Datadriven kvantfelskorrigering genom avkodning av repetitionskoden med grafneurala nätverk
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Typ
Examensarbete för kandidatexamen
Bachelor Thesis
Bachelor Thesis
Program
Modellbyggare
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Sammanfattning
Quantum error correction is one challenge faced when implementing quantum computer
systems. Repetition code is a simpler correcting decoder that detects bit- or phase-flip errors.
To fully benefit from the repetition code, an appropriate decoder is required. Therefore,
this report aims to compare two different decoding methods, a classical algorithm known as
Minimum-Weight Perfect Matching, MWPM and a machine learning-based method using
Graph Neural Networks, GNN. A modular phase-flip detecting repetition code is constructed
and executed on an IBM Quantum processor. The measured syndromes are used as training
data for GNN, which is trained on Chalmers high-performance computing cluster Alvis.
The report also investigates how decoding time scales with increasing code area. Results
showed that the GNN decoder achieved marginally higher logical accuracy αL compared
to MWPM, particularly near the code area where optimization of the hyperparameters
was performed. Furthermore, GNN achieved slightly higher logical accuracy compared
to MWPM for code distance and time repetitions d,dt ≤ 9. This is because the decoder
was tested on syndromes generated during the same quantum computer execution as the
training data. However, GNN also showed consistently higher logical error probability pL
and was less effective than MWPM when tested on different syndromes, which may hinder
practical application in environments with varying error distributions.
Beskrivning
Ämne/nyckelord
Kvantfelskorrigering, repetitionskod, kvantfasfel, grafneurala nätverk GNN, syndrommätning, Qiskit, IBM Quantum, IBM Marrakesh.