Hybrid Algorithms for Medical Insights Using Quantum Computing
Abstract
In the field of medicine, machine learning has become very important. It's helping us identify diseases better, take care of patients more personally, and improve many important parts of healthcare. With more and more medical information available, machine learning, which relies on big sets of data, can make predictions more accurate and help doctors make better decisions. This chapter looks closely at how machine learning is being used in medicine right now. It also talks about how quantum computing could change healthcare. First, it explains different ways machine learning is used and how it's being used in medicine. It talks about things like diagnosing diseases, finding new medicines, and treating patients in a way that's right for them. Then, it explores how machine learning and quantum computing could work together. Quantum computing uses special particles to do many calculations at once. This could help process medical information much faster. Quantum computing could also help simulate how molecules interact, which is important for developing new medicines.
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.