发表机构
New York University Abu Dhabi; University of Southern Denmark（纽约大学阿布扎比分校; 南丹麦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Q-PhotoMarket提出光子混合量子神经网络设计空间探索框架，探索超5000种配置，结合贝叶斯优化与阈值校准，在多个金融市场中识别稳健高性能架构，性能媲美经典基线。
AI 中文摘要
光子量子计算因其线性光学电路的天然实现以及玻色子采样的计算复杂性，近期已成为混合量子机器学习的一个有前景的平台。然而，尽管量子方法在金融领域引起了越来越多的兴趣，光子电路设计选择对预测性能的影响在很大程度上仍未得到探索。现有研究通常评估单一架构，使得更广泛的光子设计空间未被考察。在本工作中，我们提出了Q-PhotoMarket，一个系统性的设计空间探索（DSE）框架，用于应用于金融市场预测的光子混合量子神经网络（HQNN）。我们在美国、印度和加密货币市场中，跨越输入光子态、电路架构、纠缠模型和测量策略及其兼容的计算空间，探索了超过5000种有效的光子配置。为了提高搜索效率，穷举探索辅以贝叶斯优化。我们进一步引入阈值校准和预测崩溃诊断，以便在日益不平衡的回报阈值下进行可靠评估。实验结果表明，对超过5000种光子HQNN配置的系统性探索揭示了跨金融市场的架构一致性模式，识别出稳健的高性能设计，并展示了相对于经典机器学习基线的竞争性能。
英文摘要
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.
CommentsTo appear at the IEEE International Conference on Quantum Artificial Intelligence (QAI), Nottingham, UK, December 2026
