TL;DR
This paper demonstrates that a recurrent neural network decoder can significantly improve error correction in topological color codes under circuit-level noise, extending logical qubit lifetime beyond physical qubits in simulated superconducting quantum computers.
Contribution
It introduces a neural network-based decoder tailored for topological color codes that effectively incorporates flag qubit information to mitigate circuit-level noise effects.
Findings
Neural network decoder reduces logical error rate below physical qubit error rate.
The decoder achieves power law scaling of error rates with code distance.
Simulations show extended logical qubit lifetime in superconducting quantum computer models.
Abstract
A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to tailor such an algorithm to the specific error processes of an experiment --- without the need for a priori knowledge of the error model. Here, we apply this technique to topological color codes. We demonstrate that a recurrent neural network with long short-term memory cells can be trained to reduce the error rate of the encoded logical qubit to values much below the error rate of the physical qubits --- fitting the expected power law scaling , with the code distance. The neural network incorporates the information from "flag qubits" to avoid…
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