## quantum machine learning

<br>

### general reviews

<br>

* **[opportunities and challenges for quantum-assisted ml in nisq](https://iopscience.iop.org/article/10.1088/2058-9565/aab859) (2018)** 
* **[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)**

<br>

----

### discrete-variables quantum computing

<br>

#### theory

<br>

* **[quantum statistical inference](https://arxiv.org/abs/1812.04877) (2018)**
* **[quantum hardness of learning shallow classical circuits](https://arxiv.org/abs/1903.02840) (2019)**

<br>

#### variational circuits

<br>

* **[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)**

<br>

#### tensor networks

<br>

* **[towards quantum machine learning with tensor networks](https://arxiv.org/abs/1803.11537) (2018)**
* **[hierarchical quantum classifiers](https://arxiv.org/abs/1804.03680v1) (2018)**

<br>

#### reinforcement learning

<br>

* **[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)**

<br>

#### optimization

<br>

* **[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)**

<br>

#### kernel methods and svm

<br>

* **[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)**

<br>

---

### continuous-variables quantum computing

<br>

#### 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)**

<br>

#### kernel methods and svm

* **[quantum machine learning in feature hilbert spaces](https://arxiv.org/1803.07128) (2018)**