Lecture 7: Hardwares for Quantum Computing#
Overview
This is a placeholder for the Guest Lecture. It will be populated in due time with necessary prerequisits, and supplementary content on Quantum Hardwares.
About the Guest#
The Speaker: David Redmond
David Redmond received the B.E.E. degree from University College Cork, in 1988, and the M.Sc. degree in mathematics from University College Dublin, in 2017. He was involved in the semiconductor industry for more than 30 years developing high volume consumer and industrial products from handset 2/3/4G RF transceivers, to SerDes and Gigabit Ethernet products for such companies, such as AT&T Lucent, S3, Motorola, Freescale, and Analog Devices, operating at various levels of corporate research and development and product development. Since 2020, he has been with Equal1 Labs for VP AI & silicon. His research interests include machine learning and statistics.
From IEEE Xplore
Affiliation and activities
David has been with the Equal1 since 2020, working towards building silicon based quantum computing device. His expertise include:
ML hardware for QEC, mixed-signal circuits for qubit control, ML mixed-signal CMOS accelerators Quantum Algorithms, Quantum Error Correction, Hybrid ML quantum algorithms. Deep Learning Networks, NISQ Quantum Computers, Time series, Anomaly detection, Computer vision. IIoT sensors to cloud to insights, vibrational analysis and predictive maintenance, business innovation, Industrial and Consumer ethernet. Full Silicon Product development for consumer and industrial products
Summary of lecture#
Summary
This lecture will be illustrating how a quantum computing device is built. He will go through the underlying technology to define qubits, methods to control them, and challenges there in.
References#
References
Recommended Reading
Advance Reading