Quantum Computing for Analyzing CD4 Lymphocytes and Antiretroviral Therapy
Classify HIV phenotype interactions using k-means clustering
Mean Squared Errors for Interaction Classification
Mean Squared Errors for Payoff Predictions
Mean Squared Errors for Interaction Classification
Find Nearest Payoff
Create Mean Differences from Longitudinal CD4 Data
Create Mean Standardized Differences from Longitudinal CD4 Data
Estimate Payoff Parameters for HIV Phenotype Interactions
Create Mean Differences from Longitudinal Viral Load Data
Create Mean Differences from Logarithmic Viral Load Data
Create Mean Standardized Differences from Logarithmic Viral Load Data
Compute Payoff Values for Quantum HIV Phenotype Interactions
Calculate Final State and Payoffs in Quantum Game
BB84 Quantum Key Distribution (QKD) Simulation
E91 Quantum Key Distribution (QKD) Simulation
Summarize an InteractionClassification object
Summarize Payoffs
Summarize an InteractionClassification object
Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package 'qsimulatR' (Ostmeyer and Urbach, 2023, <https://CRAN.R-project.org/package=qsimulatR>), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.