While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the Deepmind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden-variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods. It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics.

尽管已经开发出了许多算法来优化不同目标的量子动力学，但是一个共同的局限性是依赖于良好的初始猜测，可以是随机的，也可以是基于启发式和直觉的。在这里，我们实现了一个表格Deepmind AlphaZero算法的深度量子探索版本，可系统地避免这一限制。AlphaZero在引导树搜索中结合了深度神经网络和深度神经网络，可用于量子参数态势的预测隐变量近似。为了强调可传递性，我们仅使用一组常见的算法超参数就三类控制问题应用该算法并对其进行基准测试。与以前的方法相比，AlphaZero在良好解决方案簇的质量和数量上都实现了重大改进。它能够自发地学习解决方案中意想不到的隐藏结构和全局对称性，甚至超越了人类的启发式方法。