Hybrid Photonic Quantum Reservoir Computing for High-Dimensional Financial Surface Prediction
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918383414411264 |
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| author | Amanov, Fyodor Azamov, Azamkhon |
| author_facet | Amanov, Fyodor Azamov, Azamkhon |
| contents | We propose a hybrid photonic quantum reservoir computing (QRC) framework for swaption surface prediction. The pipeline compresses 224-dimensional surfaces to a 20-dimensional latent space via a sparse denoising autoencoder, extracts 1,215 Fock-basis features from an ensemble of three fixed photonic reservoirs, concatenates them with a 120-dimensional classical context, and maps the resulting 1,335-dimensional feature vector to predictions with Ridge regression. We benchmark against 10 classical and quantum baselines on six held-out trading days. Our approach achieves the lowest surface RMSE of~$0.0425$ while maintaining sub-millisecond inference. The quantum layer has zero trainable parameters, sidestepping barren plateaus entirely. Variational quantum methods (VQC, Quantum LSTM) yield negative $R^{2}$ on test data, confirming that fixed quantum feature extractors paired with regularised readouts are more viable for low-data financial applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_10707 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid Photonic Quantum Reservoir Computing for High-Dimensional Financial Surface Prediction Amanov, Fyodor Azamov, Azamkhon Quantum Physics We propose a hybrid photonic quantum reservoir computing (QRC) framework for swaption surface prediction. The pipeline compresses 224-dimensional surfaces to a 20-dimensional latent space via a sparse denoising autoencoder, extracts 1,215 Fock-basis features from an ensemble of three fixed photonic reservoirs, concatenates them with a 120-dimensional classical context, and maps the resulting 1,335-dimensional feature vector to predictions with Ridge regression. We benchmark against 10 classical and quantum baselines on six held-out trading days. Our approach achieves the lowest surface RMSE of~$0.0425$ while maintaining sub-millisecond inference. The quantum layer has zero trainable parameters, sidestepping barren plateaus entirely. Variational quantum methods (VQC, Quantum LSTM) yield negative $R^{2}$ on test data, confirming that fixed quantum feature extractors paired with regularised readouts are more viable for low-data financial applications. |
| title | Hybrid Photonic Quantum Reservoir Computing for High-Dimensional Financial Surface Prediction |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.10707 |