Quantum-Enhanced Generative Models for Rare Event Prediction

• URL: http://arxiv.org/abs/2511.02042v1
• Date: Mon, 03 Nov 2025 20:24:55 GMT
• Title: Quantum-Enhanced Generative Models for Rare Event Prediction
• Authors: M. Z. Haider, M. U. Ghouri, Tayyaba Noreen, M. Salman,
• Abstract summary: We propose the Quantum-Enhanced Generative Model (QEGM), a hybrid classical-quantum framework that integrates deep latent-variable models with variational quantum circuits.<n>We evaluate QEGM on synthetic Gaussian mixtures and real-world datasets spanning finance, climate, and protein structure.<n>Results demonstrate that QEGM reduces tail KL divergence by up to 50 percent compared to state-of-the-art baselines.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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