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
Latest papers
On Optimizing Hyperparameters for Quantum Neural Networks
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training.
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.
Guided Quantum Compression for Higgs Identification
To ameliorate this issue, we design an architecture that unifies the preprocessing and quantum classification algorithms into a single trainable model: the guided quantum compression model.
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models.
Distributed Quantum Neural Networks via Partitioned Features Encoding
To mitigate these challenges, an approach using distributed quantum neural networks has been proposed to make a prediction by approximating outputs of a large circuit using multiple small circuits.
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
Training robust and generalizable quantum models
We derive tailored, parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against perturbations in the input data.
sQUlearn -- A Python Library for Quantum Machine Learning
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn.
A general learning scheme for classical and quantum Ising machines
lsschmid/ising-learning-model
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In particular, in the quantum realm, the quantum resources are used for both the execution and the training of the model, providing a promising perspective in quantum machine learning.
Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines
This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms.
