对比量子与经典模型在短信流量预测中的表现
Benchmarking Quantum and Classical Sequential Models for Urban Telecommunication Forecasting
- 用六种序列模型预测10分钟内短信量，测试不同历史长度影响
- 量子模型在部分长度下表现更好，但无普适优势
- 适合关注量子计算与时间序列结合的研究者参考
本研究评估了经典与量子启发的序列模型在米兰电信活动数据集上的单变量短信入站（SMS-in）时间序列预测性能。由于数据完整性限制，仅针对每个空间网格单元的短信入站信号进行分析。比较了五种模型：LSTM（基线）、量子LSTM（QLSTM）、量子自适应自注意力（QASA）、量子接收权重键值（QRWKV）和量子快速权重编程器（QFWP），在输入序列长度为4、8、12、16、32和64下的表现。所有模型均基于给定窗口内的历史值，预测下一个10分钟的短信入站数量。结果表明，不同模型对序列长度敏感性各异，说明量子增强并非普遍有效。量子模块的实际效果高度依赖任务特性与架构设计，反映出模型规模、参数化策略与时间建模能力之间的内在权衡。
原文摘要 · Abstract (English)
In this study, we evaluate the performance of classical and quantum-inspired sequential models in forecasting univariate time series of incoming SMS activity (SMS-in) using the Milan Telecommunication Activity Dataset. Due to data completeness limitations, we focus exclusively on the SMS-in signal for each spatial grid cell. We compare five models, LSTM (baseline), Quantum LSTM (QLSTM), Quantum Adaptive Self-Attention (QASA), Quantum Receptance Weighted Key-Value (QRWKV), and Quantum Fast Weight Programmers (QFWP), under varying input sequence lengths (4, 8, 12, 16, 32 and 64). All models are trained to predict the next 10-minute SMS-in value based solely on historical values within a given sequence window. Our findings indicate that different models exhibit varying sensitivities to sequence length, suggesting that quantum enhancements are not universally advantageous. Rather, the effectiveness of quantum modules is highly dependent on the specific task and architectural design, reflecting inherent trade-offs among model size, parameterization strategies, and temporal modeling capabilities.
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