Quantum Machine Learning Papers

• PennyLane: Automatic differentiation of hybrid quantum-classical computations (opens in a new tab) (2018): A Python framework, facilitates quantum and hybrid quantum-classical computation optimization and ML. It offers compatibility with qubit and continuous-variable 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 high-level interface for hybrid quantum-classical models. (code) (opens in a new tab)

• A divide-and-conquer algorithm for quantum state preparation (opens in a new tab) (2020): Suggests a quantum state preparation method employing a divide-and-conquer algorithm to minimize the number of quantum gates needed. It involves recursively breaking down the target state into smaller sub-states 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.

• q-means: A quantum algorithm for unsupervised machine learning (opens in a new tab) (2019): Suggests "q-means," a quantum adaptation of the widely-used k-means clustering algorithm. It employs quantum phase estimation to determine eigenvalues from a distance matrix between data points. The study showcases q-means' potential in clustering and outlines challenges in its implementation on near-term 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)