# Resources for Quantum Computing Machine Learning 

                                                
## Introductory Concepts

 
* [Atom structure](https://www.youtube.com/watch?v=g_IaVepNDT4).                               
* [Basic atom structure](https://www.youtube.com/watch?v=fwXQjRBLwsQ) 
* [Basic photon](https://www.youtube.com/watch?v=KKr91v7yLcM).               
* [Electron Spin](https://www.youtube.com/watch?v=J3xLuZNKhlY).            
* [Quantum States](https://www.youtube.com/watch?v=sICXOwOwS4E).
* [Quantum Superposition](https://www.youtube.com/watch?v=hkmoZ8e5Qn0)
* [Quantum Walks](https://www.youtube.com/watch?v=86QsYPxoBow).                                                   
* [Quantum Bits](https://www.youtube.com/watch?v=zNzzGgr2mhk).                                                           
* [Quantum Gates](https://www.youtube.com/watch?v=2Qsh_w2kq9Y).                                                         
* [Quantum Diode](https://www.youtube.com/watch?v=doyK1olswX4).
* [Quantum transistor](https://www.youtube.com/watch?v=ZTxR2n2mvjc).          
* [Quantum processor](https://www.youtube.com/watch?v=CMdHDHEuOUE).                                                  
* [Quantum Registery QRAM](https://arxiv.org/pdf/0807.4994.pdf).
* [Quantum tensors basics](https://www.youtube.com/watch?v=xzG6c96PsLs).
* [Tensors Network for quantum algorithms](https://www.youtube.com/watch?v=bD-CWgbsCeI&list=PLgKuh-lKre10UQnP7gBCFoKgq5KWIA7el).                      
* [Quantum Kmeans on Images](https://pdfs.semanticscholar.org/6d77/54d33958b4a41d57ec99558eb28ae88f9884.pdf).
* [Quantum Fuzzy theory](https://pdfs.semanticscholar.org/6d77/54d33958b4a41d57ec99558eb28ae88f9884.pdf).
* [Quantum Support Vector Machine](https://arxiv.org/pdf/1307.0471.pdf) 
* [Nice Application of QSVM](http://www.scirp.org/journal/PaperInformation.aspx?paperID=72542).                               
* [Quantum Genetic Algorithm](https://arxiv.org/pdf/1202.2026.pdf)  
* [Quantum Hidden Morkov Models](https://arxiv.org/pdf/1503.08760.pdf).                                                       
* [Quantum state classification with Bayesian methods](https://arxiv.org/pdf/1204.1550.pdf).                                   * [Quantum Ant Colony Optimization](http://ac.els-cdn.com/S2212667812001359/1-s2.0-S2212667812001359-main.pdf?_tid=42e0cd66-2f4a-11e7-920f-00000aacb361&acdnat=1493738345_8f536599e404c7588811ddd49c484688). 
* [Quantum Cellular Automata](https://arxiv.org/pdf/0808.0679.pdf).
* [Quantum perceptrons](https://arxiv.org/pdf/quant-ph/0201144.pdf).  


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##  TED Talks related to Quantum Computing

* [A beginner's guide to quantum computing](https://www.ted.com/talks/shohini_ghose_quantum_computing_explained_in_10_minutes#t-322340).
* [What can Schrödinger's cat teach us about quantum mechanics](https://www.ted.com/talks/josh_samani_what_can_schrodinger_s_cat_teach_us_about_quantum_mechanics#t-323151).
* [Schrödinger's cat: A thought experiment in quantum mechanics](https://www.ted.com/talks/chad_orzel_schrodinger_s_cat_a_thought_experiment_in_quantum_mechanics#t-263061).
* [What is the Heisenberg Uncertainty Principle](https://www.ted.com/talks/chad_orzel_what_is_the_heisenberg_uncertainty_principle#t-259830).
* [How quantum physics can make encryption stronger](https://www.ted.com/talks/vikram_sharma_how_quantum_physics_can_make_encryption_stronger#t-701357).
* [How quantum biology might explain life's biggest questions](https://www.ted.com/talks/jim_al_khalili_how_quantum_biology_might_explain_life_s_biggest_questions#t-394875).
* [Making sense of a visible quantum object](https://www.ted.com/talks/aaron_o_connell_making_sense_of_a_visible_quantum_object#t-9460).
* [The future of supercomputers? A quantum chip colder than outer space](https://www.ted.com/talks/jerry_chow_the_future_of_supercomputers_a_quantum_chip_colder_than_outer_space#t-1807).
* [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).


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## 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).

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## 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).