# Convergence analysis for autonomous adaptive learning applied to quantum architectures

@inproceedings{Gupta2019ConvergenceAF, title={Convergence analysis for autonomous adaptive learning applied to quantum architectures}, author={Riddhi Swaroop Gupta and Michael J. Biercuk}, year={2019} }

We present a formal analysis and convergence proofs for an autonomous adaptive learning algorithm useful for the tuneup and stabilization of quantum computing architectures. We focus on the specific application of spatial noise mapping in a "spectator" qubit paradigm, in which these qubits act as sensors to provide information useful for decoherence mitigation on proximal data qubits. In earlier work, the authors introduced and experimentally demonstrated this framework, Noise Mapping for… CONTINUE READING

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