AlphaQubit Overcome Major Quantum Computing Challenges
AlphaQubit, an AI-based decoder that detects quantum computing defects with cutting-edge precision, in a work that was published today in Nature
It is a neural-network decoder that uses Google’s Transformers deep learning architecture serves as the foundation for many of the big language models
It created hundreds of millions of samples in a range of settings and error levels using a quantum simulator to teach AlphaQubit the basic decoding issue
Additionally, AlphaQubit produces 30% less mistakes than correlated matching, a scalable and accurate decoder
AlphaQubit using data from simulated quantum systems of up to 241 qubits, which was more than what was accessible on the Sycamore platform
A significant advancement in the use of machine learning to quantum error correction is represented by AlphaQubit
AlphaQubit is excellent at correctly detecting mistakes, but it is still too slow to instantly fix problems in a superconducting processor