Quantum Predicate Learning Sgg

---
name: quantum-predicate-learning-sgg
description: "QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods."
metadata:
  arxiv_id: "2606.04689"
  published: "2026-06-06"
  category: "quantum-ml"
---

# QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

## Core Methodology

Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is limited by severe long-tail predicate imbalance. This work introduces a hybrid quantum predicate classifier for SGG by replacing the classical predicate head in Causal Feature Enhancement Network (CFEN) with a Quantum Predicate Head (QP-Head). The best 4-qubit QP-Head uses Amplitude Embedding and Strongly Entangling Layers to compress 4096-dimensional pair features into 16-dimensional quantum-compatible representation (256x reduction), achieving mR@100 of 57.25% vs 41.1% classical CFEN reference with only 96 trainable quantum parameters. Demonstrates parameter-efficient long-tail relational classification in visual reasoning.

## Key Mathematical Framework

- **Domain**: quantum-ml
- **arXiv**: 2606.04689
- **Date**: 2026-06-06
- **Math Keywords**: amplitude embedding, entangling layers, dimensionality reduction, cross-entropy optimization

## Application Patterns

### Pattern 1: Mathematical Analysis
- Identify core mathematical structures in quantum protocols
- Map to complexity theory bounds or statistical models
- Extract reusable analytical patterns

### Pattern 2: Quantum-Classical Comparison
- Compare quantum vs classical performance metrics
- Quantify parameter efficiency gains
- Analyze scaling behavior

## Activation Keywords

- 2606.04689
- quantum predicate learning, scene graph generation, long-tail classification, amplitude embedding, quantum neural network, hybrid quantum-classical