Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

TL;DR

This paper introduces a machine learning approach using recurrent neural networks for continuous quantum error correction in superconducting qubits, demonstrating improved performance over traditional methods in realistic noisy conditions.

Contribution

It presents a novel ML-based algorithm tailored for continuous quantum error correction that accounts for real-world measurement imperfections in superconducting qubits.

Findings

ML protocol outperforms double threshold scheme

Achieves fidelity comparable to Bayesian classifier

Effective in realistic noisy measurement scenarios

Abstract

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.