Relevance of Quantum Computing in Healthcare: A Review

Kinjal Gandhi, Dweepna Garg, Milind Shah +2 more

Sudheer Panyaram

Advancements in quantum computing hold the potential to revolutionize artificial intelligence (AI), particularly in the field of healthcare. This paper explores how quantum computing can be leveraged to improve predictive analytics and facilitate personalized medicine. Through enhanced computational capacity, quantum computing enables faster processing and analysis of large, complex datasets, essential for predictive models in healthcare. This integration can lead to more precise diagnostics, treatment options, and disease prevention strategies by refining AI’s capability to handle vast data. Quantum algorithms such as quantum neural networks and quantum support vector machines can significantly optimize machine learning models, reducing the time required for data processing and improving predictive accuracy. Personalized medicine, an emerging trend in healthcare, relies heavily on detailed patient data to tailor treatments. The combination of quantum computing and AI promises to make this process faster, more accurate, and scalable. This study presents a detailed review of the current state of quantum AI in healthcare, highlights methodologies for data analysis, and showcases a comprehensive architecture for integrating quantum computing in AI healthcare systems. We analyse relevant data from case studies to demonstrate the advantages and challenges of adopting quantum computing for AI-driven healthcare applications.

Aishwarya Sundaram

Sanjay Chakraborty

The working environment in healthcare analytics is transforming with the emergence of healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing a comprehensive set of case studies, we also review relevant literature from the fields of quantum computing applications and smart healthcare analytics, focusing on the implications of quantum deep neural networks. This study aims to shed light on the existing research gaps regarding the implications of quantum neural networks in healthcare analytics. We argue that the healthcare industry is currently transitioning from automation towards genuine collaboration with quantum networks, which presents new avenues for research and exploration. Specifically, this study focuses on evaluating the performance of Healthcare 5.0, which involves the integration of diverse quantum machine learning and quantum neural network systems. This study also explores a range of potential challenges and future directions for Healthcare 5.0, particularly focusing on the integration of quantum neural networks.

Adnan Qayyum, Raihan Ur Rasool, Hafiz Farooq Ahmad +3 more

Quantum computing is an emerging field of research that can provide a “quantum leap” in terms of computing performance and thereby enable many new exciting healthcare applications such as rapid DNA sequencing, drug research and discovery, personalized medicine, molecular simulations, diagnosis assistance, efficient radiotherapy. In this paper, we provide a taxonomy of existing literature on quantum healthcare systems and identify the key requirements of quantum computing implementations in the healthcare paradigm. We also provide a through exploration of the application areas where quantum computing could transform traditional healthcare systems. Finally, we perform an extensive study of quantum cryptography from the perspective of healthcare systems to identify security vulnerabilities in traditional cryptography systems.

Elakkiya Elango, Gayathri Nagasubramanian, S. Rakesh Kumar