Neural network decoder for topological color codes with circuit level noise

Neural network decoder for topological color codes with circuit level noise

Baireuther, P.S. (Universiteit Leiden)
Caio, M. D. (Universiteit Leiden)
Criger, D.B. (TU Delft QuTech Advanced Research Centre; TU Delft QCD/Terhal Group)
Beenakker, C. W.J. (Universiteit Leiden)
O'Brien, T.E. (Universiteit Leiden)

2019-01-08

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 _L of the encoded logical qubit to values much below the error rate _phys of the physical qubits - fitting the expected power law scaling , with d the code distance. The neural network incorporates the information from 'flag qubits' to avoid reduction in the effective code distance caused by the circuit. As a test, we apply the neural network decoder to a density-matrix based simulation of a superconducting quantum computer, demonstrating that the logical qubit has a longer life-time than the constituting physical qubits with near-term experimental parameters.

machine learning
quantum error correction
recurrent neural network
topological color codes

http://resolver.tudelft.nl/uuid:26f48756-d832-46d7-896c-5965e0f491b2

https://doi.org/10.1088/1367-2630/aaf29e

1367-2630

New Journal of Physics, 21 (1)

Institutional Repository

journal article

© 2019 P.S. Baireuther, M. D. Caio, D.B. Criger, C. W.J. Beenakker, T.E. O'Brien