Fast Simulation of High-Depth QAOA Circuits

TL;DR

This paper introduces a fast classical simulator for high-depth QAOA circuits that significantly reduces computational costs, enabling more efficient quantum algorithm development on classical hardware.

Contribution

The authors develop a novel QAOA simulator that precomputes diagonal Hamiltonian encodings, achieving an elevenfold speedup over existing GPU-based simulators for 26 qubits.

Findings

Achieves eleven times faster simulation for 26 qubits.

Supports both CPU and GPU execution.

Available as open-source on GitHub.

Abstract

Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains 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 and computing the QAOA objective to be optimized can be 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 based on cuQuantum. Our simulator is available on GitHub: https://github.com/jpmorganchase/QOKit