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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 programs. You choose whether to run the programs 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 synthesized program with the Classiq analyzer module.

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Formulating Problems

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 [ [1] ](#pyomo documentation) and [ [2] ](#pyomo cookbook) [ [3] ](#pyomo intro). The basics of problem modelling in PYOMO and a complete example are in the problem formulation section. The platform supports an extensive set of modeling configurations for your use (supported modeling).

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Solving Optimization Problems

The core Classiq capabilities are generation of a designated quantum solution, and execution of the generated algorithm on a quantum backend. The platform relies on the QAOA penalty algorithm to solve optimization problems.

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References

[1] Pyomo Documentation 6.0.1, https://pyomo.readthedocs.io/en/stable/. [2] Prof. Jeffrey Kantor’s Pyomo Cookbook https://jckantor.github.io/ND-Pyomo-Cookbook/. [3] J. D. Siirola, Introduction to Pyomo: The optimization foundation for IDAES, https://www.osti.gov/servlets/purl/1524963 (2018).