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# Resources for Quantum Computing Machine Learning # Resources for Quantum Computing
## Introductory Concepts ## Introductory Concepts
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* [John Preskill on Quantum Computing](https://blog.ycombinator.com/john-preskill-on-quantum-computing/). * [John Preskill on Quantum Computing](https://blog.ycombinator.com/john-preskill-on-quantum-computing/).
----
## 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).
----
## 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).