QueST

"QueST," a term appearing across diverse research areas, broadly refers to the pursuit of improved efficiency and interpretability in various machine learning contexts. Current research focuses on developing novel algorithms and architectures, such as graph transformers and quantized skill transformers, to enhance model performance in tasks ranging from long-context language modeling to robot control and quantum circuit reliability estimation. These efforts aim to address limitations in existing methods, particularly concerning generalization, scalability, and the explainability of model decisions, ultimately impacting fields from robotics and natural language processing to quantum computing.