Quantum Computing: The Future of Big Data and Artificial Intelligence in Spine
Abstract
With the emergence of big data and more personalized approaches to spine care and predictive modeling, data science and deep analytics are taking center-stage. Although current techniques in machine learning and artificial intelligence have gained attention, their applications remain limited by their reliance on traditional analytic platforms. Quantum computing has the ability to circumvent such constraints by attending to the various complexities of big data while minimizing space and time dimensions within computational algorithms. In doing so, quantum computing may one day address research and clinical objectives that currently cannot be tackled. Understanding quantum computing and its potential to improve patient management and outcomes is therefore imperative to drive further advancements in the spine field for the next several decades.
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.