arXiv：cs.LG· Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan, Muhammad Kashif, Muhammad Shafique·· 6 小时前AI 评分33
QLIF-CAST：量子漏电积分点火模型用于时间序列天气预报
QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting
AI 导读
研究者将量子漏电积分点火（QLIF）脉冲神经网络扩展至时间序列回归，提出 QLIF-CAST 模型，用于短期多变量天气预报。该模型在参数匹配对比中较经典 LIF 基线 MSE 低 15.4%、MAE 低 4.4%，与 QLSTM 和 QNN 相比训练收敛时间最多减少 94%。
正文
Abstract:Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings. This work adapts the Quantum Leaky Integrate-and-Fire (QLIF) spiking neural network for time-series regression tasks, specifically short-term multivariate weather forecasting. We extend QLIF beyond classification and demonstrate its applicability to continuous-valued prediction problems. The QLIF-CAST model encodes neuron excitation states as single-qubit quantum superpositions, driven by R_x rotation gates and T1 relaxation decay, and is embedded within a hybrid quantum-classical recurrent architecture. We conduct two distinct evaluations. First, a controlled comparison against a parameter-matched classical LIF baseline on a multivariate weather dataset shows that QLIF-CAST achieves 15.4% lower MSE and 4.4% lower MAE, demonstrating that quantum neuronal dynamics reduce prediction error over classical equivalents. Second, a cross-domain comparative analysis with state-of-the-art quantum LSTM (QLSTM) and quantum neural network (QNN) models on air quality and wind speed benchmarks reveals that QLIF-CAST converges in up to 94% less training time, occupying a distinct position in the speed-error trade-off space. Hardware verification on IBM Marrakesh (156-qubit QPU) confirms reliable circuit execution with only 1.2% average deviation from simulation.
| Comments: | To appear at the IEEE International Conference on Quantum Artificial Intelligence (QAI), Nottingham, UK, December 2026 |
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.18333 [quant-ph] |
| (or arXiv:2605.18333v3 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18333 arXiv-issued DOI via DataCite |
Submission history
From: Alberto Marchisio [view email]
[v1]
Mon, 18 May 2026 12:49:46 UTC (3,288 KB)
[v2]
Wed, 22 Jul 2026 06:18:37 UTC (4,084 KB)
[v3]
Wed, 7 Oct 2026 08:05:03 UTC (4,084 KB)
来源：arXiv：cs.LG · arxiv.org