Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles
- URL: http://arxiv.org/abs/2410.07692v3
- Date: Tue, 17 Dec 2024 11:02:25 GMT
- Title: Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles
- Authors: Franziska Greinert, Simon Goorney, Dagmar Hilfert-Rüppell, Malte S. Ubben, Rainer Müller,
- Abstract summary: The 2024 update to version2.5 extends it by the new proficiency triangle and qualification profiles.<n>The proficiency triangle proposes six proficiency levels for three proficiency areas, specifying knowledge and skills for each level.<n>Nine qualification profiles show prototypical qualifications or job roles relevant to the quantum industry.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the increasing industrial relevance of quantum technologies (QTs), a new quantum workforce with special qualification will be needed. Building this workforce requires educational efforts, ranging from short term training to degree programs. In order to plan, map and compare such efforts, personal qualifications or job requirements, standardization is necessary. The European Competence Framework for Quantum Technologies (CFQT) provides a common language for QT education. The 2024 update to version~2.5 extends it by the new proficiency triangle and qualification profiles: The proficiency triangle proposes six proficiency levels for three proficiency areas, specifying knowledge and skills for each level. Nine qualification profiles show prototypical qualifications or job roles relevant to the quantum industry, with the required proficiency, examples, and suggestions. This is an important step towards the standardization of QT education. The CFQT update is based on the results of an analysis of 34 interviews on industry needs. The initial findings from the interviews were complemented by iterative refinement and expert consultation.
Related papers
- The Quantum Technology Job Market: Data Driven Analysis of 3641 Job Posts [0.0]
Quantum Technology (QT) has created a growing demand for a specialized workforce, spanning academia and industry.
This study presents a quantitative analysis of the QT job market by systematically extracting and classifying thousands of job postings worldwide.
The research identifies key trends in regional job distribution, degree and skill requirements, and the evolving demand for QT-related roles.
arXiv Detail & Related papers (2025-03-24T14:41:16Z) - Quantum Computing Education in Latin America: Experiences and Strategies [0.0]
Quantum computing is a rapidly advancing field facing a significant shortage of qualified experts.
In Latin America, quantum education remains in its early stages, exacerbating the regional talent gap.
This work presents the efforts of the Quantum Computing and Artificial Intelligence research group at Universidad Nacional de Colombia to integrate quantum computing into higher education.
arXiv Detail & Related papers (2024-10-23T22:32:43Z) - Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking [59.87055275344965]
Job-SDF is a dataset designed to train and benchmark job-skill demand forecasting models.
Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023.
Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels.
arXiv Detail & Related papers (2024-06-17T07:22:51Z) - Foundations of Quantum Federated Learning Over Classical and Quantum
Networks [59.121263013213756]
Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies.
QFL can be deployed over both classical and quantum communication networks.
arXiv Detail & Related papers (2023-10-23T02:56:00Z) - Towards Quantum Federated Learning [80.1976558772771]
Quantum Federated Learning aims to enhance privacy, security, and efficiency in the learning process.
We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL.
As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries.
arXiv Detail & Related papers (2023-06-16T15:40:21Z) - Contributions from Pilot Projects in Quantum Technology Education as
Support Action to Quantum Flagship [0.0]
The goal of QTEdu is to pave the way for the training of the future quantum workforce.
New university courses need to be established to support emerging specific profiles such as a quantum engineer.
arXiv Detail & Related papers (2023-03-13T12:24:19Z) - FiTs: Fine-grained Two-stage Training for Knowledge-aware Question
Answering [47.495991137191425]
We propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance.
The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them.
The second stage, called knowledge-aware fine-tuning, aims to improve the model's joint reasoning ability.
arXiv Detail & Related papers (2023-02-23T06:25:51Z) - The Future Quantum Workforce: Competences, Requirements and Forecasts [0.0]
With the increasing industrial relevance of new quantum technologies, a well educated quantum workforce becomes increasingly crucial.
What are the expectations regarding the future relevance of second generation quantum technologies?
Which competences, knowledge and skills should the future employees have?
arXiv Detail & Related papers (2022-08-17T12:08:05Z) - ProQA: Structural Prompt-based Pre-training for Unified Question
Answering [84.59636806421204]
ProQA is a unified QA paradigm that solves various tasks through a single model.
It concurrently models the knowledge generalization for all QA tasks while keeping the knowledge customization for every specific QA task.
ProQA consistently boosts performance on both full data fine-tuning, few-shot learning, and zero-shot testing scenarios.
arXiv Detail & Related papers (2022-05-09T04:59:26Z) - When BERT Meets Quantum Temporal Convolution Learning for Text
Classification in Heterogeneous Computing [75.75419308975746]
This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification.
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
arXiv Detail & Related papers (2022-02-17T09:55:21Z) - Assessing the Needs of the Quantum Industry [2.619557992298662]
Quantum information science and technology (QIST) has progressed significantly in the last decade.
With the emergence of this new quantum industry, a new workforce trained in QIST skills and knowledge is needed.
We report on the results from a survey of 57 companies in the quantum industry.
arXiv Detail & Related papers (2021-08-25T17:13:46Z) - Preparing for the quantum revolution -- what is the role of higher
education? [3.2531696064515643]
We describe the types of activities being carried out in the quantum industry, profile the types of jobs that exist, and describe the skills valued across the quantum industry.
We present the training and hiring challenges the quantum industry is facing and how higher education may optimize the important role it is currently playing.
arXiv Detail & Related papers (2020-06-30T00:45:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.