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).

• Quantum Machine Learning: What Quantum Computing Means to Data Mining (2014)
• Quantum Machine Learning (2016)
• A Survey of Quantum Learning Theory (2017)
• Quantum Machine Learning: a classical perspective (2017)
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers (2017)
• Quantum machine learning for data scientists (2018)
• Supervised Learning with Quantum Computers (2018)

• Quantum Statistical Inference (2018)
• Quantum hardness of learning shallow classical circuits (2019)

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 Boltzmann Machine (2016)
• Quantum Perceptron Model (2016)
• Quantum autoencoders via quantum adders with genetic algorithms (2017)
• A Quantum Hopfield Neural Network (2017)
• Automated optimization of large quantum circuits with continuous parameters (2017)
• Quantum Neuron: an elementary building block for machine learning on quantum computers (2017)
• A quantum algorithm to train neural networks using low-depth circuits (2017)
• A generative modeling approach for benchmarking and training shallow quantum circuits (2018)
• Universal quantum perceptron as efficient unitary approximators (2018)
• Quantum Variational Autoencoder (2018)
• Classification with Quantum Neural Networks on Near Term Processors (2018)
• Barren plateaus in quantum neural network training landscapes (2018)
• Quantum generative adversarial learning (2018)
• Quantum generative adversarial networks (2018)
• Circuit-centric quantum classifiers (2018)
• Universal discriminative quantum neural networks (2018)
• A Universal Training Algorithm for Quantum Deep Learning (2018)
• Bayesian Deep Learning on a Quantum Computer (2018)
• Quantum generative adversarial learning in a superconducting quantum circuit (2018)
• The Expressive Power of Parameterized Quantum Circuits (2018)
• Quantum Convolutional Neural Networks (2018)
• An Artificial Neuron Implemented on an Actual Quantum Processor (2018)
• Graph Cut Segmentation Methods Revisited with a Quantum Algorithm (2018)
• Efficient Learning for Deep Quantum Neural Networks (2019)
• Parameterized quantum circuits as machine learning models (2019)
• Machine Learning Phase Transitions with a Quantum Processor (2019)
• Hybrid Quantum-Classical Convolutional Neural Networks (2019)
• Building quantum neural networks based on a swap test (2019)
• Data re-uploading for a universal quantum classifier (2020)

• q-means: A quantum algorithm for unsupervised machine learning (2018)
• Quantum Algorithms for Deep Convolutional Neural Networks (2019)

• Towards Quantum Machine Learning with Tensor Networks (2018)
• Hierarchical quantum classifiers (2018)

• Quantum reinforcement learning (2008)
• Reinforcement Learning Using Quantum Boltzmann Machines (2016)
• Generalized Quantum Reinforcement Learning with Quantum Technologies (2017)

• Quantum gradient descent and Newton’s method for constrained polynomial optimization (2016)
• Quantum algorithms and lower bounds for convex optimization (2018)

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

• Supervised learning with quantum enhanced feature spaces (2018)
• Quantum Sparse Support Vector Machines (2019)
• Sublinear quantum algorithms for training linear and kernel-based classifiers (2019)

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):

• A quantum-inspired classical algorithm for recommendation systems (2018)
• Quantum-inspired classical algorithms for principal component analysis and supervised clustering (2018)
• Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension (2018)
• Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning (2019)

• Continuous-variable quantum neural networks (2018)
• Machine learning method for state preparation and gate synthesis on photonic quantum computers (2018)
• Near-deterministic production of universal quantum photonic gates enhanced by machine learning (2018)

• Quantum machine learning in feature Hilbert spaces (2018)

• Awesome Quantum Machine Learning
• Awesome Quantum Computing
• A Guide to QC and QI