Quantum Machine Learning: Hybrid VQC vs. Quantum Kernel SVM

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Overview

This repository hosts an Advanced Quantum Machine Learning (QML) project designed for rigorous comparative analysis and reproducible research. It implements and benchmarks two leading NISQ-era paradigms:

Hybrid Variational Quantum Classifier (VQC): A trainable quantum-classical hybrid model optimized end-to-end using PyTorch and PennyLane.

Quantum Kernel Support Vector Machine (QSVM): A kernel-based model utilizing quantum feature maps from PennyLane and Qiskit, interfaced with a classical SVM from scikit-learn.

Status: Active Research & Development / Reproducible Benchmarking Framework
Primary Goal: To provide a statistically grounded, open-source comparison of VQC and QSVM stability and performance on binary classification tasks, emphasizing reproducibility and cross-platform execution.

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Technology Stack & Framework Components

Component	Library/Framework	Description
VQC Training/Evaluation	PyTorch, PennyLane	Backpropagation and quantum differentiation
QSVM Training/Evaluation	PennyLane, Qiskit, Scikit-learn	Quantum kernel computation & classification
CLI Interface & Logging	Typer, Rich	Clean command execution and formatted logs
Configuration Management	PyYAML	Configurable experiment parameters
Numerical Processing	NumPy	Data preprocessing

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Methodology & Model Architecture

Hybrid Variational Quantum Classifier (VQC)

• Architecture: HybridVariationalClassifier (in models.py) combines a quantum layer implemented with pennylane.QNode and classical layers via torch.nn.Module.
• Ansatz: build_variational_circuit (in qnn_layers.py) implements a configurable Hardware-Efficient Ansatz.
• Optimization: train_variational_model (in training.py) uses torch.optim.Adam.
• Execution: evaluate_vqc (in evaluation.py) computes final metrics on the test set.

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Quantum Kernel Support Vector Machine (QSVM)

• Architecture: QuantumKernelClassifier (in models.py) integrates qml.kernels with sklearn.svm.SVC.
• Feature Map: build_kernel_qnode (in qnn_layers.py) defines the ZZFeatureMap.
• Execution: evaluate_kernel (in evaluation.py) computes final metrics on the test set.

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Main Workflow (in main.py)

• Provides train and evaluate commands for both models (vqc, kernel).
• Integrates Typer for the CLI and seed.py for global seed configuration.

VQC vs QSVM Comparison

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Contact & License

• Author: Ahmad Rasidi
• Email: [email protected]
• GitHub: https://github.com/rasidi3112

License: MIT License

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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)

python -m qml_app.main train --model vqc --config config/default.yaml

b. Quantum Kernel SVM (QSVM)

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

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:

• shots=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.

7. Run Additional Scripts

Kernel folder:

# Convert or preprocess data with convert.py
python3 artifacts/kernel/convert.py

# Run custom scripts
python3 artifacts/kernel/script.py

VQC folder:

# Convert PyTorch model to JSON format
python3 artifacts/vqc/convert_pt_to_json.py

Generate boxplots for VQC and QSVM results:

python generate_boxplots.py