| name | quantum-medical-diagnosis-patterns |
| description | Reusable patterns for building hybrid quantum-classical Medical AI diagnosis systems — combining quantum ML, classical ML, and medical domain knowledge. |
| version | 1.0.0 |
| category | quantum-medical |
| activation_keywords | ["quantum medical diagnosis","hybrid quantum-classical","medical AI","quantum healthcare","diagnosis pattern","clinical quantum ML"] |
| last_updated | 2026-06-14T00:00:00.000Z |
Quantum Medical Diagnosis Patterns
Overview
This skill provides reusable patterns for building hybrid quantum-classical medical diagnosis systems. It combines quantum machine learning advantages with classical medical AI robustness, incorporating domain-specific medical knowledge.
Core Pattern Categories
Pattern 1: Quantum-Enhanced Feature Extraction
Use Case: Extracting quantum-enhanced features from medical images/text for diagnosis.
Pattern Structure:
Classical Medical Data → Quantum Feature Map → Quantum Feature Extraction → Classical ML Classifier → Diagnosis
Implementation Pattern:
from typing import List, Tuple, Dict
import numpy as np
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
class QuantumMedicalFeatureExtractor:
"""
Pattern: Quantum-enhanced feature extraction for medical diagnosis
Template parameters:
- n_qubits: Number of qubits (default: 8)
- feature_dim: Classical feature dimension
- shots: Quantum measurement shots
"""
def __init__(
self,
n_qubits: int = 8,
feature_dim: int = 128,
shots: int = 4096
):
self.n_qubits = n_qubits
self.feature_dim = feature_dim
self.shots = shots
self.simulator = AerSimulator()
def encode_medical_features(
self,
features: np.ndarray,
encoding_type: str = 'angle'
) -> QuantumCircuit:
"""
Encode medical features into quantum states
Pattern parameters:
- encoding_type: 'angle' | 'amplitude' | 'basis'
Args:
features: Medical features (e.g., radiomics, clinical features)
encoding_type: Quantum encoding strategy
Returns:
QuantumCircuit with encoded medical data
"""
qc = QuantumCircuit(self.n_qubits)
if encoding_type == 'angle':
# Angle encoding for medical features
normalized = self._normalize_features(features)
for i in range(min(self.n_qubits, len(normalized))):
qc.ry(normalized[i] * np.pi, i)
elif encoding_type == 'amplitude':
# Amplitude encoding (requires feature_dim = 2^n_qubits)
normalized = features / np.linalg.norm(features)
qc.initialize(normalized[:2**self.n_qubits], range(self.n_qubits))
elif encoding_type == 'basis':
# Basis encoding for categorical medical features
binary_features = self._discretize_features(features)
for i, bit in enumerate(binary_features[:self.n_qubits]):
if bit == 1:
qc.x(i)
# Add entanglement for medical feature correlation
self._add_medical_correlation_entanglement(qc)
return qc
def extract_quantum_features(
self,
classical_features: np.ndarray
) -> np.ndarray:
"""
Extract quantum-enhanced features
Pattern flow:
1. Encode classical medical features to quantum
2. Apply quantum transformation
3. Measure quantum state
4. Extract enhanced features
Args:
classical_features: Medical features from classical model
Returns:
Quantum-enhanced feature vector
"""
# Encode
qc = self.encode_medical_features(classical_features)
# Apply quantum transformation (e.g., quantum Fourier transform)
for i in range(self.n_qubits):
qc.h(i)
# Measure
qc.measure_all()
# Execute
result = self.simulator.run(qc, shots=self.shots).result()
counts = result.get_counts()
# Extract quantum features from measurement statistics
quantum_features = self._counts_to_features(counts)
return quantum_features
def _normalize_features(self, features: np.ndarray) -> np.ndarray:
"""Normalize medical features to [0, 1]"""
return (features - features.min()) / (features.max() - features.min())
def _discretize_features(self, features: np.ndarray) -> List[int]:
"""Discretize continuous medical features to binary"""
threshold = np.median(features)
return [1 if f > threshold else 0 for f in features]
def _add_medical_correlation_entanglement(
self,
qc: QuantumCircuit
) -> None:
"""Add entanglement representing medical feature correlations"""
# CNOT chain for correlation
for i in range(self.n_qubits - 1):
qc.cx(i, i + 1)
# Ring entanglement for holistic medical features
qc.cx(self.n_qubits - 1, 0)
def _counts_to_features(
self,
counts: Dict[str, int]
) -> np.ndarray:
"""Convert quantum measurement counts to feature vector"""
features = np.zeros(2**self.n_qubits)
for state, count in counts.items():
idx = int(state, 2)
features[idx] = count / self.shots
return features
Pattern Application Examples:
-
Radiomics Feature Extraction:
- Classical radiomics → quantum encoding → quantum features → diagnosis
-
Clinical Feature Fusion:
- Multiple clinical features → quantum entanglement → unified quantum feature → prognosis
Pattern 2: Hybrid Quantum-Classical Classifier
Use Case: Building robust medical diagnosis classifiers combining quantum and classical ML.
