Quantum-Optimized Selective State Space Model for Efficient Time Series Prediction

• URL: http://arxiv.org/abs/2509.00259v1
• Date: Fri, 29 Aug 2025 22:00:48 GMT
• Title: Quantum-Optimized Selective State Space Model for Efficient Time Series Prediction
• Authors: Stefan-Alexandru Jura, Mihai Udrescu, Alexandru Topirceanu,
• Abstract summary: We propose a hybrid quantum-optimized approach that integrates state space dynamics with a variational quantum gate.<n>We empirically validate Q-SSM on three widely used benchmarks, i.e., ETT, Traffic, and Exchange Rate.<n>Results show that Q-SSM consistently improves over strong baselines.
• Score: 39.146761527401424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-range time series forecasting remains challenging, as it requires capturing non-stationary and multi-scale temporal dependencies while maintaining noise robustness, efficiency, and stability. Transformer-based architectures such as Autoformer and Informer improve generalization but suffer from quadratic complexity and degraded performance on very long time horizons. State space models, notably S-Mamba, provide linear-time updates but often face unstable training dynamics, sensitivity to initialization, and limited robustness for multivariate forecasting. To address such challenges, we propose the Quantum-Optimized Selective State Space Model (Q-SSM), a hybrid quantum-optimized approach that integrates state space dynamics with a variational quantum gate. Instead of relying on expensive attention mechanisms, Q-SSM employs a simple parametrized quantum circuit (RY-RX ansatz) whose expectation values regulate memory updates adaptively. This quantum gating mechanism improves convergence stability, enhances the modeling of long-term dependencies, and provides a lightweight alternative to attention. We empirically validate Q-SSM on three widely used benchmarks, i.e., ETT, Traffic, and Exchange Rate. Results show that Q-SSM consistently improves over strong baselines (LSTM, TCN, Reformer), Transformer-based models, and S-Mamba. These findings demonstrate that variational quantum gating can address current limitations in long-range forecasting, leading to accurate and robust multivariate predictions.

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