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Our hybrid solver for QUBO problems merges classical and quantum computing, efficiently solving complex optimization tasks. It offers fast, accurate results for applications in logistics, finance, and more.

Project description

QDeep Hybrid Solver - Quadratic Unconstrained Binary Optimization Solver

QDeep Hybrid Solver is a Python library specifically developed for efficiently solving Quadratic Unconstrained Binary Optimization (QUBO) problems through advanced hybrid quantum-classical computing methods. It provides a user-friendly interface to submit QUBO matrices and retrieve structured, optimized solutions.

Mathematical Formulation

A QUBO problem is mathematically expressed as:

1742437383961

where:

  • (x) is a binary vector.
  • (Q) is a symmetric matrix representing the problem's coefficients.

Features

  • Type-hinted implementation using Python's TypedDict
  • Comprehensive input validation for matrices
  • Secure authentication token management
  • Structured and detailed response handling

Installation

Install the required dependencies along with the solver:

pip install numpy requests qdeepsdk

Dependencies

  • numpy
  • requests
  • qdeepsdk

Usage

import numpy as np
from qdeepsdk import QDeepHybridSolver

# Initialize solver
solver = QDeepHybridSolver()

# 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("Hybrid Solver Results:", results)
  
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 structured dictionary with the following format:

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

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

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