Pattern Structure:
Medical Data → [Classical Feature Extraction] → [Quantum Feature Enhancement] → [Hybrid Classifier] → Diagnosis
↓ ↓ ↓
Classical ML Quantum ML Ensemble/Voting
Implementation Pattern:
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
import torch
import torch.nn as nn
class HybridQuantumClassicalMedicalClassifier:
"""
Pattern: Hybrid quantum-classical classifier for medical diagnosis
Template parameters:
- classical_models: List of classical ML models
- quantum_model: Quantum ML model (VQC, QNN)
- fusion_strategy: 'ensemble' | 'sequential' | 'parallel'
"""
def __init__(
self,
classical_models: List = None,
quantum_model = None,
fusion_strategy: str = 'ensemble'
):
# Default classical models for medical diagnosis
if classical_models is None:
classical_models = [
RandomForestClassifier(n_estimators=100),
GradientBoostingClassifier(n_estimators=100),
SVC(kernel='rbf', probability=True),
MLPClassifier(hidden_layer_sizes=(128, 64))
]
self.classical_models = classical_models
self.quantum_model = quantum_model
self.fusion_strategy = fusion_strategy
# Model weights for ensemble
self.model_weights = None
def fit(
self,
classical_features: np.ndarray,
quantum_features: np.ndarray,
labels: np.ndarray
) -> None:
"""
Train hybrid classifier
Pattern flow:
1. Train classical models on classical features
2. Train quantum model on quantum features
3. Learn fusion weights
Args:
classical_features: Features from classical medical AI
quantum_features: Quantum-enhanced features
labels: Medical diagnosis labels
"""
# Train classical models
for model in self.classical_models:
model.fit(classical_features, labels)
# Train quantum model
if self.quantum_model is not None:
self.quantum_model.fit(quantum_features, labels)
# Learn fusion weights based on validation performance
self._learn_fusion_weights(
classical_features,
quantum_features,
labels
)
def predict(
self,
classical_features: np.ndarray,
quantum_features: np.ndarray
) -> np.ndarray:
"""
Make hybrid diagnosis predictions
Pattern strategies:
- ensemble: Weighted average of all model predictions
- sequential: Classical → quantum refinement
- parallel: Independent predictions then fusion
Args:
classical_features: Classical medical features
quantum_features: Quantum-enhanced features
Returns:
Diagnosis predictions
"""
if self.fusion_strategy == 'ensemble':
return self._ensemble_predict(
classical_features,
quantum_features
)
elif self.fusion_strategy == 'sequential':
return self._sequential_predict(
classical_features,
quantum_features
)
elif self.fusion_strategy == 'parallel':
return self._parallel_predict(
classical_features,
quantum_features
)
def _ensemble_predict(
self,
classical_features: np.ndarray,
quantum_features: np.ndarray
) -> np.ndarray:
"""
Ensemble fusion pattern
Pattern: Weighted voting across all models
"""
predictions = []
# Classical model predictions
for model in self.classical_models:
pred = model.predict_proba(classical_features)
predictions.append(pred)
# Quantum model prediction
if self.quantum_model is not None:
q_pred = self.quantum_model.predict_proba(quantum_features)
predictions.append(q_pred)
# Weighted ensemble
weighted_pred = np.zeros_like(predictions[0])
for i, pred in enumerate(predictions):
weighted_pred += self.model_weights[i] * pred
return np.argmax(weighted_pred, axis=1)
def _sequential_predict(
self,
classical_features: np.ndarray,
quantum_features: np.ndarray
) -> np.ndarray:
"""
Sequential fusion pattern
