Neural network decoder for topological color codes with circuit level noise
| Autor: | Baireuther, P., Caio, M. D., Criger, B., Beenakker, C. W. J., O'Brien, T. E. |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | New J. Phys 21, 013003 (2019) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1088/1367-2630/aaf29e |
| Popis: | 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 $\epsilon_{\rm L}$ of the encoded logical qubit to values much below the error rate $\epsilon_{\rm phys}$ of the physical qubits --- fitting the expected power law scaling $\epsilon_{\rm L} \propto \epsilon_{\rm phys}^{(d+1)/2}$, 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. Comment: 10 pages, 9 figures; V2: updated text and figures |
| Databáze: | arXiv |
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