Tayyaba Noreen

M. Z. Haider, M. U. Ghouri, Tayyaba Noreen et al.

Rare events such as financial crashes, climate extremes, and biological anomalies are notoriously difficult to model due to their scarcity and heavy-tailed distributions. Classical deep generative models often struggle to capture these rare occurrences, either collapsing low-probability modes or producing poorly calibrated uncertainty estimates. In this work, we propose the Quantum-Enhanced Generative Model (QEGM), a hybrid classical-quantum framework that integrates deep latent-variable models with variational quantum circuits. The framework introduces two key innovations: (1) a hybrid loss function that jointly optimizes reconstruction fidelity and tail-aware likelihood, and (2) quantum randomness-driven noise injection to enhance sample diversity and mitigate mode collapse. Training proceeds via a hybrid loop where classical parameters are updated through backpropagation while quantum parameters are optimized using parameter-shift gradients. We evaluate QEGM on synthetic Gaussian mixtures and real-world datasets spanning finance, climate, and protein structure. Results demonstrate that QEGM reduces tail KL divergence by up to 50 percent compared to state-of-the-art baselines (GAN, VAE, Diffusion), while improving rare-event recall and coverage calibration. These findings highlight the potential of QEGM as a principled approach for rare-event prediction, offering robustness beyond what is achievable with purely classical methods.

M. Z. Haider, M. U Ghouri, Tayyaba Noreen et al.

Blockchain systems face persistent challenges of scalability, latency, and energy inefficiency. Existing consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) either consume excessive resources or risk centralization. This paper proposes \textit{Proof-of-Spiking-Neurons (PoSN)}, a neuromorphic consensus protocol inspired by spiking neural networks. PoSN encodes transactions as spike trains, elects leaders through competitive firing dynamics, and finalizes blocks via neural synchronization, enabling parallel and event-driven consensus with minimal energy overhead. A hybrid system architecture is implemented on neuromorphic platforms, supported by simulation frameworks such as Nengo and PyNN. Experimental results show significant gains in energy efficiency, throughput, and convergence compared to PoB and PoR. PoSN establishes a foundation for sustainable, adaptive blockchains suitable for IoT, edge, and large-scale distributed systems.

M. Z. Haider, Tayyaba Noreen, M. Salman

Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates opportunities for illicit activity, challenging regulators and exchanges in anti money laundering (AML) enforcement. Detecting fraudulent transactions in blockchain networks requires models that can capture both structural and temporal dependencies while remaining resilient to noise, imbalance, and adversarial behavior. In this work, we propose an ensemble framework that integrates Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Isomorphism Networks (GIN) to enhance blockchain fraud detection. Using the real-world Elliptic dataset, our tuned soft voting ensemble achieves high recall of illicit transactions while maintaining a false positive rate below 1%, beating individual GNN models and baseline methods. The modular architecture incorporates quantum-ready design hooks, allowing seamless future integration of quantum feature mappings and hybrid quantum classical graph neural networks. This ensures scalability, robustness, and long-term adaptability as quantum computing technologies mature. Our findings highlight ensemble GNNs as a practical and forward-looking solution for real-time cryptocurrency monitoring, providing both immediate AML utility and a pathway toward quantum-enhanced financial security analytics.