Quantum Machine Learning Panel Discussion

Topic: Quantum Machine Learning Panel Discussion
Speakers: Dr. Yianni Gamvros ( QC Ware Corp)
Mr. Michael Brett ( Rigetti)
Mr. Vinay Phadnis ( Udemy instructor)
Moderator: Dr. Terrill Frantz ( Harrisburg University of Science and Technology)
Date time : May 13th 19:00-21:00 America Eastern Daylight Time
Zoom link : provided by Dr. Gamvros, will announce to participants 30 minutes before the meetup.

Abstract by Dr. Gamvros ( QC Ware Corp):
Most people think that practical QML is not attainable in the next decade because of QRAM requirements and hardware requirements for linear algebra. At the same time, proposals for using variational architectures to mimic neural nets have yet to deliver any proof of performance. Yianni will talk about QC Ware's unique approach to QML and provide some insights into the work we are pursuing and why it is important for practical QML.

Abstract by Mr. Michael Brett ( Rigetti):
Tough problems need a mix of computing solutions to meet the demands of our increasingly complex and growing computational needs. Financial, energy, and aerospace companies are already looking to quantum computing as a potential new tool to help address problems that would be practically intractable for classical computers to solve alone. Michael will discuss how Rigetti’s hybrid quantum-classical computing systems are being used to explore new use cases, including quantum machine learning. By adding quantum computers to an already heterogeneous compute environment, we may be able to improve time to solution and other performance metrics for some machine learning algorithms.

Abstract by Mr. Vinay Phadnis ( Udemy instructor) :
Quantum Machine Learning has always been considered as something which has a steep learning curve. In reality, the fundamental ideas of Quantum Machine learning are very simple to understand. This talk will cover some of the basic ideologies and concepts of Quantum Machine Learning paradigm along with many real life applications. Through these applications, I will try to first explain the need for Quantum Machine Learning and then build on some of the core concepts of Quantum Machine Learning like Directional Computing, Hybrid Computing etc