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arXiv · 2206.14681

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

Jonathon Brown·Mauro Paternostro·Alessandro Ferraro
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Abstract

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.

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Jonathon Brown, Mauro Paternostro, Alessandro Ferraro. 2023-01-25. Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks. https://doi.org/10.1088/2058-9565%2Facb2f2

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