Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits

Abstract

Machine Learning finds application in the quantum control and readout of qubits. In this work we apply Artificial Neural Networks to assist the manipulation and the readout of a prototypical molecular spin qubit - an Oxovanadium(IV) moiety - in two experiments designed to test the amplitude and the phase recognition, respectively. We first successfully use an artificial network to analyze the output of a Storage/Retrieval protocol with four input pulses to recognize the echo positions and, with further post selection on the results, to infer the initial input pulse sequence. We then apply an Artificial Neural Network to ascertain the phase of the experimentally measured Hahn echo, showing that it is possible to correctly detect its phase and to recognize additional single-pulse phase shifts added during manipulation.

10 pages, 5 figures

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Cited by in corpus (3)

• Quantum Sensing of Magnetic Fields with Molecular Spins
• Hamiltonian-learning quantum magnets with non-local impurity tomography
• Quantum sensing of time-dependent magnetic signals with molecular spins