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QML Journal Club
Speaker: Marco Cerezo, Los Alamos National Laboratory
Time: March 04, 10:00 AM
Title: Prospects and Challenges for Training Quantum Neural Networks.
Quantum Neural Networks (QNNs), and Variational Quantum Algorithms (VQAs) have the potential of enabling the first practical applications of quantum machine learning on near-term noisy devices. At their core both QNNs and VQAs train the parameters in a neural network (or a parametrized quantum circuit) to minimize a cost function which encodes the information of a problem. While many different architectures have been proposed, most of them are heuristic methods with unproven scaling that can guarantee that the optimization can be successful. In fact, one of the few rigorous results which analyze the trainability of the parameters is that the cost landscape can exhibit the so-called barren plateau phenomena, where the cost function gradients vanish exponentially with the system size. In this talk we will discuss the importance of performing rigorous scaling analysis on the trainability of QNNs and VQAs, and we argue that such study should be a staple for the community. We then review recent results where we analyze the trainability of different types of QNNs.

Mar 4, 2021 10:00 AM in Singapore

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