quantum-computing-toolkit/README.md

Resources for Quantum Computing Machine Learning

Introductory Concepts

• Atom structure.
• Basic atom structure
• Basic photon.
• Electron Spin.
• Quantum States.
• Quantum Superposition
• Quantum Walks.
• Quantum Bits.
• Quantum Gates.
• Quantum Diode.
• Quantum transistor.
• Quantum processor.
• Quantum Registery QRAM.
• Quantum tensors basics.
• Tensors Network for quantum algorithms.
• Quantum Kmeans on Images.
• Quantum Fuzzy theory.
• Quantum Support Vector Machine
• Nice Application of QSVM.
• Quantum Genetic Algorithm
• Quantum Hidden Morkov Models.
• Quantum state classification with Bayesian methods. * Quantum Ant Colony Optimization.
• Quantum Cellular Automata.
• Quantum perceptrons.

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TED Talks related to Quantum Computing

• A beginner's guide to quantum computing.
• What can Schrödinger's cat teach us about quantum mechanics.
• Schrödinger's cat: A thought experiment in quantum mechanics.
• What is the Heisenberg Uncertainty Principle.
• How quantum physics can make encryption stronger.
• How quantum biology might explain life's biggest questions.
• Making sense of a visible quantum object.
• The future of supercomputers? A quantum chip colder than outer space.
• What's the smallest thing in the universe?.

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Reviews

• 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).

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Discrete-variables quantum computing

Theory

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

Variational circuits

• 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).

Tensor Networks

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

Reinforcement learning

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

Optimization

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

Kernel methods and SVM

• 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).

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Continuous-variables quantum computing

Variational circuits

• 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).

Kernel methods and SVM

• Quantum machine learning in feature Hilbert spaces (2018).