Quantum Machine Learning: Hybrid VQC vs. Quantum Kernel SVM

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Overview

This repository contains a Quantum Machine Learning (QML) project for learning purposes and binary classification experiments.
It demonstrates a comparison between:

• Hybrid Variational Quantum Classifier (VQC) — hybrid quantum-classical model trained with PyTorch + PennyLane.
• Quantum Kernel Support Vector Machine (QSVM) — quantum kernel model leveraging PennyLane + scikit-learn.

Status: Experimental Quantum Machine Learning Project
Key Highlight: Demonstrates hybrid quantum-classical ML workflows with configurable pipelines and modular design.

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Key Features & Highlights

• Hybrid VQC: Layered hardware-efficient ansatz, gradient-based optimization via Adam.
• Quantum Kernel SVM: Feature mapping to quantum Hilbert space, kernel-based classical SVM.
• Modular architecture: YAML-config driven, easy to modify datasets and experiments.
• Automatic artifacts: Trained models, metrics, and confusion matrices stored in artifacts/.
• CLI & Logging: Powered by Typer and Rich for clean command-line execution and logging.
• Customizable experiments: Shots, feature layers, variational layers, and early stopping can be tuned.

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Technology Stack

Component	Library/Framework	Notes
Quantum ML (VQC)	PennyLane	Hybrid quantum-classical model
Quantum ML (QSVM)	PennyLane + Qiskit	Quantum kernel computation
Classical ML / SVM	Scikit-learn	QSVM classifier
Deep Learning / Optimizer	PyTorch	Gradient-based VQC training
CLI & Logging	Typer, Rich	Command-line interface & logging
Config Management	PyYAML	YAML configuration files
Numerical Computation	NumPy	Data preprocessing

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Methodology

Hybrid Variational Quantum Classifier (VQC)

• Ansatz: Layered Hardware-Efficient Circuit
• Feature Encoding: Classical features mapped to qubits using rotation gates
• Optimizer: Adam
• Framework: PennyLane + PyTorch
• Training: Early stopping, configurable shots, batch-based gradient updates

Quantum Kernel SVM (QSVM)

• Quantum Feature Map: ZZFeatureMap
• Kernel: Quantum state fidelity
• Classifier: Classical SVM (Scikit-learn)
• Framework: PennyLane + Scikit-learn

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Results and Analysis

Model	Accuracy	F1 Score
Hybrid VQC	70%	0.70
Quantum Kernel SVM (QSVM)	75%	0.75
Classical SVM (baseline)	80%	0.81

Note: QSVM shows slightly better accuracy in this simulation, while VQC allows hands-on experience with hybrid quantum-classical pipelines.

Note: Results vary significantly based on hardware constraints and simulation parameters. The metrics shown reflect typical behavior when running on standard laptop hardware.

Confusion Matrix example stored in artifacts/.

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Contact & License

• Author: Ahmad Rasidi
• Email: rasidi.basit@gmail.com
• GitHub: https://github.com/rasidi3112

License: MIT License

Disclaimer: This repository is intended for experimentation and practical exploration of Quantum Machine Learning concepts.

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Project Structure

qml_app/
├─ config/
│  └─ default.yaml
├─ qml_app/
│  ├─ __init__.py
│  ├─ config.py
│  ├─ data.py
│  ├─ evaluation.py
│  ├─ main.py
│  ├─ models.py
│  ├─ qnn_layers.py
│  ├─ training.py
│  └─ utils/
│     ├─ __init__.py
│     ├─ config_utils.py
│     ├─ logging_utils.py
│     └─ seed.py
└─ requirements.txt


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How To Run
1. Clone the Repository
  git clone https://github.com/rasidi3112/Quantum-Machine-Learning.git
  cd Quantum-Machine-Learning
  
2. Create and Activate Virtual Environment
  # macOS / Linux
  python -m venv .venv
  source .venv/bin/activate
  
  # Windows
  python -m venv .venv
  .venv\Scripts\activate

3. Install Dependencies
  pip install --upgrade pip
  pip install -r requirements.txt

4. Train Models
  a. Hybrid Variational Quantum Classifier (VQC)
      Run :
      python -m qml_app.main train --model vqc --config config/default.yaml

  b. Quantum Kernel SVM (QSVM)
      Run :
      python -m qml_app.main train --model kernel --config config/default.yaml

        Tip: Modify config/default.yaml to change datasets, qubits, layers, batch size, etc.
  

5. Evaluate Models
      Run :
      python -m qml_app.main evaluate --model vqc --config config/default.yaml
      python -m qml_app.main evaluate --model kernel --config config/default.yaml

  Evaluation results, including metrics and confusion matrices, are saved in artifacts/.

6. Additional Notes
    Device Selection:
      - Apple M1/M2 → device: mps
      - NVIDIA GPU → device: cuda
      - CPU-only → device: cpu
    Shots:
    - nshots=null for analytic/simulated mode (fast, ideal for CPU)
    - shots=1024 or higher for realistic sampling on quantum hardware
    Artifacts: Check artifacts/ for trained models, metrics, ROC curves, and confusion matrices

# Note: Adjust --device flag in config/default.yaml for CPU or GPU.