Machine learning assisted readout of trapped-ion qubits

TL;DR

This paper demonstrates how machine learning, specifically neural networks, can improve the accuracy of qubit readout in trapped-ion quantum computers by reducing errors and handling crosstalk more effectively.

Contribution

The authors introduce a neural network-based classification method for quantum state readout that enhances error reduction and generalizes across different quantum systems.

Findings

30% reduction in detection error compared to traditional methods

Effective treatment of qubit readout crosstalk

Flexible approach adaptable to various quantum platforms

Abstract

We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the incorporation of any number of features of the data with minimal modifications to the underlying network architecture. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30\% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms.