Article Dans Une Revue Physical Review Applied Année : 2024

Harnessing two-photon dissipation for enhanced quantum measurement and control

Antoine Essig
Joachim Cohen
Théau Peronnin
Sébastien Jezouin
Raphaël Lescanne

Résumé

Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In this work, we demonstrate experimentally a neural-network-based preparation of Schr\"odinger cat states in a cavity coupled dispersively to a qubit. We show that it is possible to teach a neural network to output optimized control pulses for a whole family of quantum states. After being trained in simulations, the network takes a description of the target quantum state as input and rapidly produces the pulse shape for the experiment, without any need for time-consuming additional optimization or retraining for different states. Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates.

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hal-04743642 , version 1 (20-10-2024)

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Antoine Marquet, Simon Dupouy, Ulysse Réglade, Antoine Essig, Joachim Cohen, et al.. Harnessing two-photon dissipation for enhanced quantum measurement and control. Physical Review Applied, 2024, 22 (3), pp.034053. ⟨10.1103/PhysRevApplied.22.034053⟩. ⟨hal-04743642⟩
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