论文标题
在平面超导处理器上对非平面图问题的量子近似优化
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
论文作者
论文摘要
我们演示了Google Sycamore超导量子量子处理器与量子近似优化算法（QAOA）的应用。像过去的QAOA实验一样，我们研究了硬件（平面）连接图上定义的问题的性能；但是，我们还将QAOA应用于Sherrington-Kirkpatrick模型和Maxcut，这都是QAOA需要大量汇编的高维图问题。对QAOA能量景观的实验扫描与在所研究的最大实例（23 QUAT）中与理论表现出了很好的一致性，我们能够成功地进行各种优化。对于在我们的硬件图上定义的问题，我们获得了一个近似值，该近似值与问题大小无关，并且首次观察性能随电路深度而增加。对于需要汇编的问题，绩效随问题的规模而下降，但对于涉及数千个大门的电路的随机猜测仍然可以提供优势。这种行为强调了使用近期量子计算机优化与硬件连接不同的图表上的问题的挑战。由于这些图更代表了现实世界实例，因此我们的结果倡导更多地重点是将QAOA用作量子处理器的整体设备级别的基准的发展传统中。
We demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the (planar) connectivity graph of our hardware; however, we also apply the QAOA to the Sherrington-Kirkpatrick model and MaxCut, both high dimensional graph problems for which the QAOA requires significant compilation. Experimental scans of the QAOA energy landscape show good agreement with theory across even the largest instances studied (23 qubits) and we are able to perform variational optimization successfully. For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size but still provides an advantage over random guessing for circuits involving several thousand gates. This behavior highlights the challenge of using near-term quantum computers to optimize problems on graphs differing from hardware connectivity. As these graphs are more representative of real world instances, our results advocate for more emphasis on such problems in the developing tradition of using the QAOA as a holistic, device-level benchmark of quantum processors.
