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

Quantum Science and Technology 8, 025004 (2023)​

Jonathon Brown, Mauro Paternostro and Alessandro Ferraro

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.


Project manager
Irene Spagnul
Department of Physics
University of Trieste


Co-funded by the European Commission’s
Horizon Europe Programme under GA 101046973

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