Harnessing quantum technology in food and nutrition sciences: a scoping review of innovations, challenges, and future directions
Karim Karbin, Vyom Patel, Mojtaba Shafiee, Adam D. G. Baxter-Jones, MARTA C. ERLANDSON, Ginny L Lane, Philip D. Chilibeck, Steven Rayan, Hassan Vatanparast
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.
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.
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.
This paper presents a collection of student-led review studies covering selected topics in modern quantum information technologies. The contributions explore a broad range of theoretical concepts, hardware architectures, and application domains, including quantum computing models, quantum sensing, photonic systems, and optimization techniques. The reviewed works examine both gate-based and alternative quantum computing paradigms, as well as hybrid quantum–classical approaches, highlighting their potential advantages and current technological limitations. Particular attention is given to the role of quantum algorithms and physical implementations in addressing computationally challenging problems, such as combinatorial optimization and simulation tasks. Overall, the paper provides a snapshot of the current landscape of quantum technologies from an interdisciplinary perspective, emphasizing ongoing challenges related to scalability, noise, and practical applicability, while outlining promising directions for future research and development.
Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum Machine Learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domain.
Quantum computing is at the cutting edge of technological innovation, with the potential to transform a wide array of scientific and practical fields. This literature survey addresses the critical need for a structured understanding of recent advancements and identifies research gaps. It comprehensively reviews recent advancements in quantum computing, emphasizing non-standard architectures, applications in material sciences, and the integration of quantum and classical machine learning models. The survey also examines quantum algorithms for real-time systems, explores cryptographic protocols beyond Shor's algorithm, and discusses resource management in cloud computing. Key findings highlight significant progress in algorithm efficiency, practical applications in 2D materials and topological insulators, and the hybridization of quantum and classical models. Challenges such as scalability, resource optimization, and ethical concerns still remain. This paper guides future research to focus on optimizing algorithms, efficient resource allocation, and enhancing security measures. This work synthesizes current knowledge, guiding future research efforts and contributing to the advancement of the quantum computing research community.
Abstract Animal nutrition and agricultural systems are defined by complex, interconnected challenges involving biological nonlinearity, uncertainty, highdimensional data, and competing objectives. Although classical computing, statistical modeling, and artificial intelligence have enabled significant advances, many core problems, such as largescale optimization, multivariate prediction, and the integration of heterogeneous biological and production data, become difficult to address as problems scale in size and complexity. Quantum computing is an emerging computational paradigm that leverages quantum mechanical principles to explore solution spaces in fundamentally different ways than classical systems. While quantum hardware is still in an early stage of development, hybrid quantum-classical approaches are beginning to reveal potential applications relevant to both animal nutrition and agriculture, particularly in areas that combinatorial complexity, uncertainty, or computational intractability limit conventional methods. Beyond its theoretical appeal, quantum computing is attracting attention because many agricultural systems problems are inherently combinatorial, dynamic, and multi-objective. Animal nutrition, for example, often requires balancing biological responses, economic constraints, ingredient variability, and environmental outcomes within the same decision framework. These characteristics make the field a useful case study for discussing where quantum-inspired or hybrid quantum-classical methods may eventually offer practical value. This presentation explores quantum computing and what makes it fundamentally different from traditional computing. Key approaches and modalities are introduced, along with their strengths and weaknesses. The talk concludes by examining specific potential uses cases in agriculture and animal nutrition, including ration formulation, resource allocation, systems modeling, and decision support under uncertainty, and how quantum computing may provide an advantage as these fields move toward increasingly data-rich and computationally demanding problems.
The current rise of quantum technology is compelled by quantum sensing research. Thousands of research labs are developing and testing a broad range of sensor prototypes. However, there is a lack of knowledge about specific applications and real-world use cases where the benefits of these sensors will be most pronounced. This study presents a comprehensive review of quantum sensing state-of-practice. It also provides a detailed analysis of how quantum sensing overcomes the existing limitations of sensor-driven systems’ precision and performance. Based on the review of over 500 quantum sensor prototype reports, we determined four groups of quantum sensors and discussed their readiness for commercial usage. We concluded that quantum magnetometry and quantum optics are the most advanced sensing technologies with empirically proven results. In turn, quantum timing and kinetics are still in the early stages of practical validation. In addition, we defined four systems domains in which quantum sensors offer a solution for existing limitations of conventional sensing technologies. These domains are 1) GPS-free positioning and navigating services, 2) time-based operations, 3) topological visibility, and 4) environment detection, prediction, and modeling. Finally, we discussed the current constraints of quantum sensing technologies and offered directions for future research.