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Found 7 papers, 2 papers with code
Quantum-Enhanced Selection Operators for Evolutionary Algorithms
We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization.
Hybrid quantum ResNet for car classification and its hyperparameter optimization
We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization.
Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling
Within machine learning model evaluation regimes, feature selection is a technique to reduce model complexity and improve model performance in regards to generalization, model fit, and accuracy of prediction.
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.
Investigating Reinforcement Learning Agents for Continuous State Space Environments
Given an environment with continuous state spaces and discrete actions, we investigate using a Double Deep Q-learning Reinforcement Agent to find optimal policies using the LunarLander-v2 OpenAI gym environment.
Traffic flow optimization using a quantum annealer
Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems.
Quantum Physics Data Structures and Algorithms
Identifying Similarities in Epileptic Patients for Drug Resistance Prediction
For the second part of this study, classification algorithms such as Logistic Regression, Gradient Boosted Decision Trees, and SVMs, were tested on the reduced-dimensionality features, with accuracy results of 0. 83(+/-0. 3) testing using 7 fold cross validation.
