Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks

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), fast preparation times, 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.

Automated summary

We use machine learning and evolutionary algorithms to engineer quantum states in superconducting platforms. By optimizing the time-dependent couplings between qubits and a common driven microwave resonator, we achieve high quantum fidelities and fast preparation times for various target states. The genetic algorithm proves to be effective in controlling large quantum systems, even in the presence of noise.