🏦 Banco Santander Quantum Credit Challenge - Solution Setup

This repository contains a Hybrid Quantum-Classical Machine Learning solution for the Banco Santander Quantum Credit Risk Challenge.

Our solution implements three quantum architectures (QSVC, VQC, QNN) using the Qrisp framework and compares them against a classical XGBoost baseline.

📊 Grand Benchmark Results

Performance Comparison - Simulation Results (N=100 samples)

Note: Due to credit constraints, full training was performed on simulator, with hardware validation for key trajectory steps.

Model	Architecture	Accuracy	AUC
XGBoost	Classical (Gradient Boosting)	91.5%	91.3%
QSVC	Quantum Kernel (Fidelity)	80.0%	N/A
QNN	Data Re-uploading	80.0%	65.0%
VQC	Hardware-Efficient Ansatz	72.0%	72.6%

🧠 Scientific Analysis

1. Rigorous Evaluation (70/30 Split)

To ensure Technical Quality, we implemented a strict 70/30 Train/Test split BEFORE any processing.

• PCA and Scalers are fit only on the Training set.
• This prevents "Data Leakage" (information from the test set influencing the training features), a common pitfall in ML competitions. Our results represent true generalization performance.

2. The Impact of Qubit Count (Validation)

As shown in the PCA Variance Analysis plot (above right):

• 5 Components (Vertical Red Line) capture the "knee" of the curve.
• Moving to 10 qubits yields diminishing returns in variance explained, but exponentially increases hardware noise.
• Thus, 5 Qubits is the empirically validated optimal architecture for this dataset on NISQ hardware.

3. Why did QSVC & QNN perform best?

The QSVC (Quantum Kernel) and QNN (Re-uploading) achieved 80% accuracy.

• QSVC: Uses a convex optimization landscape (SVM), guaranteeing a global optimum for the kernel boundary.
• QNN: The "Re-uploading" strategy allows a single qubit to process multiple features sequentially, increasing the effective dimensionality and expressivity beyond the physical qubit count.

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🚀 Quick Start

1. Prerequisites

• Python 3.9+
• An IQM Resonance API Token (Optional, for hardware execution)

2. Installation

Create a virtual environment and update pip:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# MacOS Users: brew install libomp (for XGBoost)

3. Running the Comparison (Simulation)

This script trains all 4 models using a simulaton backend.

python3 src/run_comparison.py

Pipeline Steps:

1. Data Loading: Loads credit_risk_dataset_red.csv (3000 samples).
2. Preprocessing:
   • Classical: Imputation + OneHotEncoding.
   • Quantum: StandardScaling $\to$ PCA(n=5) $\to$ MinMax(0, $\pi$).
3. Training: Runs XGBoost, VQC, QNN, and QSVC.
4. Output: grand_benchmark.png and console metrics.

4. Running on IQM Garnet (Hardware)

To run on real quantum hardware:

1. Create a .env file (or set environment variables):

   cp .env.example .env
   # Edit .env and paste your IQM_TOKEN

2. Run with the Garnet backend:

   export QUANTUM_BACKEND="garnet"
   python src/run_comparison.py

The code automatically detects the backend variable and switches from the simulator to the DirectIQMBackend (Garnet 20q).

📁 Repository Structure

• src/run_comparison.py: Main orchestration script.
• src/quantum_model.py: Qrisp implementations of VQC, QNN, and QSVC.
• credit_risk_dataset_red.csv: Dataset.
• grand_benchmark.png: Results plot.

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The quantum implementation follows a more sophisticated pipeline: Raw Data → OneHotEncoder → StandardScaler → PCA → MinMaxScaler(0,π)

🧪 Alternative Implementation & Validation (Track B)

To strictly validate our approach, we ran a parallel validation track using Qrisp on the IQM Sirius simulator. This track focused on solving the critical Class Imbalance problem inherent in credit risk (Defaults are rare).

📄 See Full Validation Notebook: Validation_Track_B.ipynb

The "Accuracy Paradox"

Standard models achieve high accuracy (80%) by simply predicting "No Default" (Class 0) for almost everyone. This is useless for a bank.

We implemented a Weighted MSE Cost Function to penalize missing a Default (Class 1) more than a false alarm.

📊 Comparative Results (Sim-to-Real Transfer)

Metric	Without Weighting (Baseline)	With Weighted MSE (Ours)	Impact
Accuracy	79.9%	71.6%	-8.3% (Trade-off)
ROC-AUC	0.67	0.75	+11.9% (Better Separation)
Class 1 Recall	17%	69%	4x Improvement 🚀
Class 1 F1	0.26	0.52	2x Improvement 🚀

Visual Proof:

Left: Unweighted (High Accuracy, Low Recall). Right: Weighted (Balanced, High Recall).

Conclusion: Our "71.5% Accuracy" model is actually far superior for the business use case because it actually captures risk (69% Recall vs 17%).

• Result: Achieved 71.5% Accuracy on real hardware (900 samples), validating that our models' performance is not a simulation artifact.