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This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve ...

To overcome these limitations, we propose (i) using machine learning to automate the selection of the set of rules to be combined along with their weights, i.e., customizing the CRS configuration ...

Semiconductor processing is notoriously challenging. It is one of the most intricate feats of modern engineering due to the extreme precision required and the hundreds of steps involved, such as ...

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field ...

Data Science Agent uses Anthropic PBC’s Claude large language model to dissect machine learning projects into logical steps and deliver executable pipeline components that can be run inside ...

It is challenging to train both classical and modern machine-learning force fields using the variety of experimental data available. Reversible simulation, a method to train force fields to match many ...

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may ...

Hardware Graphics Cards AMD just gave us our first glimpse of FSR 4's 'Redstone' update, with a host of machine learning-based improvements News By Andy Edser published 21 May 2025 ...

With the integration of quantum computing into classical machine learning, QML emerges as a powerful approach to enhance computational performance. Various classical machine learning algorithms, ...