🌌 Quantum‑Enhanced ML Applications

An open‑source project exploring hybrid quantum–classical machine learning with a modern, interactive UI. Built on Qiskit ≥ 1.x (Sampler API), SciPy optimizers, and a Streamlit app for demos.

⚠️ Work in progress — contributions & feedback are welcome!

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🚀 Overview

This repository demonstrates how quantum computing can augment traditional ML workflows on small datasets:

• Hybrid models: parameterized quantum circuits trained with classical optimizers.
• Modern Qiskit: uses the Sampler primitive (no deprecated OpFlow/QuantumInstance).
• Interactive UI: Streamlit web app to train, evaluate, and run live inference.
• Visuals: confusion matrix, accuracy, circuit preview, and downloadable predictions.

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✨ Features

• 🧩 Parameterized quantum classifier (feature‑encoded rotations + trainable gates).
• ⚛️ Qiskit Sampler back‑end and Aer simulators.
• 📊 Training & evaluation on the Iris dataset.
• 🔮 Inference playground (feature sliders → instant prediction).
• 🧱 Circuit rendering (matplotlib drawer).
• 🌐 One‑click Streamlit UI for demos.

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🗂 Repository Structure

Quantum-Enhanced-ML-Applications/
│
├─ notebooks/                 # Optional exploratory notebooks
├─ results/                   # Lightweight logs & predictions (no large files)
├─ src/
│  ├─ quantum_models.py       # Sampler-based classifier (Qiskit ≥ 1.x)
│  ├─ utils.py                # Data loading & preprocessing (Iris)
│  └─ visualization_utils.py  # Plotting helpers
│
├─ run_experiment.py          # CLI runner: train + evaluate
├─ streamlit_app.py           # 🌐 Streamlit UI
├─ requirements.txt           # Dependencies
├─ LICENSE
└─ README.md

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⚙️ Getting Started

Prerequisites

• Python 3.10–3.12 recommended
• (Windows) Microsoft C++ Build Tools may help for scientific wheels

1) Clone & create a virtual environment

git clone https://github.com/iamvisheshsrivastava/Quantum-Enhanced-ML-Applications.git
cd Quantum-Enhanced-ML-Applications

# Windows (PowerShell)
python -m venv venv
./venv/Scripts/Activate.ps1

# macOS/Linux
python -m venv venv
source venv/bin/activate

2) Install dependencies

python -m pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

If you don’t use the requirements file:

pip install qiskit qiskit-aer qiskit-machine-learning streamlit scikit-learn numpy matplotlib pandas python-dotenv

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▶️ Run the Streamlit App (recommended)

streamlit run streamlit_app.py

What you get:

• Train / Re‑train button (COBYLA via SciPy, configurable iterations)
• Metrics (accuracy, loss) + confusion matrix
• Inference playground (feature sliders → prediction)
• Circuit preview via Qiskit drawer
• Download CSV of predictions

Tip: The app can "match qubits to features" so Iris (4 features) uses 4 qubits automatically.

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🧪 Run From CLI

python run_experiment.py

This script loads the Iris dataset, trains the classifier, predicts on the test split, and renders a confusion matrix.

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🔐 (Optional) IBM Quantum Access

You can keep everything on simulators, or run on IBM Quantum later.

1. Create an IBM Quantum account and API token.
2. Add a local .env (already git‑ignored):

IBM_QUANTUM_TOKEN=your_token_here

3. In code, read the token with python-dotenv and select an IBM backend (future toggle in the app).

Never commit your token. .env is already ignored by .gitignore.

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📦 requirements.txt (reference)

# Core scientific stack
numpy
scipy
pandas
matplotlib
scikit-learn

# Quantum computing
qiskit
qiskit-aer
qiskit-machine-learning  # optional but handy

# App & utils
streamlit
python-dotenv

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🛠️ Troubleshooting (Qiskit ≥ 1.x)

• ModuleNotFoundError: qiskit.opflow → OpFlow was removed. Use Sampler and standard measurements.
• QuantumInstance missing → removed. Use Sampler/Estimator primitives instead.
• bind_parameters missing → renamed to assign_parameters.
• qiskit.algorithms.optimizers missing → use SciPy: from scipy.optimize import minimize with method="COBYLA".
• Simulators not found → ensure qiskit-aer is installed.

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🧭 Roadmap

• IBM hardware toggle in the UI (simulator vs real backend)
• Data re‑uploading feature maps & quantum kernels
• Benchmarks vs classical baselines
• Parameter save/load, experiment tracking
• More datasets & visual dashboards

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🤝 Contributing

1. Fork the repo & create a feature branch: git checkout -b feat/name
2. Commit changes: git commit -m "feat: ..."
3. Push & open a Pull Request.

Please keep results small; link large assets externally.

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📄 License

MIT — see LICENSE.

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📫 Contact

• Email: [email protected]
• LinkedIn: Vishesh Srivastava

If you use this project in your work, a star ⭐ on GitHub is appreciated!