Master thesis presentation Axel Prebensen, MPCAS

Abstract: Ever since the theoretical possibility of quantum computers was discovered a struggle to create the practical possibility has been ongoing. The struggle in creating a quantum computer lies mostly in the extremely sensitive and error prone quantum bits (qubits) making up the basis of the quantum computation. One of the most promising approaches to solve the issues of the sensitive qubits is quantum error correction, which aims to correct for the errors rather than eliminating them altogether. To error correct one needs to create a logical qubit consisting of multiple physical qubits, where errors on a physical qubit can be corrected to preserve the logical qubit from errors. The most common way to achieve this is by implementing a two-dimensional surface code, where a grid of qubits represent a single logical qubit. In order to decode this surface code and correct for errors we need to measure error syndromes on the surface code and decide which qubit error is most likely to cause that syndrome. In this thesis a new and alternative solution to decode called Variational Neural Annealing (VNA) is investigated, an optimization technique that uses a Recurrent Neural Network (RNN). Its viability as a decoder is determined by its accuracy and runtime. Both a two-dimensional and a one-dimensional RNN structure is studied on a surface code with code distances 3 and 5. The results of the study show that its viability as a decoder is limited in all tested configurations, and lower accuracy than comparable models is obtained. This limits the results to be presented for small surface codes with only X errors present, with further exploration of larger surface codes and error types being unnecessary for the current scope of the study. Alternative methods and approaches are presented that show more promising results, while still performing under par. This leads to a presentation of additional alternative approaches for further study on algorithms utilizing RNNs for quantum error correction.

Password: 437882

Supervisor:Mats Granath
Examiner: Mats Granath
Opponent: Anton Lindén