This is a collection of paper I have found useful in the last years. It is far from complete and you are welcome to suggest new entries here that you think I have missed. I don’t claim for completeness though.
Troubling Trends in Machine Learning Scholarship #opinion-paper
Is a self-autocritic of the ML community on the way they are doing science now. I think this might be relevant as well for the QML practicioner.
Quantum machine learning for data scientits #review
#tutorial
This is a very nice review of some of the most known qml algorithms. I wish I had this when I started studying QML.
Image classification of MNIST dataset using quantum slow feature analysis #algo
This is my first work in quantum machine learning. Here we show 2 new algorithms
The idea is to give evidence that QRAM based algorithms can obtain a speedup w.r.t classical algorithm in QML on real data.
Quantum algorithm implementations for beginners #review
#tutorial
Implementing a distance based classifier with a quantum interference circuit #algo
Quantum machine learning for quantum anomaly detection #algo
Here the authors used previous technique to perform anomaly detection. Basically they project the data on the 1-dimensional subspace of the covariance matrix of the data. In this way anomalies are supposed to lie furhter away from the rest of the dataset.
Quantum machine learning: a classical perspective: #review
#quantum learning theory
Quantum Discriminant Analysis for Dimensionality Reduction and Classification #algo
Here the authors wrote two different algorithm, one for dimensionality reduction and the second for classification, with the same capabilities
Quantum Recommendation Systems #algo
It is where you can learn about QRAM and quantum singular value estimation.
Advances in quantum machine learning #implementations
, #review
It cover things up to 2015, so here you can find descriptions of Neural Networks, Bayesian Networks, HHL, PCA, Quantum Nearest Centroid, Quantum k-Nearest Neighbour, and others.
Quantum algorithms for topological and geometric analysis of data #algo
Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning #tools
, #algorithms
This paper offer two approaches for calculating distances between vectors.
The idea for k-NN is to calculate distances between the test point and the training set in superposition and then use amplitude amplification tecniques to find the minimum, thus getting a quadratic speedup.
Quantum support vector machine for big data classification Patrick #algo
This was one of the first example on how to use HHL-like algorithms in order to get something useful out of them.
Quantum self-testing #algo
The authors discovered how partial application of the swap test are sufficient to transform a quantum state $\sigma$ into $U\sigma U^\dagger$ where $U=e^{-i\rho}$ given the ability to create multiples copies of $\rho$.
This work uses a particular access model of the data (sample complexity), which can be obtained from a QRAM
#algo
#algo