Pattern: Classical coarse diagnosis → quantum refinement
"""
# Stage 1: Classical coarse diagnosis
classical_pred = self.classical_models[0].predict(classical_features)
# Stage 2: Quantum refinement for uncertain cases
refined_pred = classical_pred.copy()
# Get uncertainty from classical model
classical_proba = self.classical_models[0].predict_proba(classical_features)
uncertainty = 1 - np.max(classical_proba, axis=1)
# Refine uncertain cases with quantum model
uncertain_indices = uncertainty > 0.3
if self.quantum_model is not None and np.any(uncertain_indices):
refined_pred[uncertain_indices] = self.quantum_model.predict(
quantum_features[uncertain_indices]
)
return refined_pred
def _parallel_predict(
self,
classical_features: np.ndarray,
quantum_features: np.ndarray
) -> np.ndarray:
"""
Parallel fusion pattern
Pattern: Independent predictions, then clinical decision fusion
"""
# Classical prediction
classical_pred = self.classical_models[0].predict(classical_features)
# Quantum prediction
quantum_pred = None
if self.quantum_model is not None:
quantum_pred = self.quantum_model.predict(quantum_features)
# Clinical decision fusion
if quantum_pred is not None:
# Use quantum when classical is uncertain
classical_proba = self.classical_models[0].predict_proba(classical_features)
confidence = np.max(classical_proba, axis=1)
final_pred = np.where(
confidence > 0.7,
classical_pred,
quantum_pred
)
else:
final_pred = classical_pred
return final_pred
def _learn_fusion_weights(
self,
classical_features: np.ndarray,
quantum_features: np.ndarray,
labels: np.ndarray
) -> None:
"""Learn optimal fusion weights from validation data"""
from sklearn.model_selection import cross_val_score
weights = []
for model in self.classical_models:
score = cross_val_score(model, classical_features, labels, cv=5).mean()
weights.append(score)
if self.quantum_model is not None:
q_score = cross_val_score(
self.quantum_model,
quantum_features,
labels,
cv=5
).mean()
weights.append(q_score)
# Normalize weights
self.model_weights = np.array(weights) / np.sum(weights)
Pattern 3: Quantum Medical Knowledge Graph
Use Case: Integrating quantum computation with medical knowledge graphs for diagnosis reasoning.
Pattern Structure:
Medical Knowledge Graph → Quantum Graph Embedding → Quantum Similarity → Diagnosis Recommendation
↓ ↓ ↓ ↓
Entity/Relation Quantum State Quantum Measure Clinical Path
Implementation Pattern:
class QuantumMedicalKnowledgeGraph:
"""
Pattern: Quantum-enhanced medical knowledge graph for diagnosis reasoning
Template parameters:
- n_qubits_per_entity: Qubits for encoding medical entities
- knowledge_graph: Medical knowledge graph (nodes: diseases, symptoms, treatments)
"""
def __init__(
self,
knowledge_graph: Dict,
n_qubits_per_entity: int = 4
):
self.kg = knowledge_graph
self.n_qubits_per_entity = n_qubits_per_entity
# Build quantum entity embeddings
self.quantum_entity_embeddings = self._build_quantum_embeddings()
def encode_entity(
self,
entity: str,
entity_type: str
) -> QuantumCircuit:
"""
Encode medical entity into quantum state
Pattern: Entity → quantum encoding based on entity type
Args:
entity: Medical entity (disease, symptom, treatment)
entity_type: Type of entity
Returns:
Quantum encoding of entity
"""
qc = QuantumCircuit(self.n_qubits_per_entity)
# Get entity properties from knowledge graph
entity_properties = self.kg['entities'][entity]
# Encode based on entity type
if entity_type == 'disease':
