On Quantum Backpropagation and Information Reuse - IPAM at UCLA
Explore the cutting-edge research on quantum backpropagation and information reuse in this 35-minute conference talk presented by Amira Abbas from the University of Amsterdam at IPAM's Mathematical Aspects of Quantum Learning Workshop. Delve into the challenges and recent developments in applying backpropagation techniques to quantum models, a crucial step for scaling quantum machine learning. Examine how shadow tomography challenges the notion that quantum measurement collapse prevents information reuse. Gain insights into the potential for achieving backpropagation scaling in parameterized quantum models, which is essential for their large-scale application. Recorded on October 20, 2023, this talk offers a deep dive into the intersection of quantum computing and machine learning, addressing key questions about the feasibility of quantum backpropagation and its implications for the future of quantum learning algorithms.