Benchmarking Quantum and Classical Sequential Models for Urban Telecommunication Forecasting

TL;DR

This paper compares classical and quantum-inspired models for forecasting telecommunication activity, revealing that quantum enhancements' effectiveness varies with task specifics and model design, highlighting complex trade-offs.

Contribution

It provides a comparative analysis of quantum and classical sequential models in telecommunication forecasting, emphasizing the nuanced impact of quantum modules on model performance.

Findings

Quantum model performance varies with sequence length

Quantum enhancements are not universally superior

Model effectiveness depends on task and architecture

Abstract

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.