Harnessing quantum technology in food and nutrition sciences: a scoping review of innovations, challenges, and future directions
Abstract
Abstract There is a burgeoning interest in emerging quantum technologies and how to make optimal use of them. This review provides a comprehensive overview of the current literature on quantum sensing and quantum computing in the field of food and nutrition science. A systematic search was conducted across Medline, Embase, Global Health, Web of Science, IEEE Xplore, and Google Scholar, up to August 20, 2024. Non-English, non-peer-reviewed articles and those solely about traditional computing without quantum applications were excluded. A total of 58 articles were included, highlighting emerging quantum technologies for nutrient detection and dietary optimization. Quantum sensing, particularly with quantum dots, shows high sensitivity for assessing food quality and composition parameters, including vitamins and antioxidants. In addition, quantum-inspired algorithms, such as the Hybrid Quantum Genetic Algorithm, are being developed for personalized nutrition and diet optimization. However, many applications are still in the early stages, with limited empirical validation and challenges, such as material toxicity and limited integration with traditional methods. Although promising, the practical benefits of quantum technology over classical methods are currently marginal, necessitating further research and technological advancements.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.