adaptive-quantum-classical-fusion - SKILL.md Agent Skill

name: adaptive-quantum-classical-fusion description: "Adaptive quantum-classical feature fusion methodologies for medical AI diagnosis. Covers Temperature-Scaled Hybrid Fusion (TSHF), tensor-network compression with quantum refinement, and multi-head quantum-aware encoding. Use when building hybrid quantum-classical models for medical image classification, federated healthcare diagnosis, or quantum-enhanced diagnostic pipelines. Activation: quantum medical, hybrid quantum classical, quantum diagnosis, quantum feature fusion, breast cancer quantum, federated quantum medical, TSHF, tensor network quantum"

Adaptive Quantum-Classical Fusion for Medical AI

Methodologies for combining quantum computing with classical deep learning in medical diagnosis contexts, extracted from arXiv papers (2026-04).

Core Patterns

1. Temperature-Scaled Hybrid Fusion (TSHF)

Paper: arXiv:2604.22903 (Sobrinho et al.)

Three progressive fusion strategies for hybrid quantum-classical architectures:

Strategy	Use Case	Mechanism
SHF (Static)	Offline extraction	Fixed concatenation of classical + quantum embeddings
DHF (Dynamic)	End-to-end training	Co-adaptive gradient flow between branches
TSHF (Temperature-Scaled)	Production	Learnable scalar τ balances gradient dynamics: `output = softmax(classical/τ, quantum/τ)`

Key insight: TSHF resolves optimization asymmetries between classical and quantum branches. With ResNet + trainable quantum circuit on BreastMNIST: 87.82% accuracy, 91.77% F1, 89.08% AUC-ROC.

Implementation guide:

Dual-branch: classical backbone (ResNet/CNN) + parameterized quantum circuit
Quantum circuit: trainable gates, strongly entangling layers
TSHF scalar: init τ=1.0, learnable via backprop
Prefer TSHF when gradient magnitudes differ between branches

2. Tensor-Network + QEP Co-Design

Paper: arXiv:2604.01616 (Yamauchi et al.)

Tensor-network frontends compress high-dimensional medical images before quantum processing:

Raw Image → [MPS/TTN/MERA] → Compressed Latent → [QEP] → Classification
                    ↑                          ↑
              Compression               Quantum refinement
              (client-side)             (post-aggregation)

Frontend comparison:

TTN + QEP: Most balanced — best accuracy/latency/communication trade-off
MPS + QEP: Fastest compression, lower expressivity
MERA + QEP: Highest expressivity, most compute

Dual role of tensor-network compression:

Enables small-qubit quantum processing on compressed features
Reduces MPC communication overhead in federated settings

Design rule: Match qubit count to latent dimension. TTN output dimension should equal 2^n_qubits.

3. Multi-Head Quantum-Aware Encoding

Paper: arXiv:2604.16953 (Syah et al.)

Quantum circuits with multi-head attention for feature encoding:

4-qubit variational circuit with strongly entangling layers
Multi-head attention captures cross-feature quantum correlations
Classical CNN layers handle spatial pattern recognition
Quantum branch handles global feature relationships

Performance: Superior convergence dynamics vs. purely classical CNNs on thermographic breast cancer data.

Decision Table

Task                              → Recommended Pattern
Breast cancer classification      → TSHF (SHF if offline)
Federated medical diagnosis       → TTN + QEP (privacy-aware)
Thermographic analysis            → Multi-head quantum encoding
Low-qubit constraint (< 10)       → Tensor-network compression first
High-dimensional input            → MERA frontend + QEP
End-to-end trainable pipeline     → DHF or TSHF
Production deployment             → TSHF (adaptive balancing)

Implementation Considerations

Quantum Circuit Design

Use strongly entangling layers for expressivity
4-qubit circuits sufficient for compressed latent features
Prefer trainable over deterministic (data re-uploading) circuits

Data Preparation

Normalize inputs to unit sphere before quantum encoding
For medical images: consider dimensionality reduction (PCA/tensor-network) before angle encoding
BreastMNIST/PneumoniaMNIST are good benchmark datasets

Classical Backbone

ResNet-family backbones work well with quantum circuits
CNN + quantum circuit: quantum branch should handle global features
Avoid placing quantum circuit too early in pipeline (information bottleneck)

Related Existing Skills

hybrid-quantum-medical-diagnosis — broader QML medical diagnosis patterns
quantum-medical-feature-fusion — general feature fusion approaches
federated-quantum-medical-diagnosis — federated learning with quantum
tensor-network-quantum-federated — tensor network methods for quantum FL

References

arXiv:2604.22903 — Adaptive Hybrid Quantum-Classical Feature Fusion (kg_entity: check by URL)
arXiv:2604.01616 — Tensor-Network Frontends for Federated Medical Diagnosis
arXiv:2604.16953 — Hybrid QNN for Breast Cancer Thermographic Classification