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FinteQ — Photonic Quantum Kernel for Swaption IVS Forecasting

Quandela Swaptions QML Hackathon 2026 · Perceval · MerLin · QPU-Compatible

What We Did

Implemented a fidelity-based photonic quantum kernel (Yin et al. 2024) on Perceval's SLOS backend for swaption implied volatility surface forecasting (494 training days → 6 predictions, 224-dimensional surface).

Core contribution: first indistinguishability sweep on financial time series — varying photon indistinguishability 0.0→1.0 to measure Hong-Ou-Mandel interference effects on prediction. Result: strictly monotonic degradation. Quantum interference measurably alters kernel geometry (diff norm=4.97) but hurts on linear data.

Results

Model
Rolling naive (true ceiling) 0.9981
Classical kernel (indist=0.0) 0.9981
Quantum kernel (indist=1.0) ★ 0.9960
QFinger Hybrid L2 (CV) 0.9997

Why Quantum Doesn't Win Here

The surface lives on a near-linear 3D manifold (PCA(3)=99.96%, autocorr R=0.9999). Complex permanents obscure linear geometry rather than enriching it. 19 ablation experiments confirm — memory saturates at depth 1, chaos scale flat, residuals white noise.

Pre-screening criterion: if PCA(3) > 99% and residual autocorr < 0.05, quantum advantage is unlikely.

Install

pip install perceval-quandela merlinquantum numpy pandas scikit-learn openpyxl pyarrow

Run

# Level-1 photonic quantum kernel submission script
python src/level1_primary/level1_01_quantum_kernel_submission.py

# Level-1 baseline validation
python src/level1_primary/level1_02_baseline_validation.py

# Level-2 hybrid model (secondary track)
python src/level2_secondary/hybrid_model.py

Structure

├── .gitignore
├── Final_Report.pdf
├── README.md
├── requirements.txt
├── docs/
│   └── references.md
├── graphs/
│   ├── experiment_findings_table.png
│   └── quantum_interference_linear_manifolds.png
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   └── 02_level2_step_by_step.ipynb
├── predictions/
│   ├── level_1_prediction.xlsx
│   └── level_2_prediction.xlsx
└── src/
    ├── level1_primary/
    │   ├── level1_01_quantum_kernel_submission.py
    │   └── level1_02_baseline_validation.py
    └── level2_secondary/
        ├── hybrid_model.py
        └── qml_extension.py

References

All references are listed in docs/references.md.

Presentation Video

Watch our project presentation here: Google Drive Video.


Team FinteQ · Quandela 2026 · 19 experiments

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Option Pricing in Finance using Quantum Machine Learning

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