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# Quantum Machine Learning Edit

15 papers with code · Methodology

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# Variational quantum Gibbs state preparation with a truncated Taylor series

By performing numerical experiments, we show that shallow parameterized circuits with only one additional qubit can be trained to prepare the Ising chain and spin chain Gibbs states with a fidelity higher than 95%.

209
18 May 2020

# TensorFlow Quantum: A Software Framework for Quantum Machine Learning

6 Mar 2020tensorflow/quantum

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.

935
06 Mar 2020

# Transfer learning in hybrid classical-quantum neural networks

We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements.

13
17 Dec 2019

# Quantum Wasserstein Generative Adversarial Networks

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.

18
01 Dec 2019

# q-means: A quantum algorithm for unsupervised machine learning

Along with the algorithm, the theorems and tools introduced in this paper can be reused for various applications in quantum machine learning.

0
01 Dec 2019

# Quantum Wasserstein Generative Adversarial Networks

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.

18
31 Oct 2019

A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.

78
04 Sep 2019

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

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.

18
08 Jul 2019

# Variational Quantum Circuits for Deep Reinforcement Learning

30 Jun 2019ycchen1989/Var-QuantumCircuits-DeepRL

The state-of-the-art Machine learning approaches are based on classical Von-Neumann computing architectures and have been widely used in many industrial and academic domains.

8
30 Jun 2019

# q-means: A quantum algorithm for unsupervised machine learning

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 $\delta$.

4
10 Dec 2018