المفاهيم الأساسية
Quantum Neural Networks hyperparameter optimization requires careful consideration of optimizers and initialization methods for improved performance.
الملخص
The article discusses the challenges of training Machine Learning models on conventional hardware due to the immense computational power required. It explores the potential of Quantum Machine Learning (QML) to offer speed-ups and enhanced performance. The study focuses on identifying impactful hyperparameters for QML models and provides performance data and suggestions for hyperparameter selection. The research evaluates different configurations using classical classification datasets and IBM Qiskit package for evaluation.
- Introduction to Machine Learning challenges and the potential of Quantum Machine Learning.
- Importance of hyperparameter tuning for Quantum Neural Networks.
- Evaluation of different hyperparameter configurations using classical datasets and IBM Qiskit package.
الإحصائيات
"Meta’s Llama LLMs require between 184,320 and 1,720,320 GPU hours for pretraining."
"The EU27 per-capita fossil fuel emissions for 2021 were 6.25 tCO2 equivalents."
"Recent quantum computing advances have opened the doors to quantum computing for researchers across various disciplines."
اقتباسات
"Quantum computers exploit principles of quantum mechanics, resulting in speed-ups over classical computers for certain computations."
"Our results show that the optimizer and initialization method constitute the most important hyperparameters for QNNs."