Paper
Hybrid Photonic Quantum Reservoir Computing for High-Dimensional Financial Surface Prediction
Authors
Fyodor Amanov, Azamkhon Azamov
Abstract
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.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.10707v1</id>\n <title>Hybrid Photonic Quantum Reservoir Computing for High-Dimensional Financial Surface Prediction</title>\n <updated>2026-03-11T12:26:16Z</updated>\n <link href='https://arxiv.org/abs/2603.10707v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.10707v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='quant-ph'/>\n <published>2026-03-11T12:26:16Z</published>\n <arxiv:comment>19 pages, 10 figures</arxiv:comment>\n <arxiv:primary_category term='quant-ph'/>\n <author>\n <name>Fyodor Amanov</name>\n <arxiv:affiliation>QuanTech</arxiv:affiliation>\n <arxiv:affiliation>New Uzbekistan University</arxiv:affiliation>\n </author>\n <author>\n <name>Azamkhon Azamov</name>\n <arxiv:affiliation>QuanTech</arxiv:affiliation>\n <arxiv:affiliation>New Uzbekistan University</arxiv:affiliation>\n </author>\n </entry>"
}