Researchers 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.
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.
In this work, the researchers leveraged machine learning in a search for error correction schemes that minimize the device overhead while maintaining good error correcting performance. To this end, they focused on an autonomous approach to quantum error correction, where a cleverly designed, artificial environment replaces the necessity to perform frequent error-detecting measurements.
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