Quantum Machine Learning Papers

PennyLane: Automatic differentiation of hybrid quantumclassical computations (opens in a new tab) (2018): A Python framework, facilitates quantum and hybrid quantumclassical computation optimization and ML. It offers compatibility with qubit and continuousvariable models, enabling gradient calculations for variational quantum circuits, bridging classical and quantum techniques in optimization and machine learning. (code) (opens in a new tab)

TensorFlow Quantum: A Software Framework for Quantum Machine Learning (opens in a new tab) (2020): An extension of TensorFlow, empowers quantum ML. It supports model construction, training, quantum circuit simulation, and combines classical and quantum computing within a single model, offering a highlevel interface for hybrid quantumclassical models. (code) (opens in a new tab)

A divideandconquer algorithm for quantum state preparation (opens in a new tab) (2020): Suggests a quantum state preparation method employing a divideandconquer algorithm to minimize the number of quantum gates needed. It involves recursively breaking down the target state into smaller substates that require fewer gates for preparation. (code) (opens in a new tab)

Quantum Neuron: an elementary building block for machine learning on quantum computers (opens in a new tab) (2017): Introduces a quantum neuron as a fundamental element for ML. Utilizing a quantum circuit, it conducts nonlinear input data transformations, exhibiting promise in solving classification problems while addressing scalability challenges for larger models.

qmeans: A quantum algorithm for unsupervised machine learning (opens in a new tab) (2019): Suggests "qmeans," a quantum adaptation of the widelyused kmeans clustering algorithm. It employs quantum phase estimation to determine eigenvalues from a distance matrix between data points. The study showcases qmeans' potential in clustering and outlines challenges in its implementation on nearterm quantum hardware.
Key Papers
 Quantum Machine Learning (opens in a new tab) (2014)  Early paper outlining basic goals and approaches for quantum ML.
 Quantum algorithms for supervised and unsupervised machine learning (opens in a new tab) (2013)  Proposes quantum algorithms for supervised classification and unsupervised clustering.
 Quantum Neural Networks (opens in a new tab) (2018)  Describes a quantum version of neural networks with quantum circuits.
 Quantum variational autoencoder (opens in a new tab) (2017)  Implements a variational autoencoder using parametrized quantum circuits.
 Quantum Boltzmann Machine (opens in a new tab) (2018)  Proposes a generative quantum model based on Boltzmann machines.
 Quantum generative adversarial networks (opens in a new tab) (2018)  Formulates a quantum version of GANs using quantum circuits.
 Quantum graph neural networks (opens in a new tab) (2019)  Applies quantum circuits for graph representation learning.
 Quantum algorithms for topological and geometric analysis of data (opens in a new tab) (2016)  Presents quantum algorithms for data topology/geometry.
 Quantum reinforcement learning (opens in a new tab) (2008)  An early approach to quantum reinforcement learning using superposition of states.
 An introduction to quantum machine learning (opens in a new tab) (2014)
 Distributed secure quantum machine learning (opens in a new tab) (2017)
 Alchemical and structural distribution based representation for universal quantum machine learning (opens in a new tab) (2018)