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Quantum Machine Learning

7 papers with code · Methodology

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

12 Nov 2018XanaduAI/pennylane

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.

QUANTUM MACHINE LEARNING

Continuous-variable quantum neural networks

18 Jun 2018XanaduAI/quantum-neural-networks

The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.

FRAUD DETECTION QUANTUM MACHINE LEARNING

Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning

8 Jul 2019glivan/tensor_networks_for_probabilistic_modeling

Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.

QUANTUM MACHINE LEARNING TENSOR NETWORKS

Quantum Wasserstein Generative Adversarial Networks

NeurIPS 2019 yiminghwang/qWGAN

The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines.

QUANTUM MACHINE LEARNING

A quantum-inspired classical algorithm for recommendation systems

10 Jul 2018nkmjm/qiML

We give a classical analogue to Kerenidis and Prakash's quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning.

QUANTUM MACHINE LEARNING RECOMMENDATION SYSTEMS

q-means: A quantum algorithm for unsupervised machine learning

NeurIPS 2019 Morcu/q-means

For a natural notion of well-clusterable datasets, the running time becomes $\widetilde{O}\left( k^2 d \frac{\eta^{2. 5}}{\delta^3} + k^{2. 5} \frac{\eta^2}{\delta^3} \right)$ per iteration, which is linear in the number of features $d$, and polynomial in the rank $k$, the maximum square norm $\eta$ and the error parameter $\delta$.

QUANTUM MACHINE LEARNING

Quantum Neuron: an elementary building block for machine learning on quantum computers

30 Nov 2017inJeans/qnn

In the construction of feedforward networks of quantum neurons, we provide numerical evidence that the network not only can learn a function when trained with superposition of inputs and the corresponding output, but that this training suffices to learn the function on all individual inputs separately.

QUANTUM MACHINE LEARNING