quantum-kernel-advantage-medical

name	quantum-kernel-advantage-medical
version	v1.0.0
last_updated	2026-05-20T00:00:00.000Z
description	Quantum kernel advantage methodology for medical imaging classification under class imbalance. Use when: (1) Evaluating QSVM vs classical SVM on medical datasets with severe class imbalance, (2) Comparing quantum and classical kernels using frozen foundation model embeddings, (3) Designing two-tier fair comparison frameworks for quantum ML, (4) Analyzing kernel eigenspectrum for effective rank, (5) Addressing classical kernel collapse on minority class prediction. Based on arXiv:2604.24597.

Quantum Kernel Advantage for Medical Imaging

Core Methodology

Demonstrate quantum kernel advantage using a two-tier fair comparison framework:

• Input: Frozen embeddings from medical foundation models (MedSigLIP, RAD-DINO, ViT)
• Dimensionality reduction: PCA to q features
• Classifiers: QSVM vs classical SVM on identical features

Two-Tier Comparison Framework

Tier 1: Untuned Comparison

• Untuned QSVM (C=1) vs untuned linear SVM (C=1)
• Both receive identical PCA-q features
• Evaluate minority-class F1 across multiple qubit counts
• Quantum advantage when classical linear kernel collapses to majority-class prediction

Tier 2: Tuned Classical Baseline

• Untuned QSVM vs C-tuned RBF SVM
• Classical SVM gets hyperparameter tuning advantage
• Quantum advantage persists if QSVM still wins

Key Findings

• Classical linear kernel collapses: 90-100% seeds predict majority class at every qubit count
• QSVM maintains non-trivial recall without tuning
• At q=11 with MedSigLIP-448: QSVM F1=0.343 vs classical F1=0.050 (gain +0.293, p<0.001)
• Quantum kernel effective rank far exceeds linear kernel rank at optimal qubit counts

Kernel Eigenspectrum Analysis

effective_rank = exp(entropy(eigenvalues))

• Quantum kernel effective rank peaks at architecture-dependent qubit count
• Classical linear kernel rank remains C-invariant (collapsing)
• Eigenspectrum analysis predicts which qubit counts will show advantage

Implementation

from qiskit_machine_learning.kernels import QuantumKernel
from sklearn.decomposition import PCA
from sklearn.svm import SVC

# Step 1: Get frozen embeddings from medical foundation model
# embeddings shape: (n_samples, d)

# Step 2: PCA dimensionality reduction
pca = PCA(n_components=q)
X_pca = pca.fit_transform(embeddings)

# Step 3: QSVM with quantum kernel
qsvm = SVC(kernel='precomputed')
# Compute quantum kernel matrix using parameterized circuit
K_q = compute_quantum_kernel(X_pca_train, X_pca_train, n_qubits=q)
qsvm.fit(K_q, y_train)

# Step 4: Classical baseline
linear_svm = SVC(kernel='linear', C=1.0)
linear_svm.fit(X_pca_train, y_train)

# Step 5: Compare F1 on minority class

When to Use

• Medical imaging classification with class imbalance
• Evaluating quantum advantage claims rigorously
• Feature selection via PCA + kernel comparison
• Foundation model embedding evaluation

Activation Keywords

• quantum kernel advantage, QSVM medical, quantum SVM classification, quantum kernel medical imaging, quantum advantage medical, quantum SVM vs classical SVM, 量子核优势医学, quantum foundation model embeddings, classical kernel collapse, kernel eigenspectrum

Paper Reference

• arXiv:2604.24597 — "Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings"
• Code: https://github.com/sebasmos/qml-medimage