Agriculture has undergone progressive transformations using ever-evolving technologies to increase productivity and profitability. A new approach to agricultural management based on concepts from the fourth industrial revolution is being gradually adopted. It aims to increase agricultural productivity while minimizing inputs and pollutants. Quantum computing has opened up new ventures for various fields, and one promising application is its integration into agriculture. This study aimed to evaluate the potential use of quantum computing in transforming smart agriculture practices to enhance sustainable crop management and increase yield. Traditional agriculture faces numerous challenges, including the need for more efficient resource allocation, precise monitoring of environmental conditions, and intelligent decision-making for optimal crop production. The innovation of new methods of agriculture brings into perspective how such capabilities can be integrated into crop and livestock farming to improve productivity, help control pests and diseases, and mitigate inherent risks. The research was explored through a narrative literature review and focused on developing quantum computing-based solutions for smart agriculture, including advanced optimization algorithms for resource allocation, quantum-enhanced machine learning models for crop disease prediction, and agricultural productivity, which can be assessed for climate change impact via modeling and simulation tools. Harnessing quantum systems' inherent parallelism and computational speed will provide farmers with actionable insights and recommendations for maximizing yield while minimizing environmental impact. The findings indicated that quantum computing could increase the precision of monitoring crop farming initiatives, livestock farming, and agricultural productivity.