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# Quantum Machine Learning Edit

15 papers with code · Methodology

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# Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets

9 Jul 2020

With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process (the quantum analog of a function) with quantum training data.

# Recurrent Quantum Neural Networks

25 Jun 2020

In this work we construct a quantum recurrent neural network (QRNN) with demonstrable performance on non-trivial tasks such as sequence learning and integer digit classification.

# Classification with Quantum Machine Learning: A Survey

22 Jun 2020

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more.

# High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

13 Jun 2020

Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes.

# Experimental demonstration of a quantum generative adversarial network for continuous distributions

2 Jun 2020

In this paper, we employ a hybrid architecture for quantum generative adversarial networks (QGANs) and study their robustness in the presence of noise.

# Quantum Natural Language Processing on Near-Term Quantum Computers

8 May 2020

In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP.

# Quantum machine learning and quantum biomimetics: A perspective

25 Apr 2020

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies.

# Fast Quantum Algorithm for Learning with Optimized Random Features

22 Apr 2020

Here, we develop a quantum algorithm for sampling from this optimized distribution over features, in runtime $O(D)$ that is linear in the dimension $D$ of the input data.

# Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components

23 Mar 2020

Eigen component analysis network (ECAN), a network of concatenated ECA models, enhances ECA and gains the potential to be not only integrated with nonlinear models, but also an interface for deep neural networks to implement on a quantum computer, by analogizing a data set as recordings of quantum states.

# Robust data encodings for quantum classifiers

3 Mar 2020

Data representation is crucial for the success of machine learning models.