Kavli Affiliate: K. Birgitta Whaley
| First 5 Authors: Ian Convy, Haoran Liao, Song Zhang, Sahil Patel, William P. Livingston
| Summary:
We propose a machine learning algorithm for continuous quantum error
correction that is based on the use of a recurrent neural network to identity
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.
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