Machine learning for continuous quantum error correction on superconducting qubits
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2022
- Source ID
- 10.1088/1367-2630/ac66f9
Entities
People
- Birgitta Whaley
- Haoran Liao
- Ho Nam Nguyen
- Ian Convy
- Irfan Siddiqi
- Sahil Patel
- Song Zhang
- William P. Livingston
Organizations
- National Aeronautics and Space Administration
- Office of Science
- United States Army Research Laboratory