Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these devices practical—using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.
The main challenge towards putting quantum computers into practice stems from the extremely fragile nature of quantum superpositions. Indeed, tiny perturbations induced, for instance, by the ubiquitous presence of an environment give rise to errors that rapidly destroy quantum superpositions and, as a consequence, quantum computers lose their edge.have been developed.
They also looked at"bosonic qubit encodings", which are, for instance, available and utilized in some of the currently most promising and widespreadFinding high-performing candidates in the vast search space of bosonic qubit encodings represents a complex optimization task, which the researchers address with, an advanced machine learning method, where an agent explores a possibly abstract environment to learn and optimize its action policy.
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