Abstract
Quantum variational circuit compiling is a technique in quantum computing where quantum circuits are transformed into more efficient forms, known as parameterized ansatzes, through a combination of quantum and classical computational methods.
This talk explains a new approach within this domain, called the recursive variational quantum compiling algorithm (RVQC). Unlike standard variational quantum compiling (VQC) algorithms, which require running the entire target circuit for compilation, RVQC breaks down the target circuit into segments. Each segment is individually and recursively compressed into the parameterized ansatz. This method's advantage lies in its ability to compress circuits without being dependent on the entire target circuit's depth.
About the qGAP meeting series
Welcome to our seminar series on Quantum Technologies, many-body physics, machine learning and quantum machine learning (or just qGAP).
The series of seminars/lectures are meant to cover broadly activities at UiO related to quantum technologies, developments in many-body theories related or unrelated to quantum technologies, machine learning applied to quantum mechanical systems and quantum machine learning.
We aim at having regular seminars, discussions of recent articles, presentations by master and PhD students and more. We aim also at discussing experimental work and theoretical work, with obviously a strong link to condensed matter physics, materials science and semi-conductor physics, nanotechnologies and quantum technologies.
Educational topics can also be included.
February 12: Håkon Kristiansen (Hyllerås Center@Chemistry, UiO) - Title: Time-dependent many-body theories
February 19: Morten Hjorth-Jensen (Physics, UiO) - Title: Parametric Matrix Models and Machine Learning
February 26: Joakim Bergli (Physics, UiO) - Title: Introduction to quantum error correction
March 4: Joakim Bergli (Physics, UiO) - Title: Synchronization in two-level quantum systems
March 11: TBA
March 18: TBA
March 25 and April 1, Easter break
April 8: TBA
April 15: TBA
April 22: TBA
April 29: TBA
May 6: TBA
May 13: TBA, last session
May 14-16, workshop on utilizing numerical analysis and physics knowledge to obtain accurate, explainable and robust machine learning models, Sintef and UiO.
Feel free to suggest speakers, yourself included!