Daniel Silver

Title: Quantum Machine Learning on Current Quantum Computers

Abstract: Quantum computing has the potential to speed up many tasks in machine learning. Properties of quantum computing such as superposition, entanglement, and reversibility, set it apart from classical computing. However, there are many challenges to the adaptation of this technology in the current noisy intermediate-scale quantum (NISQ) era of quantum computing. This era is characterized by high levels of quantum hardware noise and relatively small-sized quantum computers. Nonetheless, the current technology can still be used in innovative ways to execute machine-learning tasks. This talk presents quantum machine learning solutions for solving problems in classification, similarity detection, and image generation. Specifically, this talk will focus on recent research on how quantum computers can be used to execute machine learning tasks today. 

Bio: Daniel Silver is a Ph.D. Candidate at Northeastern University with a focus on integrating the principles of machine learning with the emerging field of quantum computing. Before starting his Ph.D. journey, Daniel also received his B.Sc. in Computer Engineering and M.Sc. in Machine Learning from Northeastern University. He has published his research at top-tier conference venues such as AAAI, ICCV, SC, ISCA, and DATE.

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