Rupak Chatterjee uses the advantages of quantum systems to address classical machine learning issues and explains why the future is quantum.
Traditional computer systems based on classical physics can process only so much data so quickly and with so much complexity. While classical computers are technically capable of analyzing massive data sets, doing so can be prohibitively slow, expensive, inefficient, and limited by existing physical storage capacity. All these issues hamper technological innovation.
Stevens Institute of Technology physics professor Rupak Chatterjee develops quantum-engineered systems to address such problems in big data analytics. His primary research focus is on using quantum systems for machine learning algorithms.
Chatterjee presented an overview of his research, as well as his philosophy of approach, last month as part of the School of Engineering and Science’s Virtual Research Forum.
This weekly presentation series was launched in response to the COVID-19 pandemic to foster engagement and cross-disciplinary collaboration. Currently leader of the Quantum Big Data Analytics Cluster in the Stevens Center for Quantum Science and Engineering (CQSE), Chatterjee joined Stevens full-time in the fall of 2012, having taught financial engineering and risk management as an adjunct professor the previous year. He is the former director of the Financial Engineering Division and an inaugural member of the Hanlon Financial Systems Center at Stevens.
To read the full article, click here.