Quantum mixture-density network for multimodal probabilistic prediction

• URL: http://arxiv.org/abs/2506.09497v1
• Date: Wed, 11 Jun 2025 08:13:29 GMT
• Title: Quantum mixture-density network for multimodal probabilistic prediction
• Authors: Jaemin Seo,
• Abstract summary: Multimodal probability distributions are common in both quantum and classical systems.<n>We introduce a Quantum Mixture-Density Network (Q-MDN) that employs parameterized quantum circuits to efficiently model multimodal distributions.
• Score: 1.6317061277457001
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal probability distributions are common in both quantum and classical systems, yet modeling them remains challenging when the number of modes is large or unknown. Classical methods such as mixture-density networks (MDNs) scale poorly, requiring parameter counts that grow quadratically with the number of modes. We introduce a Quantum Mixture-Density Network (Q-MDN) that employs parameterized quantum circuits to efficiently model multimodal distributions. By representing an exponential number of modes with a compact set of qubits and parameters, Q-MDN predicts Gaussian mixture components with high resolution. We evaluate Q-MDN on two benchmark tasks: the quantum double-slit experiment and chaotic logistic bifurcation. In both cases, Q-MDN outperforms classical MDNs in mode separability and prediction sharpness under equal parameter budgets. Our results demonstrate a practical quantum advantage in probabilistic regression and highlight the potential of quantum machine learning in capturing complex stochastic behavior beyond the reach of classical models.

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