PyQBPP Document

This documentation for PyQBPP (Python binding of QUBO++) is currently under development. Some pages may contain incomplete or provisional information.

Getting Started

1. HUBO and QUBO
2. Quick Start
3. Installation

Basics

This section provides a step-by-step introduction to PyQBPP. By reading the pages in order, you will learn how to define variables and expressions, model optimization problems, and solve them using PyQBPP. After completing this tutorial, you should be able to use PyQBPP for most typical applications.

1. Defining Variables and Creating Expressions
2. Solving Expressions
3. Array of Variables and Array Functions
4. Solving Partitioning Problem Using Array of Variables
5. Permutation Matrix and Solving Assignment Problem
6. Integer Variables and Solving Simultaneous Equations
7. Factorization Through HUBO Expression
8. Range Constraints and Solving Integer Linear Programming

Topics

This section provides topic-wise explanations of selected features of PyQBPP. Each page focuses on a specific topic and offers deeper insights into design decisions, usage patterns, and, where appropriate, internal implementations.

1. Data Types of Variables and Expressions
2. Basic Operators and Functions
3. Basic Operators and Functions for Arrays
4. Multi-dimensional Variables and Expressions
5. Comparison Constraints
6. Expression Classes
7. Evaluating Expressions
8. Replace Functions
9. Negated Literals
10. Sum Functions for Multi-dimensional Arrays
11. Einsum Function for Tensor Contraction
12. Slice and Concat
13. One-Hot to Integer Conversion
14. Easy Solver Usage
15. Exhaustive Solver Usage
16. ABS3 Solver Usage
17. Gurobi Optimizer Usage
18. Search Parameters
19. Performance Tips

Case Studies

This section provides examples of solving optimization, mathematical, and graph problems using PyQBPP. See the Case Studies page for the full list.

Quick References

1. Variables and Expressions
2. Operators and Functions
3. Operations and Functions for Integer Variables and Constraints
4. Solutions

Experimental Solver Support

Experimental Solver Support — PyQBPP-only experimental integrations that let you call third-party solvers through a unified interface, including Fixstars Amplify, D-Wave (Advantage / Leap Hybrid / Neal / Tabu / Steepest Descent), dimod ExactSolver, OpenJij, TYTAN-SDK MIKAS, qubovert, Simulated Bifurcation, IBM CPLEX, IBM Qiskit Optimization, and Google OR-Tools CP-SAT.