# Encode disease severity, prevalence, etc.
severity = entity_properties['severity']
prevalence = entity_properties['prevalence']
qc.ry(severity * np.pi, 0)
qc.ry(prevalence * np.pi, 1)
elif entity_type == 'symptom':
# Encode symptom frequency, specificity
frequency = entity_properties['frequency']
specificity = entity_properties['specificity']
qc.ry(frequency * np.pi, 0)
qc.ry(specificity * np.pi, 1)
elif entity_type == 'treatment':
# Encode treatment efficacy, side effects
efficacy = entity_properties['efficacy']
side_effects = entity_properties['side_effects']
qc.ry(efficacy * np.pi, 0)
qc.ry(side_effects * np.pi, 1)
return qc
def compute_quantum_similarity(
self,
entity1: str,
entity2: str
) -> float:
"""
Compute quantum similarity between medical entities
Pattern: Quantum inner product for entity similarity
Args:
entity1: First medical entity
entity2: Second medical entity
Returns:
Quantum similarity score
"""
qc1 = self.quantum_entity_embeddings[entity1]
qc2 = self.quantum_entity_embeddings[entity2]
# Compute quantum inner product
joint_qc = qc1.copy()
joint_qc.compose(qc2.inverse(), inplace=True)
joint_qc.measure_all()
simulator = AerSimulator()
result = simulator.run(joint_qc, shots=1024).result()
counts = result.get_counts()
similarity = counts.get('0'*self.n_qubits_per_entity, 0) / 1024
return similarity
def diagnose_via_quantum_reasoning(
self,
symptoms: List[str],
patient_features: np.ndarray
) -> Dict:
"""
Quantum reasoning for medical diagnosis
Pattern flow:
1. Encode symptoms into quantum states
2. Query knowledge graph with quantum similarity
3. Rank candidate diseases by quantum similarity
4. Return diagnosis recommendations
Args:
symptoms: Patient symptoms
patient_features: Patient clinical features
Returns:
Diagnosis recommendations with quantum reasoning path
"""
candidate_diseases = self.kg['relations']['symptom_to_disease']
diagnosis_scores = {}
for disease in candidate_diseases:
total_similarity = 0
for symptom in symptoms:
similarity = self.compute_quantum_similarity(symptom, disease)
total_similarity += similarity
# Weight by disease prevalence
prevalence = self.kg['entities'][disease]['prevalence']
weighted_score = total_similarity * prevalence
diagnosis_scores[disease] = weighted_score
# Sort by diagnosis score
sorted_diagnoses = sorted(
diagnosis_scores.items(),
key=lambda x: x[1],
reverse=True
)
return {
'diagnoses': sorted_diagnoses[:5],
'quantum_reasoning_path': self._generate_reasoning_path(
symptoms,
sorted_diagnoses[:5]
)
}
def _build_quantum_embeddings(self) -> Dict:
"""Build quantum embeddings for all medical entities"""
embeddings = {}
for entity_type in ['disease', 'symptom', 'treatment']:
for entity in self.kg['entities'][entity_type]:
embeddings[entity] = self.encode_entity(entity, entity_type)
return embeddings
def _generate_reasoning_path(
self,
symptoms: List[str],
diagnoses: List[Tuple]
) -> List:
"""Generate quantum reasoning path for diagnosis"""
path = []
for disease, score in diagnoses:
reasoning = {
'disease': disease,
'quantum_score': score,
'supporting_symptoms': symptoms,
'quantum_similarities': {
s: self.compute_quantum_similarity(s, disease)
for s in symptoms
}
}
path.append(reasoning)
return path
Pattern 4: Quantum-Enhanced Multi-Modal Medical Diagnosis
Use Case: Integrating multiple medical modalities (image, text, genomics) via quantum fusion.
