Journal Article

Machine learning for continuous quantum error correction on superconducting qubits

Livingston, William P, Whaley, K Birgitta, Patel, Sahil, Nguyen, Ho Nam, Liao, Haoran, Convy, Ian, Zhang, Song, Siddiqi, Irfan

2022

Overview

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.

• Publisher: IOP Publishing
• Subject: Algorithms / Bayesian analysis / Bayesian inference / Classifiers / Error correction / Error correction & detection / Machine learning / Neural networks / Noise measurement / Physics / quantum annealing / quantum computing / quantum error correction / Qubits (quantum computing) / Recurrent neural networks / superconducting qubits / Superconductivity / White noise