Quantum Machine Learning
123 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
A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
ucl-ccs/qsvdd
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In this work, we propose a novel quantum machine learning method, called Quantum Support Vector Data Description (QSVDD), for practical anomaly detection, which aims to achieve both parameter efficiency and superior accuracy compared to classical models.
QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values and is an essential task in a wide range of domains including weather forecasting, stock market analysis, disease outbreak prediction.
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
To enhance the efficiency of ES, a RL agent dynamically adjusts the hyperparameters of ES.
Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification
Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data.
Quantum Deep Equilibrium Models
martaskrt/qdeq
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In this work, we present Quantum Deep Equilibrium Models (QDEQs): a training paradigm that learns parameters of a quantum machine learning model given by a PQC using DEQs.
Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization
nicomeyer96/regularized-qpg
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We show that training with RegQPG improves the robustness and generalization of the resulting policies.
Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks
With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention.
Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
dan-lb/integrated_encoding_for_QuanvNN
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We compare our proposed integrated model with a classical convolutional neural network and the well-known rotational encoding method, on two different classification tasks.
QuForge: A Library for Qudits Simulation
tiago939/QuForge
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Additionally, by constructing quantum circuits as differentiable graphs, QuForge facilitates the implementation of quantum machine learning algorithms, enhancing the capabilities and flexibility of quantum computing research.
AQMLator -- An Auto Quantum Machine Learning E-Platform
Tomev/AQMLator
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A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure.
