules; for each of three vendors we benchmark the performance in standard/matched module pairs and in unmatched pairs to emulate configurations in the Switched QKD network architecture. The analysis reveals that in some cases a notable variation in the generated secret key rate (SKR) between the matched and unmatched pairs is observed. Driven by these experimental findings, we conduct a comprehensive theoretical analysis that evaluates the network-wide performance of the two architectures. Our analysis is based on uniform ring networks, where we derive optimal key management configurations and analytical formulas for the achievable consumed SKR. We compare network performance under varying ring sizes, QKD link losses, QKD receivers' sensitivity and performance penalties of unmatched modules. Our findings indicate that Switched QKD performs better in dense rings (short distances, large node counts), while Relayed QKD is more effective in longer distances and large node counts. Moreover, we confirm that unmatched QKD modules penalties significantly impact the efficiency of Switched QKD architecture.

cs.CR↗
arXiv2025

A Scalable Framework for Post-Quantum Authentication in Public Key Infrastructures

This work explores the performance and scalability of a hierarchical certificate authority framework with automated certificate issuance employing post-quantum cryptographic (PQC) signature algorithms. The system is designed for compatibility with both classical and PQC algorithms, promoting crypto-agility while ensuring robust security against quantum-based threats. The proposed framework design expects minimal cryptographic requirements from potential clients, protects certificates of high importance against cross-dependent chains-of-trust and allows for prompt switching between classical and PQC algorithms. Finally, we evaluate SPHINCS$^+$, Falcon, and Dilithium variants in various configurations of certificate issuance and verification accommodating a large client base, underlining the trade-offs in balancing performance, scalability, and security.

cs.CR↗
arXiv2021

Static Skew Compensation in Multi Core Radio over Fiber systems for 5G Mmwave Beamforming

Multicore fibers can be used for Radio over Fiber transmission of mmwave signals for phased array antennas in 5G networks. The inter-core skew of these fibers distort the radiation pattern. We propose an efficient method to compensate the differential delays, without full equalization of the transmission path lengths, reducing the power loss and complexity.

physics.app-ph↗
arXiv2021

Compensation of Multicore Fiber Skew Effects for Radio over Fiber mmWave Antenna Beamforming

In 5G networks, a Radio over Fiber architecture utilizing multicore fibers can be adopted for the transmission of mmwave signals feeding phased array antennas. The mmwave signals undergo phase shifts imposed by optical true time delay networks, to provide squint free beams. Multicore fibers are used to transfer the phase shifted optical signals. However, the intercore static skew of these fibers, if not compensated, distorts the radiation pattern. We propose an efficient method to compensate the differential delays, without full equalization of the transmission path lengths, reducing the power loss and complexity. Statistical analysis shows that regardless of the skew distribution, the frequency response can be estimated with respect to the rms skew delays. Simulation analysis of the complete Radio over Fiber and RF link validates the method.

physics.app-ph↗
arXiv2020

Photonic Pseudo-Random Number Generator for Internet-of-Things Authentication using a Waveguide based Physical Unclonable Function

In this paper we experimentally evaluate a physical unclonable function based on a polymer optical waveguide, as a time-invariant, replication-resilient, source of entropy. The elevated physical unclonability of our implementation is combined with spatial light modulation and post processing techniques, thus allowing the deterministic generation of an exponentially large pool of unpredictable responses. The quality of the generated numbers is validated through NIST/DIEHARD(ER) suites, whereas the overall security of the scheme is benchmarked assuming attackers with elevated privileges in terms of system access. Finally, based on the demonstrated key features, we present and analyze a mutual authentication implementation scenario which is fully compatible with state-of-the-art commercial Internet-Of-Things architectures

cs.CR↗