Machine learning for continuous quantum error correction on superconducting qubits

• Published in: New journal of physics Vol. 24; no. 6; pp. 63019 - 63042
• Main Authors: Convy, Ian, Liao, Haoran, Zhang, Song, Patel, Sahil, Livingston, William P, Nguyen, Ho Nam, Siddiqi, Irfan, Whaley, K Birgitta
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2022
• Subjects: 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
• ISSN: 1367-2630; 1367-2630
• DOI: 10.1088/1367-2630/ac66f9

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.