Quantum Machine Learning in Finance: Time Series Forecasting

TL;DR

This paper investigates the use of parametrised quantum circuits as quantum neural networks for time series forecasting, demonstrating comparable or superior performance to classical models in noisy conditions, with faster training times.

Contribution

It introduces a novel application of quantum neural networks for time series forecasting and compares their performance against classical LSTM models under various noise levels.

Findings

PQCs perform similarly to BiLSTM with small noise

PQCs outperform BiLSTM with higher noise

Quantum models train faster than classical ones

Abstract

We explore the efficacy of the novel use of parametrised quantum circuits (PQCs) as quantum neural networks (QNNs) for forecasting time series signals with simulated quantum forward propagation. The temporal signals consist of several sinusoidal components (deterministic signal), blended together with trends and additive noise. The performance of the PQCs is compared against that of classical bidirectional long short-term memory (BiLSTM) neural networks. Our results show that for time series signals consisting of small amplitude noise variations (up to 40 per cent of the amplitude of the deterministic signal) PQCs, with only a few parameters, perform similar to classical BiLSTM networks, with thousands of parameters, and outperform them for signals with higher amplitude noise variations. Thus, QNNs can be used effectively to model time series having, at the same time, the significant advantage of being trained significantly faster than a classical machine learning model in a quantum computer.