The Classiq combinatorial optimization platform is a quantum software engine for user-defined optimization problems. It allows users to formulate real-world optimization challenges, and generate customized quantum circuits that tackle them. The user may choose to run circuits on either a quantum backend or a classical simulation, and receive the optimized solution. To gain further insights regarding the synthesis process, the generated circuit may be examined using Classiq’s analyzer module.
This platform may serve a wide range of users with differing backgrounds. On the one hand, the solution strategy is highly customizable and may be fitted to the user's preferences. On the other hand, default solution strategies are available, enabling use which doesn't require any quantum expertise.
The user may insert 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, SAT problems, and many more. More in-depth reviews about the language’s capabilities are available in the following links    . In the problem formulation section, we explain the basics of problem modelling in PYOMO and provide a complete example.
Supported modeling configuration¶
The Classiq platform supports an extensive set of modeling configurations, as is elaborated on in the supported modeling section. Many optimization problems may be modeled using these configurations. We give an overview of several examples in the field of graph problems and integer linear programming in the problem library.
Solve Optimization problem¶
So far, we went through the optimization model formulation. Now we will present the core Classiq capabilities: generation of a designated quantum solution, and execution of the generated algorithm on a quantum backend. Both are available as part of the python SDK.
In the problem solving section we demonstrate these options.
The classiq platform relies on several established quantum algorithms to solve optimization problems. These algorithms are presented in the quantum algorithms section.
The aforementioned solving algorithms are flexible and customizable, as demonstrated in the solver customization page.
 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)