Proactively incremental-learning QAOA
Solving optimization problems with high performance is the target of existing works of Quantum Approximate Optimization Algorithm (QAOA). With this intention, we propose an advanced QAOA based on incremental learning, where the training trajectory is proactively segmented into incremental phases. Taking the MaxCut problem as our example, we randomly select a small subgraph from the whole graph and train the quantum circuit to get optimized parameters for the MaxCut of the subgraph in the first phase. Then in each subsequent incremental phase, a portion of the remaining nodes and edges are added to the current subgraph, and the circuit is retrained to get new optimized parameters. The above operation is repeated until the MaxCut problem on the whole graph is solved. The key point is that the optimized parameters of the previous phase will be reused in the initial parameters of the current phase. Numerous simulation experiments show our method has superior performance on Approximation Ratio (AR) and training time compared to prevalent works of QAOA. Specifically, the AR is higher than standard QAOA by 13.17% on weighted random graphs.