论文标题
量子近似优化算法的经典变异模拟
Classical variational simulation of the Quantum Approximate Optimization Algorithm
论文作者
论文摘要
量子计算中的一个关键开放问题是，量子算法是否可以潜在地比经典算法为实践意义的任务提供重要的优势。了解模拟量子系统中经典计算的极限是解决此问题的重要组成部分。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许多变异量子算法背后的结构。使用多数波波函数的神经网络参数化，重点是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 QAOA层上达到54个QUBITS，大约实现了324个RZZ大门和216 RX大门，而无需大规模计算资源。对于较大的系统，我们的方法可用于在先前未开发的参数值下提供准确的QAOA模拟，并基于嘈杂的中间尺度量子（NISQ）ERA中的下一代实验基准。
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.