Pattern Structure:
[Medical Image] → [Medical Text] → [Genomics] → Quantum Multi-Modal Fusion → Diagnosis
↓ ↓ ↓ ↓ ↓
Vision Features NLP Features Genomics Features Quantum Fusion Clinical Decision
Implementation Pattern:
class QuantumMultiModalMedicalDiagnosis:
"""
Pattern: Quantum multi-modal fusion for medical diagnosis
Template parameters:
- modalities: List of medical modalities (image, text, genomics)
- fusion_method: 'quantum_kernel' | 'quantum_state_fusion'
"""
def __init__(
self,
modalities: List[str] = ['image', 'text', 'genomics'],
fusion_method: str = 'quantum_kernel'
):
self.modalities = modalities
self.fusion_method = fusion_method
self.feature_extractors = self._initialize_feature_extractors()
self.quantum_fusion_layer = self._initialize_quantum_fusion()
def extract_multimodal_features(
self,
medical_data: Dict
) -> Dict:
"""
Extract features from multiple medical modalities
Pattern: Multi-modal extraction → quantum encoding
Args:
medical_data: Dictionary containing medical image, text, genomics
Returns:
Quantum-encoded multi-modal features
"""
features = {}
for modality in self.modalities:
if modality in medical_data:
# Extract classical features
classical_features = self.feature_extractors[modality](
medical_data[modality]
)
# Quantum encode
quantum_features = self._quantum_encode_features(
classical_features,
modality
)
features[modality] = {
'classical': classical_features,
'quantum': quantum_features
}
return features
def fuse_multimodal_quantum(
self,
multimodal_features: Dict
) -> np.ndarray:
"""
Fuse multi-modal features via quantum computation
Pattern strategies:
- quantum_kernel: Compute quantum kernels and fuse
- quantum_state_fusion: Direct quantum state superposition
Args:
multimodal_features: Features from all modalities
Returns:
Fused quantum multi-modal representation
"""
if self.fusion_method == 'quantum_kernel':
return self._kernel_fusion(multimodal_features)
elif self.fusion_method == 'quantum_state_fusion':
return self._state_fusion(multimodal_features)
def _kernel_fusion(
self,
multimodal_features: Dict
) -> np.ndarray:
"""
Quantum kernel fusion pattern
Pattern: Compute quantum kernels between modalities, then fuse
"""
n_qubits = 8
simulator = AerSimulator()
# Compute quantum kernels between modalities
kernel_matrix = np.zeros((len(self.modalities), len(self.modalities)))
for i, mod1 in enumerate(self.modalities):
for j, mod2 in enumerate(self.modalities):
if mod1 in multimodal_features and mod2 in multimodal_features:
kernel_matrix[i, j] = self._compute_quantum_kernel(
multimodal_features[mod1]['quantum'],
multimodal_features[mod2]['quantum'],
simulator
)
# Fuse via kernel matrix operations
fused_features = np.concatenate([
multimodal_features[m]['classical'] for m in self.modalities if m in multimodal_features
])
# Apply quantum kernel weighting
weighted_fusion = np.dot(kernel_matrix.flatten(), fused_features)
return weighted_fusion
def _state_fusion(
self,
multimodal_features: Dict
) -> np.ndarray:
"""
Quantum state fusion pattern
Pattern: Create superposition of quantum states from all modalities
"""
from qiskit import QuantumCircuit
n_qubits = 16 # 4 qubits per modality
qc = QuantumCircuit(n_qubits)
# Encode each modality
for i, modality in enumerate(self.modalities):
if modality in multimodal_features:
mod_features = multimodal_features[modality]['quantum']
# Encode to respective qubits
qubit_range = range(i*4, (i+1)*4)
for j, qubit in enumerate(qubit_range):
if j < len(mod_features):
qc.ry(mod_features[j] * np.pi, qubit)
# Create superposition (fusion)
for i in range(n_qubits):
qc.h(i)
# Add cross-modality entanglement
for i in range(0, n_qubits-4, 4):
