Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction

2026-01-07

arXiv.org

AI Summary

This study addresses the lack of systematic, reproducible comparisons between quantum machine learning (QML) and classical methods in financial forecasting. We establish a standardized benchmarking framework that evaluates architecturally matched quantum and classical models under identical data splits, features, and evaluation metrics across three tasks: stock return direction prediction, simulated trading performance, and volatility forecasting. Our experiments include hybrid quantum neural networks, quantum LSTM (QLSTM), and angle-encoded quantum support vector regression (QSVR), compared against parameter-matched classical ANN, LSTM, and SVR counterparts. Results show that QML achieves 3.8% and 4.9% higher AUC on AAPL and KCHOL direction prediction, respectively; QLSTM yields superior risk-adjusted returns in half of the S&P 500 market regimes; and QSVR attains the lowest QLIKE error on KCHOL, revealing that QML can outperform classical models when data structure and quantum circuit design are suitably aligned.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial tasks: (i) directional return prediction on U.S. and Turkish equities, (ii) live-trading simulation with Quantum LSTMs versus classical LSTMs on the S\&P 500, and (iii) realized volatility forecasting using Quantum Support Vector Regression. By standardizing data splits, features, and evaluation metrics, our study provides a fair assessment of when current-generation QML models can match or exceed classical methods. Our results reveal that quantum approaches show performance gains when data structure and circuit design are well aligned. In directional classification, hybrid quantum neural networks surpass the parameter-matched ANN by \textbf{+3.8 AUC} and \textbf{+3.4 accuracy points} on \texttt{AAPL} stock and by \textbf{+4.9 AUC} and \textbf{+3.6 accuracy points} on Turkish stock \texttt{KCHOL}. In live trading, the QLSTM achieves higher risk-adjusted returns in \textbf{two of four} S\&P~500 regimes. For volatility forecasting, an angle-encoded QSVR attains the \textbf{lowest QLIKE} on \texttt{KCHOL} and remains within $\sim$0.02-0.04 QLIKE of the best classical kernels on \texttt{S\&P~500} and \texttt{AAPL}. Our benchmarking framework clearly identifies the scenarios where current QML architectures offer tangible improvements and where established classical methods continue to dominate.

Problem

• Quantum Machine Learning
• Classical Machine Learning
• Financial Prediction
• Benchmarking
• Performance Comparison

Innovation

• Quantum Machine Learning
• Benchmarking Framework
• Financial Prediction
• Hybrid Quantum Neural Networks
• Quantum LSTM