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Owais Ishtiaq Siddiqui

Publications and source records attributed to Owais Ishtiaq Siddiqui.

3 recordsLinked to original sources

QIAP: A Quantum-Inspired Authentication Protocol with BB84-Based Channel Monitoring

Authentication in quantum-aware communication settings requires mechanisms that verify user legitimacy while also assessing whether the communication channel exhibits excessive disturbance. This paper presents QIAP, a stateful hybrid framework that combines classical commitment-based challenge-response authentication with BB84-based quantum-channel monitoring. QIAP uses a dual-branch verification structure inspired by rectilinear and diagonal basis selection. In the rectilinear branch, the prover opens a hash-based commitment and the verifier checks consistency with the registered round token. In the diagonal branch, the prover returns an HMAC tag bound to the verifier nonce, round identifier, and submitted commitment without disclosing the round-specific sub-secret. Under the stated hash, HMAC, freshness, secure-registration, protected-storage, and high-entropy-secret assumptions, QIAP provides computational resistance to the modeled wrong-secret and replay attempts and protects the long-term master secret from direct recovery. After successful authentication, the BB84 module estimates the quantum bit error rate (QBER) and aborts the session when the measured value reaches or exceeds the configured threshold. In the reported simulation, no successful authentication was observed among 1,000 tested wrong-secret attempts per branch. The baseline and full intercept-resend conditions produced mean QBER values of approximately 0.02 and 0.25, respectively. The classical authentication stage required approximately 0.6 ms, and the implemented authentication messages required approximately 113 bytes per round. These results indicate that QIAP can provide a lightweight hybrid framework for combining secret-based identity verification with QBER-based channel-disturbance assessment under the stated threat model.

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Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection

Quantum Machine Learning (QML) has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling. However, its practical implementation faces challenges, including limited quantum hardware and the complexity of integrating quantum algorithms with classical systems. One critical challenge is handling imbalanced datasets, where rare events are often misclassified due to skewed data distributions. Quantum Bayesian Networks (QBNs) address this issue by enhancing feature extraction and improving the classification of rare events such as oil spills. This paper introduces a Bayesian approach utilizing QBNs to classify satellite-derived imbalanced datasets, distinguishing ``oil-spill'' from ``non-spill'' regions. QBNs leverage probabilistic reasoning and quantum state preparation to integrate quantum enhancements into classical machine learning architectures. Our approach achieves a 0.99 AUC score, demonstrating its efficacy in anomaly detection and advancing precise environmental monitoring and management. While integration enhances classification performance, dataset-specific challenges require further optimization.

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Quantum State Tomography using Quantum Machine Learning

Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which makes them impractical for large-scale quantum systems. To overcome this challenge, we propose the integration of Quantum Machine Learning (QML) techniques to enhance the efficiency of QST. In this paper, we conduct a comprehensive investigation into various approaches for QST, encompassing both classical and quantum methodologies; We also implement different QML approaches for QST and demonstrate their effectiveness on various simulated and experimental quantum systems, including multi-qubit networks. Our results show that our QML-based QST approach can achieve high fidelity (98%) with significantly fewer measurements than conventional methods, making it a promising tool for practical QIP applications.

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