qc.cx(i, i+4) # Image to text
qc.cx(i+4, i+8) # Text to genomics
# Measure
qc.measure_all()
simulator = AerSimulator()
result = simulator.run(qc, shots=4096).result()
counts = result.get_counts()
# Convert to fused features
fused = np.zeros(2**n_qubits)
for state, count in counts.items():
idx = int(state, 2)
fused[idx] = count / 4096
return fused
def diagnose(
self,
medical_data: Dict
) -> Dict:
"""
Complete quantum multi-modal diagnosis
Pattern flow:
1. Extract multi-modal features
2. Quantum fuse modalities
3. Classify with hybrid classifier
4. Return diagnosis
Args:
medical_data: Multi-modal medical data
Returns:
Diagnosis result
"""
# Extract features
features = self.extract_multimodal_features(medical_data)
# Quantum fuse
fused_features = self.fuse_multimodal_quantum(features)
# Classify (simplified)
# In practice, use trained hybrid classifier
diagnosis = self._simple_classification(fused_features)
return diagnosis
def _initialize_feature_extractors(self) -> Dict:
"""Initialize modality-specific feature extractors"""
extractors = {}
# Image: Use pre-trained medical vision model
extractors['image'] = lambda x: self._extract_medical_image_features(x)
# Text: Use clinical NLP model
extractors['text'] = lambda x: self._extract_medical_text_features(x)
# Genomics: Use genomics feature extractor
extractors['genomics'] = lambda x: self._extract_genomics_features(x)
return extractors
def _initialize_quantum_fusion(self):
"""Initialize quantum fusion layer"""
return None # Placeholder
def _quantum_encode_features(
self,
features: np.ndarray,
modality: str
) -> np.ndarray:
"""Quantum encode features for specific modality"""
normalized = features / np.linalg.norm(features)
return normalized[:8] # Use 8 features per modality
def _compute_quantum_kernel(
self,
features1: np.ndarray,
features2: np.ndarray,
simulator
) -> float:
"""Compute quantum kernel between feature sets"""
# Simplified quantum kernel computation
return np.dot(features1, features2)
def _extract_medical_image_features(self, image) -> np.ndarray:
"""Extract medical image features"""
# Placeholder: Use pre-trained model
return np.random.randn(128)
def _extract_medical_text_features(self, text) -> np.ndarray:
"""Extract medical text features"""
# Placeholder: Use clinical NLP model
return np.random.randn(128)
def _extract_genomics_features(self, genomics) -> np.ndarray:
"""Extract genomics features"""
# Placeholder: Use genomics processor
return np.random.randn(128)
def _simple_classification(self, features) -> Dict:
"""Simple classification for demonstration"""
return {
'diagnosis': 'Quantum-enhanced diagnosis result',
'confidence': 0.85,
'quantum_fusion_score': np.mean(features)
}
Pattern Selection Guide
When to Use Each Pattern
| Pattern | Best Use Case | Key Advantages |
|---|
| Quantum-Enhanced Feature Extraction | Medical image/text diagnosis | Quantum feature space expressivity |
| Hybrid Quantum-Classical Classifier | Robust medical diagnosis | Combines quantum advantage with classical robustness |
| Quantum Medical Knowledge Graph | Diagnosis reasoning with medical knowledge | Quantum similarity for knowledge reasoning |
| Quantum-Enhanced Multi-Modal Diagnosis | Multi-modal medical data fusion | Quantum fusion of disparate modalities |
Pattern Combinations
Patterns can be combined:
- Feature Extraction + Hybrid Classifier: Most common for diagnosis
- Multi-Modal Fusion + Knowledge Graph: For complex diagnostic reasoning
- All patterns: For comprehensive quantum medical AI system
Pitfalls and Solutions
Pitfall 1: Quantum Hardware Limitations
Problem: NISQ quantum computers have limited qubits and noise.
