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👾 notes and resources on quantum computing (e.g., quantum information, post-quantum cryptography, implications on post-quantum and blockchains)
blockchain-technologydocumentspost-quantum-cryptographyquantumquantum-algorithmsquantum-computingquantum-informationquantum-machine-learningquantum-mechanicsresources
| .gitignore | ||
| intro_group_theory_mia_von_steinkirch.pdf | ||
| intro_quantum_fcomputing_mia_von_steinkirch.pdf.pdf | ||
| intro_quantum_field_theory_mia_von_steinkirch.pdf | ||
| LICENSE | ||
| 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.(https://www.youtube.com/watch?v=xzG6c96PsLs).
- 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.
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?.
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).
Discrete-variables quantum computing
Theory
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).
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).