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Quantum reservoir computing for machine learning
August 11 @ 11:30 - 12:00
Speaker: Alexander Stasik, Department of Mathematics and Cybernetics, SINTEF
Title: Quantum reservoir computing for machine learning
Abstract: Reservoir computing and extreme learning machines are alternative approaches in machine learning. They use a random non-linear system to transform the input and only learn a linear mapping from the system output to the learning targets. This drastically simplifies the learning process, compared to e.g., neural networks, where the entire non-linear system needs to be learned. In this talk, I will talk about recent work on quantum reservoir computing. Quantum reservoir computing used random quantum circuits as non-linear systems, thus does not require fine-tuning of the circuit. It is also suited for NISQ devices and can be used to understand the power of a quantum enhanced feature space.
Join Zoom Meeting: https://zoom.us/j/92762737127