Quantum Machine Learning
88 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Quantum Machine Learning
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Libraries
Use these libraries to find Quantum Machine Learning models and implementations
iris
Most implemented papers
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
bagmk/Quantum_Machine_Learning_Express
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Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space.
Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components
chenmiaomiao/eca
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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.
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%.
Recurrent Quantum Neural Networks
rumschuettel/rvqe
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NeurIPS 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.
Quantum One-class Classification With a Distance-based Classifier
We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers.
The effect of data encoding on the expressive power of variational quantum machine learning models
Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions.
A rigorous and robust quantum speed-up in supervised machine learning
Over the past few years several quantum machine learning algorithms were proposed that promise quantum speed-ups over their classical counterparts.
Power of data in quantum machine learning
prantik-pdeb/Quantum-Machine-Learning
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These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems.
VSQL: Variational Shadow Quantum Learning for Classification
Classification of quantum data is essential for quantum machine learning and near-term quantum technologies.
Information-theoretic bounds on quantum advantage in machine learning
We prove that for any input distribution $\mathcal{D}(x)$, a classical ML model can provide accurate predictions on average by accessing $\mathcal{E}$ a number of times comparable to the optimal quantum ML model.
