quantum-computing-toolkit/quantum_machine_learning

quantum machine learning

general reviews

• opportunities and challenges for quantum-assisted ml in nisq (2018)
• 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)