- 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)
- 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)
- towards quantum machine learning with tensor networks (2018)
- hierarchical quantum classifiers (2018)
- quantum reinforcement learning (2008)
- reinforcement learning using quantum boltzmann machines (2016)
- generalized quantum reinforcement learning with quantum technologies (2017)
- quantum gradient descent and newton’s method for constrained polynomial optimization (2016)
- quantum algorithms and lower bounds for convex optimization (2018)
- 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-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)