Transfer learning of optimal QAOA parameters in combinatorial optimization

Understand

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them.

• However, multiple factors make the parameter search of the QAOA a hard optimization problem.
• In this work, we study transfer learning (TL), a methodology to reuse pre-trained QAOA parameters of one problem instance into different COP instances.
• This methodology can be used to alleviate the necessity of classical optimization to find good parameters for individual problems.