cv-qnn-edge-medical-imaging

name	cv-qnn-edge-medical-imaging
description	Parameter-efficient continuous-variable photonic quantum neural networks for edge medical AI. Room-temperature quantum ML for medical image classification with 40-45% parameter reduction. Covers CV-QNN architecture simplification, barren plateau mitigation, and edge deployment strategies.
tags	["quantum","machine-learning","medical","edge-ai","photonic"]
related_skills	["hybrid-quantum-classical-feature-fusion-medical","qml-feature-encoding"]

CV-QNN Edge Medical Imaging

Design and implement parameter-efficient continuous-variable (CV) photonic quantum neural networks for medical image classification on edge devices.

Core Architecture

Simplified CV-QNN Layer: Φ ∘ D ∘ U₁

Standard CV-QNN (Killoran et al. 2019):

U₂(θ₂) ∘ S(r) ∘ U₁(θ₁)  # 3-gate sequence

Simplified architecture (40-45% parameter reduction):

Φ(θ₃) ∘ D(α) ∘ U₁(θ₁)  # displacement + single interferometer

Where:

• U₁ = interferometric unitary (linear optics)
• D = displacement gate
• Φ = nonlinear Kerr gate

Pipeline

1. Classical feature extraction: MobileNetV1 or similar lightweight CNN
2. Dimensionality reduction: PCA to 16 dimensions (critical for avoiding barren plateaus)
3. Encoding: Encode PCA features into CV quantum states
4. CV-QNN: Parameterized circuit with displacement, interferometry, and Kerr gates
5. Measurement: Homodyne detection → classification

Barren Plateau Mitigation

Strategy	Effect
PCA dimensionality reduction (to 16D)	Raises gradient variance by ~58 orders of magnitude
Encoding restriction	Limits feature space to avoid saturation
Simplified layer architecture	Better at ≥4 qumodes with 44% fewer params

Design Trade-offs

• 2 qumodes: Full layer has slight edge
• 4+ qumodes: Simplified layer wins (44% fewer params, better performance)
• 18-parameter model: Can exceed 55-parameter classical baseline with 67% fewer params

Edge Deployment Advantages

• Room temperature: Photonic QCs don't need cryogenics
• Low parameter count: 18 parameters for strong medical classification
• Calibrated accuracy: 100% test accuracy across all seeds demonstrated

Use Cases

• Oral cancer detection from smartphone images
• Low-resource medical screening
• Edge-deployed diagnostic tools
• Any medical imaging classification with resource constraints

Activation

cv-qnn, continuous variable quantum, photonic qnn, edge quantum ai, medical quantum ml, quantum oral cancer, parameter efficient qnn, cv quantum classifier, room temperature quantum

References

• arXiv:2606.28252 — "Parameter-Efficient Continuous-Variable Photonic Quantum Neural Networks for Edge Quantum AI: Demonstration in Oral Cancer Detection" (2026)
• Killoran et al. (2019a) — Standard CV-QNN layer architecture