Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Prediction

2026-10-07

AI Summary

This study addresses the unclear impact of photonic quantum circuit design choices on financial forecasting performance by constructing a systematic design space exploration framework. Methodologically, it presents the first evaluation of over 5,000 photonic hybrid quantum neural network configurations, integrating Bayesian optimization to enhance search efficiency and introducing a threshold calibration diagnostic mechanism to improve robustness. The research reveals cross-market robust architectural patterns and high-performance design paradigms, achieving predictive accuracy comparable to classical machine learning baselines. Ultimately, this work establishes critical design guidelines for applying photonic quantum computing in the financial domain.

Abstract

Photonic quantum computing has recently emerged as a promising platform for hybrid quantum machine learning due to its native realization of linear-optical circuits and the computational complexity of boson sampling. However, despite growing interest in quantum methods for finance, the influence of photonic circuit design choices on predictive performance remains largely unexplored. Existing studies typically evaluate a single architecture, leaving the broader photonic design space unexamined. In this work, we present Q-PhotoMarket, a systematic design space exploration (DSE) framework for photonic hybrid quantum neural networks (HQNNs) applied to financial market prediction. We explore over 5,000 valid photonic configurations spanning input photon states, circuit architectures, entangling models, and measurement strategies across their compatible computation spaces, for U.S., Indian, and cryptocurrency markets. To improve search efficiency, the exhaustive exploration is complemented with Bayesian optimization. We further incorporate threshold calibration and prediction-collapse diagnostics to enable reliable evaluation under increasingly imbalanced return thresholds. Experimental results show that systematic exploration of more than 5,000 photonic HQNN configurations reveals consistent architectural patterns across financial markets, identifies robust high-performing designs, and demonstrates competitive performance relative to classical machine learning baselines.

Problem

• Photonic Quantum Computing
• Hybrid Quantum Neural Networks
• Financial Market Prediction
• Design Space Exploration

Innovation

• Photonic Hybrid Quantum Neural Networks
• Design Space Exploration
• Bayesian Optimization
• Financial Market Prediction
• Threshold Calibration