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Awesome 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).

Reviews

Discrete-variables quantum computing

Variational circuits

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.

Theory

Data-encoding

Classification and regression

Generative models

Reinforcement learning

Kernel methods and SVM

Quantum circuits that are used to extract features from data or to improve kernel-based ML algorithms in general

Auto-encoders

Bayesian approaches

Barren plateau

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.

QRAM-based quantum ML

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.

Classification and regression

Unsupervised learning

Reinforcement learning

Optimization

Dequantization of QRAM-based QML

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):

Applications

Software

Continuous-variables quantum computing

Variational circuits

Kernel methods and SVM

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