arXiv subjects

Abdullah Al Shafin

Publications and source records attributed to Abdullah Al Shafin.

4 recordsLinked to original sources

arXiv2025

Geometric Latent Space Tomography with Metric-Preserving Autoencoders

Quantum state tomography faces exponential scaling with system size, while recent neural network approaches achieve polynomial scaling at the cost of losing the geometric structure of quantum state space. We introduce geometric latent space tomography, combining classical neural encoders with parameterized quantum circuit decoders trained via a metric-preservation loss that enforces proportionality between latent Euclidean distances and quantum Bures geodesics. On two-qubit mixed states with purity 0.85--0.95 representing NISQ-era decoherence, we achieve high-fidelity reconstruction (mean fidelity $F = 0.942 \pm 0.03$) with an interpretable 20-dimensional latent structure. Critically, latent geodesics exhibit strong linear correlation with Bures distances (Pearson $r = 0.88$, $R^2 = 0.78$), preserving 78\% of quantum metric structure. Geometric analysis reveals intrinsic manifold dimension 6.35 versus 20 ambient dimensions and measurable local curvature ($κ= 0.011 \pm 0.006$), confirming non-trivial Riemannian geometry with $O(d^2)$ computational advantage over $O(4^n)$ density matrix operations. Unlike prior neural tomography, our geometry-aware latent space enables direct state discrimination, fidelity estimation from Euclidean distances, and interpretable error manifolds for quantum error mitigation without repeated full tomography, providing critical capabilities for NISQ devices with limited coherence times.

quant-ph↗

arXiv2025

Quantum-Enhanced Attention Mechanism in NLP: A Hybrid Classical-Quantum Approach

Recent advances in quantum computing have opened new pathways for enhancing deep learning architectures, particularly in domains characterized by high-dimensional and context-rich data such as natural language processing (NLP). In this work, we present a hybrid classical-quantum Transformer model that integrates a quantum-enhanced attention mechanism into the standard classical architecture. By embedding token representations into a quantum Hilbert space via parameterized variational circuits and exploiting entanglement-aware kernel similarities, the model captures complex semantic relationships beyond the reach of conventional dot-product attention. We demonstrate the effectiveness of this approach across diverse NLP benchmarks, showing improvements in both efficiency and representational capacity. The results section reveal that the quantum attention layer yields globally coherent attention maps and more separable latent features, while requiring comparatively fewer parameters than classical counterparts. These findings highlight the potential of quantum-classical hybrid models to serve as a powerful and resource-efficient alternative to existing attention mechanisms in NLP.

cs.CL↗

arXiv2025

Quantum Token Obfuscation via Superposition: A Post-Quantum Security Framework Using Multi-Basis Verification and Entropy-Driven Evolution

Traditional cryptographic techniques, including token obfuscation, are increasingly vulnerable to quantum attacks due to advancements in quantum computing. Quantum algorithms such as Shor's and Grover's pose significant threats to classical security methods, necessitating quantum-resistant alternatives. This study proposes a quantum-based approach to token obfuscation that leverages superposition and multi-basis verification to enhance security against quantum adversaries. Tokens are encoded in quantum superposition states, ensuring probabilistic concealment until measured. A multi-basis verification protocol strengthens authentication by requiring validation across multiple quantum measurement bases. Additionally, a quantum decay protocol and token refresh mechanism dynamically manage the token lifecycle to prevent prolonged exposure and replay attacks. The model was tested through quantum simulations, evaluating entropy quality, adversarial robustness, and token verification reliability. Experimental validation demonstrates an entropy quality score of 0.9996, a 0% attack success rate across five adversarial models, and a 67% false positive rate, indicating strict security constraints. These findings confirm the effectiveness of quantum-based token obfuscation in preventing unauthorized reconstruction. The proposed approach provides a foundation for post-quantum cryptographic security by integrating entropy-driven state transformations, dynamic token evolution, and multi-basis verification. Future work will focus on optimizing computational efficiency and testing real-world implementations on quantum hardware.

quant-ph↗

arXiv2024

Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized gate selection, and the integration of the quantum layer with classical neural network components. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.

quant-ph↗