Hybrid Quantum-Classical Ridgelet Neural Networks for Portfolio Optimization

TL;DR

This paper presents a novel hybrid quantum-classical neural network model using ridgelet transforms and QAOA for improved financial time-series forecasting and portfolio optimization.

Contribution

It introduces a Quantum Ridgelet Neural Network that combines ridgelet transforms, parametrized quantum circuits, and QAOA to enhance scalability and accuracy in financial modeling.

Findings

Ridgelet transforms improve feature extraction for quantum models.

The proposed model captures significant predictive signals in financial data.

QAOA effectively solves the portfolio optimization problem.

Abstract

In this study, we introduce a quantum computing method that incorporates Ridglet transforms into quantum processing pipelines for financial time-series forecasting with Quantum Approximate Optimization Algorithm (QAOA)-based portfolio optimization. We propose a Quantum Ridgelet Neural Network (QRNN) model for forecasting time-series data that integrates Parametrized Quantum Circuits (PQCs) with ridgelet-based feature transformations and QAOA-based portfolio optimization for asset selection. By breaking down financial time-series data into multi-resolution components, the ridgelet transform enables the identification of both local and global trends. Ridgelet-based features improve the scalability and accuracy of quantum computing by significantly reducing the number of qubits needed. However, the predicted results are turned into a QUBO-based mean-variance optimization problem and solved with QAOA to select the best stocks. Our study begins with a theoretical formulation of the single-qubit system for our proposed model. This formulation is further extended to a multi-qubit system, and we show that it captures a significant fraction of the predictive signal.