Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

• URL: http://arxiv.org/abs/2110.10378v2
• Date: Tue, 5 Jul 2022 18:55:56 GMT
• Title: Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits
• Authors: Ian Convy, Haoran Liao, Song Zhang, Sahil Patel, William P. Livingston, Ho Nam Nguyen, Irfan Siddiqi and K. Birgitta Whaley
• Abstract summary: Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction. We propose a machine learning algorithm for continuous quantum error correction based on the use of a recurrent neural network.
• Score: 1.8249709209063887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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