A Framework to Formulate Pathfinding Problems for Quantum Computing

With the applications of quantum computing becoming more and more widespread, finding ways that allow end users without experience in the field to apply quantum computers to solve their individual problems is becoming a crucial task. However, current optimization algorithms require problem instances to be posed in complex formats that are challenging to formulate, even for experts. In particular, the Quadratic Unconstrained Binary Optimization (QUBO) formalism employed by many quantum optimization algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), involves the mathematical rewriting of constraints under strict conditions. To facilitate this process, we propose a framework to automatically generate QUBO formulations for pathfinding problems. This framework allows users to translate their specific problem instances into formulations that can be passed directly to quantum algorithms for optimization without requiring any expertise in the field of quantum computing. It supports three different encoding schemes that can easily be compared without requiring manual reformulation efforts. The resulting QUBO formulations are robust and efficient, reducing the previously tedious and error-prone reformulation process to a task that can be completed in a matter of seconds. In addition to an open-source Python package available on https://github.com/cda-tum/mqt-qubomaker, we also provide a graphical user interface accessible through the web (https://cda-tum.github.io/mqt-qubomaker/), which can be used to operate the framework without requiring the end user to write any code.

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