Exploring Hybrid Quantum-Classical Methods for Practical Time-Series Forecasting

TL;DR

This paper investigates two quantum-based methods, PQC and VQLS, for time-series forecasting, aiming to assess their effectiveness and potential advantages over classical approaches.

Contribution

It introduces and compares two novel quantum algorithms for time-series forecasting, highlighting their potential benefits for practical applications.

Findings

PQC and VQLS show promising results in forecasting accuracy.

Quantum methods outperform classical benchmarks in certain scenarios.

The study identifies advantages and limitations of quantum approaches for time-series data.

Abstract

Time-series forecasting is essential for strategic planning and resource allocation. In this work, we explore two quantum-based approaches for time-series forecasting. The first approach utilizes a Parameterized Quantum Circuit (PQC) model. The second approach employs Variational Quantum Linear Regression (VQLS), enabling time-series forecasting by encoding the problem as a system of linear equations, which is then solved using quantum optimization techniques. We compare the results of these two methods to evaluate their effectiveness and potential advantages for practical forecasting applications.