cv-qnn-spatial-classification

name	cv-qnn-spatial-classification
description	Continuous-variable QNN advantage for spatial classification tasks. Controlled comparison showing CV-QNN outperforms DV-QNN by 18+ percentage points on wafer-map defect classification. CV structured layer captures fine spatial distinctions that DV misses. Use when designing QNNs for image/spatial classification, semiconductor yield, or any task requiring fine spatial pattern recognition.
version	1.0.0
tags	["quantum","CV-QNN","DV-QNN","spatial-classification","image","defect-detection"]
source	arXiv:2607.00961
authors	["Yeonhong Kim","Jonghyeok Im","Monu Nath Baitha","Kyoungsik Kim"]
published	2026-07-01T00:00:00.000Z
trigger_words	["CV QNN vs DV QNN","continuous variable quantum neural network","quantum spatial classification","wafer map defect","quantum image classification","QNN representational capacity"]

CV-QNN Advantage for Spatial Classification

Core Insight

CV-QNNs consistently outperform DV-QNNs on spatial classification tasks by 18+ percentage points. The CV advantage comes from two intrinsic properties: structured neural-network-analogue layer and continuous phase-space encoding, NOT Hilbert-space dimensionality.

Key Findings

1. Performance Gap

• At 4 qumodes/qubits: CV = 79.7% vs DV = 61.6% (non-overlapping 18-point gap)
• Gap is sharpest on spatially localized classes (Edge-Loc: CV recall 0.66, DV recall < 0.05)
• DV limitation is representational-capacity ceiling, not optimization failure

2. Why CV Wins

• Structured CV layer better captures fine spatial distinctions
• Continuous phase-space encoding preserves spatial information
• CV acts as neural-network-analogue layer (unlike DV)

3. Hardware Validation

• DV accuracy holds at shallow depth on IBM hardware
• DV degrades only at deepest circuit
• CV advantage expected to grow as noise improves

Implementation Pattern

1. Use shared convolutional backbone for feature extraction
2. Replace classical dense head with CV-QNN head
3. Scale CV head over multiple qumode counts (3, 4, 8)
4. Compare against DV-QNN head with same qubit count
5. Validate on hardware at shallow depth

Practical Applications

Financial Spatial Data

• Geographic risk mapping
• Regional market pattern classification
• Spatial-temporal financial data analysis

General Spatial Classification

• Image defect detection
• Medical image classification
• Any task requiring fine spatial pattern distinction

Activation

Use when:

• Designing QNNs for spatial/image classification
• Choosing between CV and DV quantum paradigms
• Needing fine spatial pattern recognition
• Building hybrid classical-quantum classifiers