qdeepsdk 0.1.0

QDeep QUBO Solver

# QUBOSolver - Quadratic Unconstrained Binary Optimization Solver

QUBOSolver is a Python library for solving Quadratic Unconstrained Binary Optimization (QUBO) problems using multiple algorithms via an external API service. It provides a clean interface to submit QUBO matrices and retrieve structured results from different solving algorithms.

## Features

- Supports multiple solving algorithms:
  - Simulated Bifurcation
  - Tensor Train
  - Simulated Annealing
- Type-hinted implementation using Python's TypedDict
- Input validation for matrices
- Authentication token management
- Structured response handling

## Installation

```bash
pip install numpy requests

Note: This library requires numpy and requests as dependencies.

Usage

import numpy as np
from qdeepsdk import QUBOSolver

# Initialize solver
solver = QUBOSolver()

# Set authentication token
solver.token = "your-auth-token-here"

# Create a QUBO matrix
matrix = np.array([
    [-1,  2,  2],
    [ 0, -1,  2],
    [ 0,  0, -1]
])

# Solve the QUBO problem
try:
    results = solver.solve(matrix)
  
    # Access results
    print("Simulated Bifurcation:", results["SimulatedBifurcation"])
    print("Tensor Train:", results["TensorTrain"])
    print("Simulated Annealing:", results["SimulatedAnnealer"])
  
except ValueError as e:
    print(f"Error: {e}")
except requests.RequestException as e:
    print(f"API Error: {e}")

API Response Structure

The solver returns a SolveResult dictionary with the following structure:

{
    "SimulatedBifurcation": {
        "configuration": List[int],  # Solution vector
        "energy": float,            # Energy of the solution
        "time": float              # Computation time in seconds
    },
    "TensorTrain": {
        "configuration": List[int],
        "energy": float,
        "time": float
    },
    "SimulatedAnnealer": {
        "configuration": List[int],
        "energy": float,
        "time": float
    }
}

Requirements

• Python 3.7+
• NumPy
• Requests

The library includes comprehensive error handling for:

- Invalid or missing authentication tokens
- Non-square matrices
- Non-2D matrices
- Non-numpy array inputs
- API connection issues