Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits

TL;DR

This paper demonstrates how artificial neural networks can enhance the manipulation and readout of molecular spin qubits, improving phase recognition and pulse sequence inference in quantum experiments.

Contribution

It introduces neural network-based methods for analyzing quantum control outputs of molecular spin qubits, enabling more accurate phase detection and pulse sequence recognition.

Findings

Neural networks successfully recognize echo positions in storage/retrieval protocols.

ANNs accurately infer initial pulse sequences from experimental data.

Phase detection of Hahn echoes is improved using neural network analysis.

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.