Dear Professor/Researcher,

Book Chapter proposal is invited for the edited book titled “Quantum Machine Learning (QML): Platform, Tools & Applications”.

The main goal of this book is to deliberate upon the various aspects of Quantum Machine Learning in distributed systems, cryptography and security by a galaxy of intellectuals from academia, researcher, professional community and industry. While this book would dwell on the foundations of Quantum Machine Learning as a part of transparency, scalability, integrity, security, it will also focus on contemporary topics for Research and Development on QML.

Topics for which Chapter proposals are invited:

Topic 4. Quantum Error Mitigation(QEM)

4.1 Introduction to quantum errors and noise

4.2 Quantum error mitigation techniques

4.3 Integrating QEM to the QML framework

Topic 5. Quantum Error Correction(QEC)

5.1. Introduction to quantum error correction

5.2 Quantum error correction techniques

5.3 Fault-tolerant quantum computing

Publisher:

ELSEVIER

Series: Advances in Computers Serial

Volume 140

Editors

Prof Shiho Kim[Chief Editor]

School of Integrated Technology, Yonsei University, South Korea

Ganesh Chandra Deka

Directorate General of Training, Ministry of Skill Development and Entrepreneurship, INDIA

With warm regards,

Shiho Kim

https://scholar.google.com/citations?hl=en&user=X3gOnQ0AAAAJ&view_op=list_works&sortby=pubdate

GC Deka

https://scholar.google.co.in/citations?user=Qw5HblgAAAAJ&hl=en

Creating a high-quality dataset for fine-tuning machine learning models is a crucial step in building robust and accurate models. The process of creating a dataset involves data collection, preprocessing, labeling, and validation. Here's a step-by-step guide to help you create a dataset for fine-tuning ML models:

Creating a high-quality dataset is a foundational step in machine learning, and it often requires substantial effort. Properly curated datasets are essential for training and fine-tuning models effectively.

Dear Yasir Nooruldeen Ibrahim ,

To create an instruction dataset for fine-tuning an LLM, start by cleaning and formatting domain-specific text, then use a pre-trained LLM to fine-tune it on the text dataset, and finally generate synthetic instruction-based fine-tuning data sets for the desired domain.

https://eightify.app/summary/artificial-intelligence-and-machine-learning/creating-an-instruction-dataset-for-fine-tuning-llm-tools-and-techniques#:~:text=and%20answer%20datasets.-,To%20create%20an%20instruction%20dataset%20for%20fine-tuning%20an%20LLM,sets%20for%20the%20desired%20domain.

Regards,

Shafagat