Quantum-Powered Time Series Forecasting in Finance: Replication, Reliability, and Architectural Exploration

About

Machine learning has enabled computers to learn from data and improve their performance on tasks, revolutionizing various industries by automating processes, uncovering insights, and enhancing decision-making.

NISQ (Noisy Intermediate-Scale Quantum) refers to the current stage of quantum computing, characterized by quantum devices with a moderate number of qubits and significant noise. In the NISQ era, parametrized quantum circuits (PQC) are employed in machine learning as flexible quantum algorithms that can be executed on current noisy quantum devices. By introducing tunable parameters, these circuits can adapt to the limitations of NISQ devices, enabling quantum-enhanced solutions to classification and generative tasks while paving the way for practical applications of quantum machine learning.

Time series arise from the abundance of sequential data in various fields, and time series analysis uncovers patterns, trends, and dependencies within the temporal data, enabling informed decision-making and predictions for a wide range of applications.

In this project, we aim to replicate the findings of a paper that utilized parametrized quantum circuits (PQCs) to forecast time series data in the context of finance. By faithfully reproducing the original experiments, we seek to establish the reliability and applicability of PQCs for financial time series predictions.

Building upon these results, we further investigate the impact of hyperparameters and explore novel circuit architectures to optimize the performance of quantum machine learning in time series forecasting..

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