🌀 QuantumSim-Framework

QuantumSim-Framework is a comprehensive Python-based quantum computing simulation toolkit designed to model, optimize, and visualize quantum algorithms with classical hardware constraints in mind. Built for developers and researchers, it provides a modular architecture for simulating quantum circuits using core gates (Hadamard, CNOT, SWAP, etc.), managing task dependencies via priority queues, and mitigating errors through advanced techniques like Richardson extrapolation. The framework supports real-time visualization of quantum states (Bloch spheres, probability distributions) and circuits, while offering adaptive resource scheduling for parallel task execution. With integrated benchmarking tools, noise modeling, and hardware-aware compilation, it enables users to explore algorithm performance under realistic conditions, test error-correction strategies, and optimize quantum workflows. Ideal for prototyping hybrid quantum-classical algorithms, studying decoherence effects, or teaching quantum computing concepts, QuantumSim-Framework bridges the gap between theoretical quantum mechanics and practical implementation.

🚀 Table of Contents

• ✨ Features
• ⚙️ Installation
• 📊 Architecture
• 🔍 Usage Examples
• 📚 Documentation
• 🤝 Contributing
• 📜 License

✨ Features

Feature	Description	Icon
Quantum Gates	Implement 8+ core quantum gates (H, CNOT, SWAP, etc.)	⚛️
Task Scheduling	Priority-based quantum task queue with dependency management	🎛️
Error Mitigation	Richardson extrapolation and zero-noise extrapolation techniques	🛡️
Visualization	Circuit diagrams, Bloch spheres, and probability distributions	📊
Resource Management	Thread-safe quantum resource allocation system	💻
Benchmarking	Comparative analysis of quantum algorithms	📈

⚙️ Installation

# Clone repository
git clone https://github.com/yourusername/QuantumSim-Framework.git
cd QuantumSim-Framework

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
.\.venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

📊 Architecture

%% Quantum Simulation Workflow
sequenceDiagram
    participant User
    participant Scheduler
    participant ResourceManager
    participant QuantumWorker
    participant Hardware
    
    User->>Scheduler: Submit QuantumTask
    Scheduler->>ResourceManager: Allocate Resources
    ResourceManager-->>Scheduler: Resource Token
    Scheduler->>QuantumWorker: Execute Task
    QuantumWorker->>Hardware: Run Circuit
    Hardware-->>QuantumWorker: Measurement Result
    QuantumWorker-->>User: Final Result

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🔍 Usage Examples

Grover's Search Algorithm

from quantum import GroverSearch, QuantumTaskFactory

# Create Grover's search task for 3-qubit system
grover_task = QuantumTaskFactory.create_grover_search_task(
    task_id=1,
    complexity=3,
    priority=5,
    n_qubits=3,
    target_state=0b101
)

# Execute and visualize results
result = grover_task.execute()
print(f"Measured state: {bin(result)}")

Circuit Visualization

from quantum import QuantumCircuitVisualizer

# Generate circuit diagram
circuit = QuantumCircuit(2)
circuit.add_gate(QuantumGate.HADAMARD, 0)
circuit.add_gate(QuantumGate.CNOT, 1, 0)
QuantumCircuitVisualizer.draw_circuit(circuit)

📚 Documentation

Component	Description	API Reference
QuantumGate	Enum of quantum gate operations	Gates Docs
QuantumState	State vector manipulation	State Docs
QuantumHyperThreading	Parallel task execution manager	Threading Docs
QuantumErrorMitigation	Noise simulation and error correction	Error Docs

🤝 Contributing

1. Fork the repository
2. Create your feature branch:
   git checkout -b feature/AmazingFeature
3. Commit changes:
   git commit -m 'Add some AmazingFeature'
4. Push to branch:
   git push origin feature/AmazingFeature
5. Open a Pull Request

📜 License

Distributed under the MIT License. See LICENSE for more information.