[introductory] An Introduction to Quantum Neural Networks [with Rita Singh, Daniel Justice and Prabh Baweja]
Quantum Computing is a new computing paradigm promising algorithmic accelerations for many real world applications. Machine Learning is one such field where Quantum Computing may, or may not, offer significant improvements. In this series we will lecture on the basics of Quantum Computing before introducing Quantum Machine Learning and then finally ending with a walk through a tutorial showcasing current state of the art Quantum Machine Learning.
- A New Way of Compute (Raj)
- Mapping the New Compute to Quantum Hardware (Raj)
- Programming a Quantum Computer – Basics (Justice)
- Machine Learning Overview (Raj)
- Where Can Quantum Computers Improve Classical ML? (Justice)
- Programming Quantum Machine Learning – Tutorial (Baweja)
- Conclusion (Raj)
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Bhiksha Raj is a Professor in the School of Computer Science at Carnegie Mellon University. His fields of research include speech and audio processing, machine learning and deep learning, privacy and security, and quantum machine learning. He has several hundred of papers on these subjects and also teaches several popular courses on them at CMU and elsewhere. Dr. Raj is a Fellow of the IEEE.
Rita Singh is a Professor in the School of Computer Science at Carnegie Mellon University. Her fields of research include voice and audio forensics, cybersecurity and, lately, quantum computing. Her work on voice forensics and security has been presented in the US House of Representatives, and she has been invited as a featured speaker at various scientific, industry, and policy forums, such as the World Economic Forum and the Congreso Futuro. She is also the author of several hundred of papers, an edited volume, and a landmark book, “Profiling Humans from Their Voice”. She also teaches a variety of courses at CMU including a popular one on quantum machine learning.
Daniel Justice is a Software Engineer at Carnegie Mellon University’s Software Engineering Institute, where he works primarily on the develop of quantum algorithms for computing and machine learning. He is an experienced educator and has taught several popular courses, at CMU and elsewhere, on quantum computing, and is one of the leading experts on the subject.
Prabh Baweja is a machine learning engineer at Apple Inc. with a special interest in quantum computing, particularly as applied to machine learning problems. He holds a Masters’ degree from Carnegie Mellon University, in which he focused on the area. Prabh’s specialty lies in the application of quantum machine learning techniques to neural networks.