Quantum Machine Learning
118 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
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.
Better than classical? The subtle art of benchmarking quantum machine learning models
Benchmarking models via classical simulations is one of the main ways to judge ideas in quantum machine learning before noise-free hardware is available.
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
luthierman/quantum-research-colab
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This work explores the potential for quantum computing to facilitate reinforcement learning problems.
Experimental Quantum Generative Adversarial Networks for Image Generation
niccolot/QML_ComputerVision
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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
amyami187/effective_dimension
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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
sophchoe/Binary_Classification_Pennylane_Keras
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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.
