Quantum  Quantitative

Double Quant

Double Quant - A high-performance quantum computing framework for solving quantitative finance problems.

🌟 Overview

Double Quant is a framework that bridges quantum computing and quantitative finance. By leveraging quantum algorithms and advanced optimization techniques, it provides efficient solutions to complex financial computational problems.

Key Features

• 🚀 Optimized Quantum Solvers: State-of-the-art quantum solvers, including lots of bottom-level optimization algorithms.
• 🎯 Three-Layer Architecture: Clean separation of concerns with Data → Solver → Application layers.
• 📊 Type-Safe Design: Full type hints support for better IDE integration and code reliability.
• 🔧 Easy to Use: Intuitive API design that makes quantum finance accessible to practitioners.
• ⚙️ Modular Structure: Flexible components that can be combined for various financial applications.

🏗️ Architecture

Double Quant follows a well-designed three-layer architecture:

┌─────────────────────────────────────┐
│         Application Layer           │
│      (Portfolio, Pricing, ...)      │
└─────────────────────────────────────┘
                  ↓
┌─────────────────────────────────────┐
│           Solver Layer              │
│             (VQA, HHL, ...)         │
└─────────────────────────────────────┘
                  ↓
┌─────────────────────────────────────┐
│            Data Layer               │
│        (Stock data, ...)            │
└─────────────────────────────────────┘

Layer Responsibilities

• Data Layer (double_quant.common): Core data structures and utilities.

• Solver Layer (double_quant.solver): Quantum algorithms and solvers.

• Application Layer: High-level financial applications

  • Portfolio optimization
  • Option pricing
  • Risk analysis
  • (More coming soon...)

📦 Installation

Prerequisites

• Python 3.11 or higher
• uv package manager (recommended)

Install with uv (Recommended)

# Clone the repository
git clone https://github.com/yourusername/double-quant.git
cd double-quant

# Install dependencies
uv sync

Install with pip

# Clone the repository
git clone https://github.com/yourusername/double-quant.git
cd double-quant

# Install in development mode
pip install -e .

🚀 Quick Start

Example: Solving a Linear System

from double_quant import LinearSystem, QuantumLinearSolver

# Define a linear system Ax = b
A = [[4, 1], [1, 3]]
b = [1, 2]

# Create a linear system model
system = LinearSystem(A, b)

# Initialize quantum solver
solver = QuantumLinearSolver()

# Solve the system
solution = solver.solve(system)

print(f"Solution: {solution}")

Example: Using SAPO Optimizer

from double_quant import SAPO, LinearSystem
from double_quant.solver import QuantumLinearSolver

# Create a linear system
system = LinearSystem([[2, 1], [1, 2]], [1, 1])

# Use SAPO optimizer for better performance
optimizer = SAPO()
solver = QuantumLinearSolver(optimizer=optimizer)

# Solve with optimization
solution = solver.solve(system)

🔬 Core Components

Quantum Linear Solver

The QuantumLinearSolver implements the HHL (Harrow-Hassidim-Lloyd) algorithm, one of the most important quantum algorithms for solving linear systems exponentially faster than classical methods.

📚 Documentation

For detailed documentation, API reference, and tutorials, visit our documentation site (coming soon).

🛠️ Development

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=double_quant

Building the Package

uv build

Code Style

This project follows ruff guidelines and uses type hints throughout.

🤝 Contributing

We welcome contributions! Whether it's bug reports, feature requests, or code contributions, please feel free to get involved.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat(scope): add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Commit Message Format

We follow the Angular commit message convention:

<type>(<scope>): <description>

[optional body]

[optional footer]

Types: feat, fix, docs, style, refactor, test, chore

📄 License

This project is licensed under the GPLv3 - see the LICENSE file for details.

🙏 Acknowledgments

This project draws inspiration from:

• Qiskit - Open-source quantum computing framework.
• Qiskit Finance - Quantum algorithms for finance and its architecture.

🗺️ Roadmap

• Core linear system solver with HHL algorithm
• Portfolio optimization applications
• Option pricing modules
• Risk analysis tools
• Integration with major quantum computing platforms
• Comprehensive documentation and tutorials
• Performance benchmarks
• Real-world case studies