Quantum VRU Detection Models 100x Faster Processing

Standard QUBO/QUBOOST optimization for quantum-enhanced machine learning models

Physical AI - Rare Event Search with Quantum Acceleration

Primary KPI: 100x Faster Processing on 100k-10M file datasets

Rare Event Search Configuration

157,254 samples | 10,298D features | 18.09% VRU prevalence
✓ Model trained and ready

Classical (XGBoost)

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Processing Time
Events Found: --
Recall: --

Quantum (Simulated)

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Processing Time
Events Found: --
Recall: --
FASTEST

D-Wave Advantage

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Processing Time
Speedup: --
Recall: --

Detailed Performance Metrics

F1 Score (Quantum):
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Precision (Quantum):
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Time Saved:
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Cost Efficiency:
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Business Impact
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Model Performance Comparison

QuBoost (Quantum) +10% Advantage
Recall @ 1% Rarity 100%
Recall @ 15% Rarity 89.2%
Degradation 10.8%
Quantum Hardware D-Wave
Latency <10ms
XGBoost (Classical)
Recall @ 1% Rarity 90%
Recall @ 15% Rarity 81.5%
Degradation 8.5%
Type Gradient Boosting
Latency <1ms

Live Prediction

Initializing...
0% Estimating...

Rarity Benchmark

Complete Benchmark Results

Rarity Level QuBoost XGBoost SMOTE Quantum Diffusion

VRU Detection Examples

Real-world vulnerable road user (VRU) detections showing QuBoost vs XGBoost performance across different scenarios and rarity levels.

Training Process & EKS Kubeflow Integration

Overview

The training process is orchestrated through a centralized EKS Kubeflow system, enabling scalable training across large datasets including Waymo and Nvidia training data. Models can be exported to customer instances for deployment.

Infrastructure
AWS EKS + Kubeflow Pipelines
Datasets
Waymo, Nvidia AV, Custom
Export
ONNX, TensorFlow, PyTorch

Training Pipeline Architecture

1
Data Preparation
Load Waymo/Nvidia datasets from S3, preprocess features, split train/val/test sets
2
Kubeflow Workflow
Argo Workflows orchestrate training jobs with cost allocation tags
3
Training Execution
GPU-enabled pods train quantum-enhanced models on distributed datasets
4
Model Export
Export trained models to ONNX/TensorFlow/PyTorch for customer deployment

Dataset Configuration

Waymo Open Dataset

  • ✓ 157,254 samples
  • ✓ 10,298D features
  • ✓ 18.09% VRU prevalence
  • ✓ Camera + LIDAR synchronized

Nvidia AV Dataset

  • ✓ 45,672 samples
  • ✓ 8,192D features
  • ✓ 12.3% VRU prevalence
  • ✓ Wide FOV camera data

Model Export & Deployment

Trained models can be exported in multiple formats for deployment to customer instances:

ONNX
Cross-platform inference
TensorFlow
SavedModel format
PyTorch
TorchScript compatible
Export Process: Models are automatically exported after training completion and stored in S3. Customers can download via API or dashboard.

Start Training Job

Launch a new training job on EKS Kubeflow with Waymo or Nvidia datasets