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Found 3 papers, 2 papers with code
Automatic re-calibration of quantum devices by reinforcement learning
During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings.
A semi-agnostic ansatz with variable structure for quantum machine learning
Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization.
Real-time calibration of coherent-state receivers: learning by trial and error
The optimal discrimination of coherent states of light with current technology is a key problem in classical and quantum communication, whose solution would enable the realization of efficient receivers for long-distance communications in free-space and optical fiber channels.
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