Hybrid Quantum-Classical Algorithms in QML: A Lightning Review

Title: Hybrid Quantum-Classical Algorithms in QML: A Lightning Review
Speaker: Dr. Bhaskar Roy Bardhan
Abstract: Two of the cornerstone topics in quantum machine learning are quantum data
and hybrid quantum-classical models. In today’s era of noisy intermediate scale quantum computing,
it is important for the quantum processors to work in conjunction with the classical resources to
make the optimal use of both quantum and classical resources.
In this talk, I will provide a brief overview of such hybrid quantum-classical models and explain the core concepts with well-known models of hybrid computation.

3)Title : Variational Quantum Algorithms: A lightening review
Speaker: Dr. Brajesh Gupt
Abstract: While the ambition to build a full-fledged quantum computer is underway,
near term noisy intermediate quantum (NISQ) devices promise early indication of quantum advantage
and provide avenue to use quantum computing to solve problems of practical interest.
In this spirit, variational quantum algorithms are leading the way.
Based on a synergetic classical-quantum hybrid tandem of CPU and QPU,
a lot of progress have been made both on the algorithm and hardware side in the past decade.
I will review some of those algorithms and provide an overview of the current state of affairs.

Bios:
Aroosa is a graduate researcher at Vector Institute for Artificial Intelligence and University of Waterloo.
Her research interests lie at the intersection of machine learning and Physics,
in particular quantum-enhanced machine learning and applications of classical machine learning in Physics.
In the past, she has worked as a Quantum machine learning scientist at Xanadu.
During her education, she specialized in quantum information and conducted research on various systems for quantum
computing such as color centers in diamond, quantum dots, and two-dimensional topological superconductors.

Dr. Bhaskar Roy Bardhan's research interests lie at the interface of quantum computing,
quantum machine learning and quantum communications and he has experience of working in these
fields for more than 12 years. He received his PhD in photonic quantum computing from Louisiana State University, USA.
He then joined MIT as a post-doctoral research associate.
He was a visiting assistant professor of physics at State University of New York at Geneseo
and a research scientist at Xanadu, a quantum computing company based in Toronto, Canada.
He serves as the peer reviewer for various international journals and member of the editorial board of the journal Frontiers
for physics, computer science, and quantum engineering and technology.

Brajesh obtained his PhD in Theoretical and Computational Physics from Louisiana State University in 2014.
Prior to joining TACC, he worked at Xanadu Quantum Technologies, Inc. in Toronto for two years focusing on
developing quantum algorithms and benchmarking Noisy Intermediate-Scale Quantum devices (NISQ)
using supercomputers. Before working at Xanadu, Brajesh was a postdoctoral scholar at the Institute for
Gravitation and the Cosmos at the Pennsylvania State University, University Park. Since joining TACC,
Brajesh's focus has been quantum computing, developing and benchmarking quantum algorithms for near terms applications.

Moderators:

1. Pawel Gora, CEO of Quantum AI Foundation
2. Hennking Dekant, co-founder of Artiste QB Net
3. Kareem El-Safty, co-organizer of Alexandria Quantum Computing Meetup