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 wavefunction 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.

量子计算中一个关键的悬而未决的问题是，对于实际感兴趣的任务，量子算法是否可以提供优于经典算法的显着优势。理解经典计算在模拟量子系统方面的局限性是解决这个问题的一个重要组成部分。我们介绍了一种模拟由参数化门组成的分层量子电路的方法，这是许多适用于近期量子计算机的变分量子算法背后的架构。使用多量子位波函数的神经网络参数化，重点关注与量子近似优化算法 (QAOA) 相关的状态。对于模拟的最大电路，我们在 4 个 QAOA 层达到了 54 个量子位，大约实现了 324 个 RZZ 门和 216 个 RX 门，而无需大规模计算资源。