用学习模型为 QAOA 生成变分角度，替代逐实例优化
Learning angles for quantum approximate optimization
量子优化里最耗时的角度调参，现在一个前向传播就能搞定，还开源了，做 QAOA 的可以看看。
QAOA 是求解组合优化问题的主流量子算法，其性能依赖一组需要针对每个问题实例做非凸优化的变分角度。该研究训练模型直接从问题实例映射到高质量 QAOA 角度，单次前向传播比传统求解器快数个数量级，质量接近相同。模型可泛化到未见过的 Barabási-Albert 拓扑和 2D 网格图。量子硬件实验显示，模型生成的角度与求解器角度产出的采样结果无法区分，模型已开源。
Learning angles for quantum approximate optimization
The Quantum Approximate Optimization Algorithm (QAOA) and its variants are leading algorithms to help solve challenging combinatorial optimization problems. QAOA maps these problems to Ising Hamiltonians such that their ground state is the desired solution. Candidate solutions are then drawn from a variational quantum circuit. The performance of QAOA hinges on a set of variational angles. However, finding good angles is an instance-specific and computationally expensive non-convex classical optimization task. Here, we learn the mapping from the problem instance to high-quality QAOA angles. Our learned models replace the per-instance optimization, that we call the solver, with a single forward pass that is several orders of magnitude faster than the solver with a near identical quality. Our models further generalize to unseen topologies such as Barabási-Albert and 2D grid graphs. Experiments on quantum hardware confirm that QAOA samples with angles produced by our open-source models are indistinguishable from solver angles. Crucially, models sensitive to the topology of the problem instance increase the quality of the QAOA angles. Our work amortizes a major bottleneck in QAOA by an up-front data generation and training cost. Importantly, this cost can be amortized over time in settings where many similar problems are optimized on a regular basis, as is often the case in applications for industry.
