Update README.md

README.md

@@ -183,3 +183,91 @@ https://www.ted.com/talks/jerry_chow_the_future_of_supercomputers_a_quantum_chip
 
 9. What's the smallest thing in the universe?
 https://www.ted.com/talks/jonathan_butterworth_what_s_the_smallest_thing_in_the_universe#t-305865
+
+
+----
+
+
+## Reviews
+
+* [Quantum Machine Learning: What Quantum Computing Means to Data Mining](https://www.researchgate.net/publication/264825604_Quantum_Machine_Learning_What_Quantum_Computing_Means_to_Data_Mining) (2014)
+* [Quantum Machine Learning](https://arxiv.org/abs/1611.09347v2) (2016)
+* [A Survey of Quantum Learning Theory](https://arxiv.org/abs/1701.06806) (2017)
+* [Quantum Machine Learning: a classical perspective](https://arxiv.org/abs/1707.08561) (2017)
+* [Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers](https://arxiv.org/abs/1708.09757) (2017)
+* [Quantum machine learning for data scientists](https://arxiv.org/abs/1804.10068) (2018)
+* [Supervised Learning with Quantum Computers](https://www.springer.com/gp/book/9783319964232) (2018)
+
+----
+
+## Discrete-variables quantum computing
+
+### Theory
+
+* [Quantum Statistical Inference](https://arxiv.org/abs/1812.04877) (2018)
+* [Quantum hardness of learning shallow classical circuits](https://arxiv.org/abs/1903.02840) (2019)
+
+### Variational circuits
+
+* [Quantum Boltzmann Machine](https://arxiv.org/abs/1601.02036) (2016)
+* [Quantum Perceptron Model](https://arxiv.org/abs/1602.04799) (2016)
+* [Quantum autoencoders via quantum adders with genetic algorithms](https://arxiv.org/abs/1709.07409) (2017)
+* [A Quantum Hopfield Neural Network](https://arxiv.org/abs/1710.03599) (2017)
+* [Automated optimization of large quantum circuits with continuous parameters](https://arxiv.org/abs/1710.07345) (2017)
+* [Quantum Neuron: an elementary building block for machine learning on quantum computers](https://arxiv.org/abs/1711.11240) (2017)
+* [A quantum algorithm to train neural networks using low-depth circuits](https://arxiv.org/abs/1712.05304) (2017)
+* [A generative modeling approach for benchmarking and training shallow quantum circuits](https://arxiv.org/abs/1801.07686) (2018)
+* [Universal quantum perceptron as efficient unitary approximators](https://arxiv.org/abs/1801.00934) (2018)
+* [Quantum Variational Autoencoder](https://arxiv.org/abs/1802.05779) (2018)
+* [Classification with Quantum Neural Networks on Near Term Processors](https://arxiv.org/abs/1802.06002) (2018)
+* [Barren plateaus in quantum neural network training landscapes](https://arxiv.org/abs/1803.11173) (2018)
+* [Quantum generative adversarial learning](https://arxiv.org/abs/1804.09139) (2018)
+* [Quantum generative adversarial networks](https://arxiv.org/abs/1804.08641) (2018)
+* [Circuit-centric quantum classifiers](https://arxiv.org/abs/1804.00633) (2018)
+* [Universal discriminative quantum neural networks](https://arxiv.org/abs/1805.08654) (2018)
+* [A Universal Training Algorithm for Quantum Deep Learning](https://arxiv.org/abs/1806.09729) (2018)
+* [Bayesian Deep Learning on a Quantum Computer](https://arxiv.org/abs/1806.11463) (2018)
+* [Quantum generative adversarial learning in a superconducting quantum circuit](https://arxiv.org/abs/1808.02893) (2018)
+* [The Expressive Power of Parameterized Quantum Circuits](https://arxiv.org/abs/1810.11922) (2018)
+* [Quantum Convolutional Neural Networks](https://arxiv.org/abs/1810.03787) (2018)
+* [An Artificial Neuron Implemented on an Actual Quantum Processor](https://arxiv.org/pdf/1811.02266.pdf) (2018)
+* [Graph Cut Segmentation Methods Revisited with a Quantum Algorithm](https://arxiv.org/abs/1812.03050) (2018)
+* [Efficient Learning for Deep Quantum Neural Networks](https://arxiv.org/abs/1902.10445) (2019)
+* [Parameterized quantum circuits as machine learning models](https://arxiv.org/abs/1906.07682) (2019)
+* [Machine Learning Phase Transitions with a Quantum Processor](https://arxiv.org/abs/1906.10155) (2019)
+
+### Tensor Networks
+
+* [Towards Quantum Machine Learning with Tensor Networks](https://arxiv.org/abs/1803.11537) (2018)
+* [Hierarchical quantum classifiers](https://arxiv.org/abs/1804.03680v1) (2018)
+
+### Reinforcement learning
+
+* [Quantum reinforcement learning](https://arxiv.org/abs/0810.3828) (2008)
+* [Reinforcement Learning Using Quantum Boltzmann Machines](https://arxiv.org/abs/1612.05695) (2016)
+* [Generalized Quantum Reinforcement Learning with Quantum Technologies](https://arxiv.org/abs/1709.07848) (2017)
+
+### Optimization
+
+* [Quantum gradient descent and Newton’s method for constrained polynomial optimization](https://arxiv.org/abs/1612.01789) (2016)
+* [Quantum algorithms and lower bounds for convex optimization](https://arxiv.org/pdf/1809.01731.pdf) (2018)
+
+### Kernel methods and SVM
+
+* [Supervised learning with quantum enhanced feature spaces](https://arxiv.org/abs/1804.11326) (2018)
+* [Quantum Sparse Support Vector Machines](https://arxiv.org/abs/1902.01879) (2019)
+* [Sublinear quantum algorithms for training linear and kernel-based classifiers](https://arxiv.org/pdf/1904.02276.pdf) (2019)
+
+---
+
+## Continuous-variables quantum computing
+
+### Variational circuits
+
+* [Continuous-variable quantum neural networks](https://arxiv.org/abs/1806.06871) (2018)
+* [Machine learning method for state preparation and gate synthesis on photonic quantum computers](https://arxiv.org/abs/1807.10781) (2018)
+* [Near-deterministic production of universal quantum photonic gates enhanced by machine learning](https://arxiv.org/abs/1809.04680) (2018)
+
+### Kernel methods and SVM
+
+* [Quantum machine learning in feature Hilbert spaces](https://arxiv.org/1803.07128) (2018)