Göteborgs universitet
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Mats Granath

Universitetslektor

Institutionen för fysik
Telefon
0723-08 71 60
E-post
mats.granath@physics.gu.se
Besöksadress
Origovägen 6B
41296 Göteborg
Rumsnummer
S3010
Postadress
Kemigården 4
412 96 Göteborg

Om Mats Granath

Research Keywords: quantum error correction; stabilizer codes; quantum optimization algorithms; topological states; strongly correlated systems; high-temperature superconductivity; deep learning; deep reinforcement learning. web

Postdoc opening together with Pedro Trancoso, at CSE, Chalmers: (Deadline Feb 28 2025) Postdoc position in efficient simulation of noisy quantum circuits

Appointments:

Director - Complex Adaptive Systems master (N2CAS/MPCAS) at GU and Chalmers

Member - Committee for AI and Digitalization (SAID) at GU

Group leader at the Wallenberg Centre for Quantum Technology (WACQT)

Scientific coordinator, Guest Researchers Program WACQT

Course engagements:

Open Online Course: AI and Professions

Introduktion till djup maskininlärning och förstärkningsinlärning

Advanced machine learning using neural networks

Introduction to artificial intelligence

Master thesis project proposal on machine learning and quantum computing

Recent Master thesis projects:

Simulating quantum error correction in a small stabilizer code

Deep Reinforcement Learning for Quantum Error Correction

Quantum Models for Word- Sense Disambiguation

Quantum approximate optimization using SWAP gates for mixing

Quantum Error Correction Using Graph Neural Networks

Machine Learning Assisted Quantum Error Correction Using Scalable Neural Network Decoders

Tensor network based decoders for topological stabilizer codes with diverse qubit error rates

Quantum Error Correction using Variational Neural Annealing

Controlling Diffusion: Input-Output Mapping of the Components of a Diffusion Model as a Potential Approach for Enhanced Model Control

Testing the Bell inequality for Higgs boson decay with supervised learning Multi-Variate Analysis classifiers

Decoding the surface code using graph neural networks

Data-driven decoding of the surface code using a neural matching decoder