2018/04/30 by P. Baireuther, M. D. Caio, B. Criger +2 · 4 citations
Physics and Astronomy · #quant-ph #cond-mat.dis-nn
paper · pdf · doi:10.1088/1367-2630/aaf29e
published as New J. Phys 21, 013003 (2019) · 10 pages, 9 figures; V2: updated text and figures
arxiv created 2018/10/18 · arxiv updated 2019/01/15
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 ε\rm L of the encoded logical qubit to values much below the error rate ε\rm phys of the physical qubits --- fitting the expected power law scaling ε\rm L ∝ ε\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.