# Fast Simulation of High-Depth QAOA Circuits

arXiv: https://arxiv.org/abs/2309.04841 · HTML: https://mlsanity.com/paper/2309.04841/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2023.

## Built on by (most cited first, 6 of 6)

- [TQml Simulator: optimized simulation of quantum machine learning](https://mlsanity.com/paper/2506.04891/) (2025)
- [Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm](https://mlsanity.com/paper/2403.03310/) (2024)
- [Generalized Probabilistic Approximate Optimization Algorithm](https://mlsanity.com/paper/2507.07420/) (2025)
- [Dynamical Lie Algebras Cannot Describe Shallow QAOA: Cragged Terrains, Barren Plateaus, and Empirical Hardness Models](https://mlsanity.com/paper/2608.04252/) (2026)
- [LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones](https://mlsanity.com/paper/2608.05610/) (2026)
- [RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation](https://mlsanity.com/paper/2608.05646/) (2026)

## Abstract (arXiv)

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