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Omdena-Quantum-Self-Driving Project 2023

Project Lead: Raghav Thiruvallur (Please contact raghav.thiruv@gmail.com for any questions)

Variational Quantum Convolutional Neural Network for Image Recognition to Determine Steering Angle for Autonomous Vehicles

Goal

Design and implement a Variational Quantum Algorithm alongside a classical Machine Learning model to determine the steering angle correction for self-driving vehicles

Motivation

  • Existing classical deep neural networks yield good performance
  • Aim for more generalized models and higher efficiency while training and testing
  • Can we harness the power of quantum computing to help increase generality and/or efficiency?

Project Approach

  1. Dataset:
    • Find a dataset which contains front-camera view of the road and corresponding steering wheel angle change to keep the car straight.
    • Understand the dimensions and variety of input images
    • Clean and preprocess the dataset
  2. Build the Quantum and Classical Models
    • Group 1: Create a classical Convolutional Neural Network (CNN) to predict the vehicle's steering angle based on images
    • Group 2: Simultaneously build and test a Variational Quantum Algorithm (VQA)
      • Use a simple existing vanilla neural network in place of the CNN
      • Build various ansatze circuits and compare their performances (e.g., convergence, accuracy, loss, runtime, etc.)
  3. After initial development, combine CNN and VQA, and fine-tune the model
  4. Import the model and work with vehicle-environment simulations (e.g., CARLA, AirSim) and test hybrid model in the environment.

Project Architecture

Deep Learning Neural Networks + Variational Quantum Circuit

Training models using 4 qubit HVA circuit + Deep Learnning Neural netwwrk

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