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Neural network decoder for topological color codes with circuit level noise
| Content Provider | Scilit |
|---|---|
| Author | O’Brien, T. E. Baireuther, P. Caio, M. D. Criger, B. Beenakker, C. W. J. |
| Copyright Year | 2019 |
| Description | Journal: New Journal of Physics 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. |
| Related Links | http://iopscience.iop.org/article/10.1088/1367-2630/aaf29e/pdf |
| e-ISSN | 13672630 |
| DOI | 10.1088/1367-2630/aaf29e |
| Journal | New Journal of Physics |
| Issue Number | 1 |
| Volume Number | 21 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: New Journal of Physics Ej.iop.org/icons/entities/epsi.gif |
| Content Type | Text |
| Resource Type | Article |
