Fast Simulation of High-Depth QAOA Circuits

Classical simulation is a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA). Our simulator is designed with the goal of reducing the computational cost of QAOA parameter optimization and supports both CPU and GPU execution. Our central observation is that the computational cost of both simulating the QAOA state andcomputing the QAOA objective to be optimized canbe reduced by precomputing the diagonal Hamiltonian encoding the problem. We reduce the time for a typical QAOA parameter optimization by eleven times for n = 26 qubits compared to a state-of-the-art GPU quantum circuit simulator.

The simulator supports various QAOA problems such as MaxCut, LABS and portfolio optimization, as well as usage of Hamming weight preserving mixers.

Our simulator supports a variety of hardware from CPU to large-scale distributed GPU supercomputers. We demonstrate scaling of distributed simulations and compare MPI communication with custom communication implemented in the cuQuantum library. Our simulator is available on GitHub: https://github.com/jpmorganchase/QOKit