论文标题
通过遗传算法控制的最佳量子控制，用于驱动谐振器介导的网络中的量子状态工程
Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks
论文作者
论文摘要
我们采用基于进化算法的量子状态工程采用机器学习方法。特别是，我们专注于超导平台，并考虑一个Qubits网络 - 在没有直接耦合的人造原子状态下编码 - 通过常见的单模微波驱动的微波谐振器进行交互。假定量子谐振器耦合在共振方案中，并且可以及时调节。使用遗传算法来找到耦合的功能时间依赖性，以优化演变状态和各种目标之间的保真度，包括三Q级GHz和Dicke State和Dicke State和四Qubit态度。尽管在理想的无噪声设置中训练了算法，但我们观察到高量子保真度（在最坏的情况下，在有效尺寸96的最坏情况下高于0.96）和对噪声的弹性。这些结果表明，遗传算法代表了控制大维量子系统的有效方法。
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. The qubit-resonator couplings are assumed to be in the resonant regime and tunable in time. A genetic algorithm is used in order to find the functional time-dependence of the couplings that optimise the fidelity between the evolved state and a variety of targets, including three-qubit GHZ and Dicke states and four-qubit graph states. We observe high quantum fidelities (above 0.96 in the worst case setting of a system of effective dimension 96) and resilience to noise, despite the algorithm being trained in the ideal noise-free setting. These results show that the genetic algorithms represent an effective approach to control quantum systems of large dimensions.
