Publication Library
From Deep Learning to LLMs - A survey of AI in Quantitative Investment
Description: Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.
Created At: 07 April 2025
Updated At: 07 April 2025
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