Thesis supervisor: Tamás Kozsik
co-supervisor: Zoltán Zimborás
Location of studies (in Hungarian): Eötvös Loránd Tudományegyetem Abbreviation of location of studies: ELTE
Description of the research topic:
The aim of the PhD student will be to explore how ideas and techniques from machine learning (ML) and quantum computing can feed into and benefit the other. The project will involve the development of a software package for ML based gate decomposition and error mitigation, which will enable a scaling up of the currently available methods. In particular, we aim to develop ML based algorithms optimized for the Rydberg platform, and through this acquire knowledge of state-of-the-art variational methods for treating quantum dynamics in strongly-correlated regimes.
The tasks will be the following: (1) Developing neural network based techniques for learning the readout errors, testing it on prototype quantum processors, and applying it for variational quantum algorithms; (2) Reinforcement learning (RL) based optimization of gate decompositions, (3) Tailoring the RL-based gate decomposition for near-term devices by taking into account native gates and the device; (4) Application of the developed gate-decomposition methods for near-term applications, such as variational quantum algorithms.
Required language skills: angol Recommended language skills (in Hungarian): középfok Further requirements: Masters degree in computer science, physics, mathematics, or another related field. Efficient programming skills, strong foundations in linear algebra and machine learning, possibly in probability theory and algorithm theory as well. Adequate English
Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
Login
