Qsvm Fraud Detection Eval

qsvm-fraud-detection-eval

Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection — Michele Grossi et al. (arXiv:2208.07963, 2022)

What this evaluates

Evaluates a quantum support vector machine (QSVM) with quantum feature selection for binary fraud detection on real-world card payment data. It probes the model's ability to identify fraudulent transactions using a balanced dataset and compares performance against classical feature selection baselines.

Datasets

• Real-world card payment data (Balanced Data Set) — total 2500; splits: train (1500), test (1000)

Metrics

• Accuracy (primary) — range: [0, 1]
  • Fraction of correctly classified test instances out of the total test instances.
• AUC — range: [0, 1]
  • Area under the Receiver Operating Characteristic curve, measuring the model's ability to distinguish between classes across all classification thresholds.

Input / output format

Input: Balanced card payment dataset features (e.g., F_15, F_42, etc.) with binary fraud labels.

Output: Binary class prediction (fraud vs. non-fraud) per transaction.

Scoring recipe

def compute_metrics(predictions, labels):
    accuracy = sum(p == l for p, l in zip(predictions, labels)) / len(labels)
    auc = roc_auc_score(labels, predictions)
    return accuracy, auc

Common pitfalls

• The paper reports results averaged over 5 random trials in the text but 6 trials in Table 6, creating ambiguity in the exact repetition count.
• Performance is highly sensitive to the quantum backend simulation mode; noise models drastically reduce accuracy compared to ideal state-vector simulators.
• Feature selection is integral to the evaluation; swapping quantum-selected features for classical ones changes accuracy by ~0.02, so the protocol must specify the feature set used.

Evidence (verbatim from paper)

Due to data under-sampling, we have used 5 random trials to minimize bias. The average KPIs for accuracy and ACU are reported in the Table 6.

Citation

@misc{grossi2022mixed,
  title={Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection},
  author={Michele Grossi et al.},
  year={2022},
  note={arXiv:2208.07963}
}

• arXiv: 2208.07963