A Depth-Progressive Initialization Strategy for Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) is known for its capability and universality in solving combinatorial problems on near-term devices. results yielded by QAOA depend strongly initial variational parameters. Hence, parameter selection becomes an active area of research, as bad initialization might deteriorate the quality results, especially at great circuit depths. We first discuss patterns optimal parameters two directions: angle index depth. Then, we symmetries periodicity expectation that used to determine bounds search space. Based restriction, propose a strategy predicts new taking difference between previous Unlike most other strategies, does not require multiple trials ensure success. It only requires one prediction when progressing next compare this with our previously proposed layerwise Max-cut problem terms approximation ratio cost. also address non-optimality parameters, which seldom discussed works despite importance explaining behavior algorithms.