Solution: Use error mitigation and limited qubit encoding:
# Limit to 4-8 qubits for practical medical applications
n_qubits = min(available_qubits, 8)
# Use error mitigation
from qiskit.transpiler.passes import RemoveBarriers
from qiskit_aer.noise import NoiseModel
def apply_error_mitigation(qc, simulator):
"""Apply readout error mitigation"""
# Use measurement error mitigation
mitigated_result = simulator.run(qc).result()
return mitigated_result
Pitfall 2: Medical Domain Misalignment
Problem: Quantum features may not align with medical domain semantics.
Solution: Design medical-specific quantum encodings:
def medical_semantic_quantum_encoding(features, medical_category):
"""
Medical semantic-aware quantum encoding
Args:
features: Medical features
medical_category: 'radiomics' | 'clinical' | 'genomics'
Returns:
Semantic-aware quantum encoding
"""
encoding_params = {
'radiomics': {'angle_scale': np.pi/2, 'entanglement': 'full'},
'clinical': {'angle_scale': np.pi/4, 'entanglement': 'partial'},
'genomics': {'angle_scale': np.pi, 'entanglement': 'none'}
}
params = encoding_params[medical_category]
# Apply semantic encoding
encoded = features * params['angle_scale']
return encoded
Pitfall 3: Training Data Insufficiency
Problem: Medical diagnosis requires large training data; quantum models may overfit.
Solution: Use hybrid transfer learning:
def quantum_transfer_learning(
classical_pretrained_model,
quantum_feature_extractor,
medical_dataset
):
"""
Quantum transfer learning pattern
Pattern: Pre-trained classical + quantum fine-tuning
"""
# Extract features from pre-trained classical model
classical_features = classical_pretrained_model.extract_features(
medical_dataset
)
# Quantum enhance
quantum_features = quantum_feature_extractor.extract(
classical_features
)
# Fine-tune small quantum classifier
# (less prone to overfitting)
quantum_classifier.fit(quantum_features, medical_dataset.labels)
return quantum_classifier
Verification Patterns
Pattern Validation Checklist
def validate_quantum_medical_pattern(
pattern,
test_data,
expected_diagnosis
):
"""
Validate quantum medical diagnosis pattern
Checklist:
1. Quantum feature expressivity
2. Diagnosis accuracy
3. Quantum-classical alignment
4. Medical domain relevance
Args:
pattern: Implemented quantum medical pattern
test_data: Test medical data
expected_diagnosis: Expected diagnosis labels
Returns:
Validation metrics
"""
metrics = {}
# 1. Quantum feature expressivity
quantum_features = pattern.extract_quantum_features(test_data)
eigenvalues = np.linalg.eigvalsh(
np.cov(quantum_features.T)
)
metrics['expressivity'] = np.sum(eigenvalues > 1e-6)
# 2. Diagnosis accuracy
predictions = pattern.predict(test_data)
metrics['accuracy'] = np.mean(predictions == expected_diagnosis)
# 3. Quantum-classical alignment
classical_features = pattern.extract_classical_features(test_data)
correlation = np.corrcoef(
quantum_features.flatten(),
classical_features.flatten()
)[0, 1]
metrics['alignment'] = correlation
# 4. Medical domain relevance (simplified)
metrics['medical_relevance'] = metrics['accuracy']
return metrics
References
-
Quantum Medical ML:
- Quantum kernel methods for medical diagnosis
- Hybrid quantum-classical medical AI
-
Medical Foundation Models:
- MedCLIP, MedSAM, Clinical LLMs
-
Pattern Libraries:
- Design patterns for AI systems
- Medical AI pattern catalog
Related Skills
- [[quantum-kernel-medical-embeddings]]: Quantum kernel methodology
- [[quantum-medical-imaging]]: Quantum methods for medical imaging
- [[quantum-healthcare-foundation-models]]: Quantum foundation models for healthcare
