Ethical, Regulatory, and Societal Dimensions in Quantum AI

Islam, Ray

Azizi Othman

The integration of Artificial intelligence (AI) and Quantum computing (QC) is redefining the future of pharmaceutical research and development.While AI-particularly Machine learning (ML) and deep learning-accelerates target identification, virtual screening, and de novo drug design, it often suffers from interpretability issues, data bias, and limited mechanistic understanding.Conversely, QC leverages quantum mechanics to achieve unprecedented accuracy in molecular simulations, yet remains restricted by noise, limited qubits in the Noisy Intermediate-Scale Quantum (NISQ) era, and high costs.Emerging hybrid quantum-classical algorithms and Quantum Machine Learning (QML) approaches bridge these gaps, enabling high-fidelity in silico drug discovery workflows.Despite these advances, the regulatory environment has not kept pace.Current frameworks, such as Physiologically Based Pharmacokinetic (PBPK) modelling and Quantitative Structure-Activity Relationship (QSAR) models, provide limited guidance for validating complex AI-QC systems.Challenges include the lack of explainable artificial intelligence (XAI), uncertainty in verifying quantum outputs, data integrity, and accountability in hybrid pipelines.These gaps highlight the urgent need for regulatory science to adopt dynamic validation frameworks, algorithmic audits, and international harmonization.This review critically examines the technological and regulatory dimensions of AI-QC synergy in drug discovery, outlining key applications, limitations, and ethical considerations.We propose pathways for explainability-by-design, continuous learning oversight, and regulatory sandboxes to facilitate safe and effective adoption.The synergy of AI and QC offers the potential to accelerate timelines, reduce costs, and deliver precision medicines globally-but its promise can only be realized through proactive, transparent, and ethical governance.

Dominique J Monlezun

Alexander Bohnert, Alexander Dotterweich, Svenja Ernst

Quantum computing holds the potential to improve handling computational uncertainty, predictive modeling, and optimization challenges which is particularly promising for the insurance and risk management fields. This paper presents the current state of literature on quantum computing applications within the insurance industry, with a focus on actuarial science. The novel application opportunities of quantum technologies in probabilistic modeling, data analyses, and optimization in areas including insurance capital modeling, pricing, and reserving are examined. The paper categorizes the literature as follows: (1) technical applications, (2) security and healthcare implications, and (3) legal and societal perspectives. Regulatory and socioeconomic concerns are evaluated, stressing the necessity of infrastructure readiness against quantum threats, particularly in securing sensitive data. Governance frameworks and ethical guidelines tailored to the insurance sector are discussed. By integrating technical, regulatory, and societal aspects, this paper provides a comprehensive view of the opportunities and challenges quantum computing presents to the insurance industry. The need for coordinated efforts across actuarial science, legal scholarship, and strategic management is emphasized to ensure secure and ethical implementation.

Victor Habib Lantyer