# Quantum Machine Learning

77 papers with code • 2 benchmarks • 1 datasets

## Libraries

Use these libraries to find Quantum Machine Learning models and implementations## Most implemented papers

# PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation.

# TensorFlow Quantum: A Software Framework for Quantum Machine Learning

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

# A divide-and-conquer algorithm for quantum state preparation

Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space.

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

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.

# 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 $\delta$.

# Reinforcement Learning with Quantum Variational Circuits

This work explores the potential for quantum computing to facilitate reinforcement learning problems.

# Experimental Quantum Generative Adversarial Networks for Image Generation

For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.

# The power of quantum neural networks

We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks.

# Supervised quantum machine learning models are kernel methods

With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circuit.

# Practical distributed quantum information processing with LOCCNet

Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks.