Quantum Machine Learning Beyond Parametrized

"Quantum machine learning beyond parametrized quantum circuits: Quantum Gaussian Processes"

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Quantum machine learning beyond kernel methods

Title:

Maria Schuld - How to rethink quantum machine learning - IPAM at UCLA

Maria Schuld of Xanadu

2023-09-18 - NITheCS Colloquium: 'Quantum machine learning with parameterized quantum circuits' ...

2023-09-18 - NITheCS Colloquium:

Ewin Tang: Building a classical framework to analyze quantum machine learning speedups

An invited talk by Ewin Tang at the 14th Conference on the Theory of

Quantum machine learning beyond quantum kernels and with smaller circuits

Quantum machine learning

Recurrent Quantum Neural Networks, presented by Johannes Bausch, Cambridge University

Recurrent neural networks are the foundation of many sequence-to-sequence models in

David Wierichs: Backpropagation scaling in parametrized quantum circuits

... backpropagation scaling in

Sofiene Jerbi: Quantum machine learning beyond kernel methods

This is a talk on

J. Biamonte, Introduction to modern quantum algorithms: Optimisation, simulation, machine learning

References Jacob Biamonte et al.,

Towards Provably Efficient Quantum Algorithms for Large scale Machine learning Models

Title: Towards Provably Efficient

Quantum Machine Learning

A special lecture entitled "

Marco Cerezo: Towards Geometric Quantum Machine Learning | QHack 2023

Marco Cerezo, Staff Scientist at Los Alamos National Laboratory, speaks at QHack 2023.