safe-quantum-ml

name	safe-quantum-ml
description	SAFE Quantum Machine Learning methodology — variational quantum classifiers with amplitude encoding, learnable classical pre-encoding, and SAFE-AI reliability metrics (Cramer-von-Mises-based accuracy/robustness/explainability evaluation). For designing safety-critical quantum ML models.

SAFE Quantum Machine Learning

Description

SAFE Quantum Machine Learning methodology combining variational quantum classifiers (VQCs) with amplitude encoding and learnable classical pre-encoding layers. Uses SAFE-AI evaluation framework (accuracy, robustness, explainability via Cramer-von-Mises divergence) for systematic reliability assessment of quantum ML models in safety-critical applications. Based on arXiv:2605.16067.

Activation Keywords

• safe quantum ml
• quantum classifier safety
• quantum ml reliability
• variational quantum classifier
• quantum amplitude encoding
• 量子机器学习安全性
• quantum robustness evaluation
• SAFE-AI metrics
• quantum model explainability
• amplitude encoding classifier

Tools Used

• terminal: Run quantum ML experiments (PennyLane, Qiskit, or custom VQC implementations)
• write: Create SKILL.md and supporting experiment scripts
• search_files: Find existing quantum ML code or skills

Core Concepts

SAFE Framework Dimensions

Dimension	Metric	Purpose
Security	Adversarial robustness	Resistance to input perturbations
Accuracy	Predictive performance	Classification/regression quality
Fairness	Bias detection	Equitable predictions across groups
Explainability	Cramer-von-Mises divergence	Interpretable feature attribution

SAFE-AI Evaluation

• Uses Cramer-von-Mises (CvM) divergence for consistent reliability evaluation
• Measures consistency across accuracy, robustness, and explainability dimensions
• Provides balanced reliability profiles for comparing quantum vs classical models

Usage Patterns

Pattern 1: SAFE VQC Design

Build a variational quantum classifier with the SAFE architecture:

1. Classical Pre-Encoding Layer: Learnable classical layer that maps raw features to amplitude-encoded quantum states
2. Amplitude Encoding: Normalized input vectors → quantum state amplitudes
3. Parameterized Quantum Circuit (PQC): Variational quantum circuit for classification
4. Bounded Observables: Quantum observables with bounded eigenvalues for stable gradients

Pattern 2: SAFE Reliability Evaluation

Evaluate quantum ML models using the SAFE-AI framework:

1. Compute accuracy on test set
2. Measure robustness via input perturbation response
3. Assess explainability via CvM-based feature attribution
4. Generate balanced SAFE score across all dimensions

Pattern 3: Quantum-Classical Comparison

Compare quantum models with classical baselines using SAFE metrics:

1. Train equivalent classical model with same architecture depth
2. Apply identical SAFE evaluation pipeline
3. Compare SAFE profiles (not just accuracy)
4. Identify trade-offs between quantum and classical approaches

Instructions for Agents

Step 1: Problem Definition

• Determine if the task is suitable for quantum ML (small-to-medium datasets, structured features)
• Define SAFE requirements: which dimensions (S/A/F/E) are most critical

Step 2: Data Preparation

• Normalize input features to [0, 1] range for amplitude encoding
• Ensure feature dimension matches quantum register size (2^n features for n qubits)
• Split into train/validation/test sets

Step 3: Architecture Design

• Design classical pre-encoding layer (typically MLP)
• Choose PQC architecture (hardware-efficient, alternating layers, etc.)
• Select bounded quantum observables (typically Z-basis measurements)
• Determine number of qubits and circuit depth

Step 4: Training

• Use hybrid quantum-classical optimization (e.g., SPSA, Adam)
• Apply gradient clipping for stable training
• Monitor convergence and barren plateau indicators
• Use mini-batch training with shot-based estimation

Step 5: SAFE Evaluation

• Security: Test adversarial robustness (PGD, FGSM attacks)
• Accuracy: Standard metrics (accuracy, F1, AUC)
• Fairness: Check prediction bias across demographic groups
• Explainability: Compute CvM divergence for feature importance

Step 6: Comparison & Reporting

• Compare with classical baselines using SAFE profiles
• Document trade-offs and quantum advantage (if any)
• Report all four SAFE dimensions, not just accuracy

Error Handling

Barren Plateaus

• If gradients vanish: reduce circuit depth, use layer-wise training
• Try different initialization strategies
• Use problem-inspired ansatz instead of hardware-efficient

Shot Noise

• If shot noise dominates: increase shots, use variance reduction
• Consider analytical gradients where possible

Amplitude Encoding Dimensionality

• If features ≠ 2^n: pad with zeros or use PCA to reduce
• Consider angle encoding for large feature spaces

Examples

Example: Binary Classification with SAFE VQC

# Conceptual architecture
class SafeVQC:
    def __init__(self, n_qubits=4, n_layers=2):
        self.pre_encoding = nn.Linear(n_features, 2**n_qubits)  # Classical
        self.pqc = ParameterizedQuantumCircuit(n_qubits, n_layers)
        self.observable = BoundedObservable(n_qubits)
    
    def forward(self, x):
        # Classical pre-encoding
        encoded = self.pre_encoding(x)
        # Amplitude normalization
        encoded = encoded / torch.norm(encoded, dim=1, keepdim=True)
        # Quantum circuit
        quantum_state = self.pqc(encoded)
        # Bounded measurement
        return self.observable(quantum_state)

Limitations

• Currently demonstrated on small-scale problems (≤ 10 qubits)
• Requires careful hyperparameter tuning
• Shot noise limits gradient estimation precision
• Not yet proven for large-scale real-world datasets

Resources

• arXiv:2605.16067 — "SAFE Quantum Machine Learning with Variational Quantum Classifiers"
• PennyLane: https://pennylane.ai
• Qiskit: https://qiskit.org

Related Skills

• quantum-ml-patterns — General QML research patterns
• qml-mutation-testing — QML model testing and robustness
• quantum-neural-architecture — QNN architecture design