The Classiq combinatorial optimization platform is a quantum software engine for optimization problems that you define. The engine tackles your formulated real-world optimization challenges and generates customized quantum circuits. You choose whether to run circuits on a quantum backend or a classical simulation, and receive the optimized solution. To gain further insights regarding the synthesis process, you can examine the generated circuit with the Classiq analyzer module.
This platform serves a wide range of users with differing backgrounds. On the one hand, the solution strategy is highly customizable so you can suit it to your preferences. On the other hand, if you have little or no quantum expertise, default solution strategies are available.
You describe new optimization problems using the Python SDK package. The problem is formulated using PYOMO, a Python-based, open-source optimization modeling language. The language supports a wide variety of problem types, such as integer linear programming, quadratic programming, graph theory problems, and SAT problems. Read in-depth reviews of the language’s capabilities in  and   . The basics of problem modelling in PYOMO and a complete example are in the problem formulation section.
The Classiq platform supports an extensive set of modeling configurations for your use (supported modeling). See examples of graph problems and integer linear programming in the problem library.
Solving Optimization Problems¶
The core Classiq capabilities are generation of a designated quantum solution, and execution of the generated algorithm on a quantum backend. Both capabilities are available as part of the Python SDK and demonstrated in the problem solving section.
The classiq platform relies on several established quantum algorithms to solve optimization problems. See the quantum algorithms section.
The solving algorithms are flexible and customizable, as demonstrated in the solver customization section.
 Pyomo Documentation 6.0.1, https://pyomo.readthedocs.io/en/stable/.
 Prof. Jeffrey Kantor’s Pyomo Cookbook https://jckantor.github.io/ND-Pyomo-Cookbook/.
 J. D. Siirola, Introduction to Pyomo: The optimization foundation for IDAES, https://www.osti.gov/servlets/purl/1524963 (2018).