Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design. Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems. We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability. In application to Grover search, our RL-design automatically produces an adiabatic quantum algorithm that has the quadratic speedup. We find for all our studied cases that quantitatively the RL-designed algorithm has a better performance compared to the analytically constructed non-linear Hamiltonian path when the encoding Hamiltonian is solvable, and that this RL-design approach remains applicable even when the non-linear Hamiltonian path is not analytically available. In 3-SAT, we find RL-design has fascinating transferability---the adiabatic algorithm obtained by training on a specific choice of clause number leads to better performance consistently over the linear algorithm on different clause numbers. These findings suggest the applicability of reinforcement learning for automated quantum adiabatic algorithm design. Further considering the established complexity-equivalence of circuit and adiabatic quantum algorithms, we expect the RL-designed adiabatic algorithm to inspire novel circuit algorithms as well. Our approach is potentially applicable to different quantum hardwares from trapped-ions and optical-lattices to superconducting-qubit devices.

中文翻译：

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使用强化学习的量子绝热算法设计

量子算法设计在利用量子设备的计算优势方面起着至关重要的作用。在这里，我们为量子绝热算法设计开发了一种基于深度强化学习的方法。我们的方法一般适用于解决难以找到但易于验证的一类问题，例如搜索和 NP 完全问题。我们在 Grover-search 和 3-SAT 问题中对这种方法进行了基准测试，并发现通过我们的 RL 方法获得的绝热算法显着提高了最终成功概率。在 Grover 搜索的应用中，我们的 RL 设计自动生成具有二次加速比的绝热量子算法。我们发现，对于我们所有研究的案例，当编码哈密顿量可解时，RL 设计的算法在数量上比分析构造的非线性哈密顿路径具有更好的性能，并且这种 RL 设计方法即使在非线性哈密​​顿路径在解析上不可用。在 3-SAT 中，我们发现 RL-design 具有迷人的可迁移性——通过对特定子句编号进行训练获得的绝热算法在不同子句编号上的性能始终优于线性算法。这些发现表明强化学习在自动化量子绝热算法设计中的适用性。进一步考虑电路和绝热量子算法的既定复杂性等效性，我们希望 RL 设计的绝热算法也能激发新的电路算法。我们的方法可能适用于从俘获离子和光晶格到超导量子位器件的不同量子硬件。