Classical variational simulation of the Quantum Approximate Optimization Algorithm

TL;DR

This paper presents a neural-network-based classical simulation method for layered quantum circuits, enabling efficient simulation of QAOA states with up to 54 qubits, aiding benchmarking of near-term quantum algorithms.

Contribution

Introduces a neural-network approach to simulate large-scale QAOA circuits classically, expanding the capacity to benchmark and analyze near-term quantum algorithms.

Findings

Simulated up to 54 qubits with 4 QAOA layers.

Achieved efficient simulation of 324 RZZ and 216 RX gates.

Provided a tool for benchmarking NISQ-era quantum algorithms.

Abstract

A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is an important component of addressing this question. We introduce a method to simulate layered quantum circuits consisting of parametrized gates, an architecture behind many variational quantum algorithms suitable for near-term quantum computers. A neural-network parametrization of the many-qubit wave function is used, focusing on states relevant for the Quantum Approximate Optimization Algorithm (QAOA). For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers, approximately implementing 324 RZZ gates and 216 RX gates without requiring large-scale computational resources. For larger systems, our approach can be used to provide accurate QAOA simulations at previously unexplored parameter values and to benchmark the next generation of experiments in the Noisy Intermediate-Scale Quantum (NISQ) era.