Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm

arXiv:2403.03310v1 Announce Type: cross
Abstract: In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential to efficiently solve the Max-Cut problem, a quintessential example of combinatorial optimization. However, practical application faces challenges due to current limitations on quantum computational resource. Our work optimizes QAOA initialization, using …

abstract algorithm arxiv computational computing cs.lg graph graph learning novel optimization prediction quant-ph quantum quantum computing type