Publication Library
A Framework for Curriculum Transformation in Quantum Information Science and Technology Education
Description: The field of Quantum Information Science & Technology (QIST) is booming. Due to this, many new educational courses and university programs are needed in order to prepare a workforce for the developing industry. Owing to its specialist nature, teaching approaches in this field can easily become disconnected from the substantial degree of science education research which aims to support the best approaches to teaching in Science, Technology, Engineering & Mathematics (STEM) fields. In order to connect these two communities with a pragmatic and repeatable methodology, we have synthesised this educational research into a decision-tree based theoretical model for the transformation of QIST curricula, intended to provide a didactical perspective for practitioners. The Quantum Curriculum Transformation Framework (QCTF) consists of four steps: 1. choose a topic, 2. choose one or more targeted skills, 3. choose a learning goal and 4. choose a teaching approach that achieves this goal. We show how this can be done using an example curriculum and more specifically quantum teleportation as a basic concept of quantum communication within this curriculum. By approaching curriculum creation and transformation in this way, educational goals and outcomes are more clearly defined which is in the interest of the individual and the industry alike. The framework is intended to structure the narrative of QIST teaching, and with future testing and refinement it will form a basis for further research in the didactics of QIST.
Created At: 07 April 2025
Updated At: 07 April 2025
The Quantum Technology Job Market - Data Driven Analysis of 3641 Job Posts
Description: The rapid advancement of Quantum Technology (QT) has created a growing demand for a specialized workforce, spanning across 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 classification pipeline leverages large language models (LLMs) whilst incorporating a "human-in-the-loop" validation process to ensure reliability, achieving an F1-score of 89%: a high level of accuracy. The research identifies key trends in regional job distribution, degree and skill requirements, and the evolving demand for QT-related roles. Findings reveal a strong presence of the QT job market in the United States and Europe, with increasing corporate demand for engineers, software developers, and PhD-level researchers. Despite growing industry applications, the sector remains in its early stages, dominated by large technology firms and requiring significant investment in education and workforce development. The study highlights the need for targeted educational programs, interdisciplinary collaboration, and industry-academic partnerships to bridge the QT workforce gap.
Created At: 07 April 2025
Updated At: 07 April 2025
Cyber Threats in Financial Transactions -- Addressing the Dual Challenge of AI and Quantum Computing
Description: The financial sector faces escalating cyber threats amplified by artificial intelligence (AI) and the advent of quantum computing. AI is being weaponized for sophisticated attacks like deepfakes and AI-driven malware, while quantum computing threatens to render current encryption methods obsolete. This report analyzes these threats, relevant frameworks, and possible countermeasures like quantum cryptography. AI enhances social engineering and phishing attacks via personalized content, lowers entry barriers for cybercriminals, and introduces risks like data poisoning and adversarial AI. Quantum computing, particularly Shor's algorithm, poses a fundamental threat to current encryption standards (RSA and ECC), with estimates suggesting cryptographically relevant quantum computers could emerge within the next 5-30 years. The "harvest now, decrypt later" scenario highlights the urgency of transitioning to quantum-resistant cryptography. This is key. Existing legal frameworks are evolving to address AI in cybercrime, but quantum threats require new initiatives. International cooperation and harmonized regulations are crucial. Quantum Key Distribution (QKD) offers theoretical security but faces practical limitations. Post-quantum cryptography (PQC) is a promising alternative, with ongoing standardization efforts. Recommendations for international regulators include fostering collaboration and information sharing, establishing global standards, supporting research and development in quantum security, harmonizing legal frameworks, promoting cryptographic agility, and raising awareness and education. The financial industry must adopt a proactive and adaptive approach to cybersecurity, investing in research, developing migration plans for quantum-resistant cryptography, and embracing a multi-faceted, collaborative strategy to build a resilient, quantum-safe, and AI-resilient financial ecosystem
Created At: 07 April 2025
Updated At: 07 April 2025
Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Records
Description: Objective: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelling of disease outcomes. This study developed an efficient strategy based on advanced large language models to identify multiple conditions from EHR clinical notes. Methods: We linked a cardiac registry cohort in 2015 with an EHR system in Alberta, Canada. We developed a pipeline that leveraged a generative large language model (LLM) to analyze, understand, and interpret EHR notes by prompts based on specific diagnosis, treatment management, and clinical guidelines. The pipeline was applied to detect acute myocardial infarction (AMI), diabetes, and hypertension. The performance was compared against clinician-validated diagnoses as the reference standard and widely adopted International Classification of Diseases (ICD) codes-based methods. Results: The study cohort accounted for 3,088 patients and 551,095 clinical notes. The prevalence was 55.4%, 27.7%, 65.9% and for AMI, diabetes, and hypertension, respectively. The performance of the LLM-based pipeline for detecting conditions varied: AMI had 88% sensitivity, 63% specificity, and 77% positive predictive value (PPV); diabetes had 91% sensitivity, 86% specificity, and 71% PPV; and hypertension had 94% sensitivity, 32% specificity, and 72% PPV. Compared with ICD codes, the LLM-based method demonstrated improved sensitivity and negative predictive value across all conditions. The monthly percentage trends from the detected cases by LLM and reference standard showed consistent patterns.
Created At: 07 April 2025
Updated At: 07 April 2025
Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning
Description: Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges often rely on population-level statistics, which may not adequately account for the influence of inter-individual variability driven by factors such as lifestyle and genetics. In this work, we introduce a novel framework for predicting future blood biomarker values and define personalized references through learned representations from lifestyle data (physical activity and sleep) and blood biomarkers. Our proposed method learns a similarity-based embedding space that captures the complex relationship between biomarkers and lifestyle factors. Using the UK Biobank (257K participants), our results show that our deep-learned embeddings outperform traditional and current state-of-the-art representation learning techniques in predicting clinical diagnosis. Using a subset of UK Biobank of 6440 participants who have follow-up visits, we validate that the inclusion of these embeddings and lifestyle factors directly in blood biomarker models improves the prediction of future lab values from a single lab visit. This personalized modeling approach provides a foundation for developing more accurate risk stratification tools and tailoring preventative care strategies. In clinical settings, this translates to the potential for earlier disease detection, more timely interventions, and ultimately, a shift towards personalized healthcare.
Created At: 07 April 2025
Updated At: 07 April 2025
