We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p = 1, 2 rounds is executed on the 127 qubit heavy-hex graph gate-model quantum computer ibm_washington, using on-device grid-searches for angle finding, and QA is executed on two Pegasus-chip D-Wave quantum annealers. The problems are random Ising models whose connectivity matches heavy-hex graphs and the Pegasus graph connectivity, and optionally include hardware-compatible cubic terms (ZZZ terms). The QAOA circuits are heavily optimized and of extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of the heavy-hex lattice. QAOA and QA are both compared against simulated annealing and the optimal solutions are computed exactly using CPLEX. The noiseless mean QAOA expectation values for p = 1, 2 are computed using classical light-cone based simulations. We find QA outperforms QAOA on the evaluated devices.

我们在 127 个量子位问题实例上对 QAOA（量子交替算子 Ansatz）和 QA（量子退火）进行了直接比较。p  = 1、2 轮的 QAOA 在 127 量子位重六角图门模型量子计算机 ibm_washington 上执行，使用设备上网格搜索进行角度查找，QA 在两个 Pegasus 芯片 D-Wave 量子退火器上执行。问题是随机 Ising 模型，其连接性与重六角图和 Pegasus 图连接性相匹配，并且可以选择包含硬件兼容的三次项（Z Z Z项）。QAOA 电路经过深度优化，深度极短，每轮 CNOT 深度为 6，允许整个芯片使用重型六角晶格。QAOA 和 QA 均与模拟退火进行比较，并使用 CPLEX 精确计算最佳解决方案。p = 1, 2时的无噪声平均 QAOA 期望值 是使用基于经典光锥的模拟计算的。我们发现 QA 在评估的设备上优于 QAOA。