Curated list of awesome papers and resources in quantum machine learning
A list of awesome papers and cool resources in the field of quantum machine learning (machine learning algorithms running on quantum devices). It does not include the use of classical ML algorithms for quantum purpose. Don't hesitate to suggest resources I could have forgotten (I take pull requests).
Variational circuits are quantum circuits with variable parameters that can be optimized to compute a given function. They can for instance be used to classify or predict properties of quantum and classical data, sample over complicated probability distributions (as generative models), or solve optimization and simulation problems.
Quantum circuits that are used to extract features from data or to improve kernel-based ML algorithms in general
The barren plateau phenomenon occurs when the gradient of a variational circuit vanishes exponentially with the system size for a random initialization. When an architecture exhibits this phenomenon, it hinders its potential for being trainable at large-scale.
The following QML algorithms assume the existence of an efficient way to load classical data on a quantum device, such as a quantum RAM (QRAM). While this can be a complicated requirement in the short-term, QRAM-based algorithms often come with a rigourously-proven speed-up.
Kingdom of Ewin Tang. Papers showing that a given quantum machine learning algorithm does not lead to any improved performance compared to a classical equivalent (either asymptotically or including constant factors):