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quant-ph202410 cited

Preparing Schrödinger cat states in a microwave cavity using a neural network

Hector Hutin, Pavlo Bilous, Chengzhi Ye +8

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 ch…

quant-ph20243 cited

Discovering Local Hidden-Variable Models for Arbitrary Multipartite Entangled States and Arbitrary Measurements

Nick von Selzam, Florian Marquardt

Measurement correlations in quantum systems can exhibit non-local behavior, a fundamental aspect of quantum mechanics with applications such as device-independent quantum informati…

quant-ph2023

Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology

Federico Belliardo, Fabio Zoratti, Florian Marquardt +1

Quantum sensors offer control flexibility during estimation by allowing manipulation by the experimenter across various parameters. For each sensing platform, pinpointing the optim…

quant-ph2023

Reservoir Engineering for Classical Nonlinear Fields

Benedikt Tissot, Hugo Ribeiro, Florian Marquardt

Reservoir engineering has become a prominent tool to control quantum systems. Recently, there have been first experiments applying it to many-body systems, especially with a view t…

quant-ph2023

Fast quantum control of cavities using an improved protocol without coherent errors

Jonas Landgraf, Christa Flühmann, Thomas Fösel +2

The selective number-dependent arbitrary phase (SNAP) gates form a powerful class of quantum gates, imparting arbitrarily chosen phases to the Fock states of a cavity. However, for…