Prediction and real-time compensation of qubit decoherence via machine learning.

ABSTRACT: The wide-ranging adoption of quantum technologies requires practical, high-performance advances in our ability to maintain quantum coherence while facing the challenge of state collapse under measurement. Here we use techniques from control theory and machine learning to predict the future evolution of a qubit's state; we deploy this information to suppress stochastic, semiclassical decoherence, even when access to measurements is limited. First, we implement a time-division multiplexed approach, interleaving measurement periods with periods of unsupervised but stabilised operation during which qubits are available, for example, in quantum information experiments. Second, we employ predictive feedback during sequential but time delayed measurements to reduce the Dick effect as encountered

SUBMITTER: Mavadia S 

PROVIDER: S-EPMC5241809 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature