<![CDATA[Indonesia faces pressing challenges in achieving sustainable food self-sufficiency due to climate change, population growth, land degradation, and inefficient agricultural systems. To address these multifaceted issues, the Indonesian government is exploring the integration of cutting-edge technologies, including quantum computing, into national agricultural policies. This paper examines how quantum computing can transform sustainable food production by enabling advanced simulations, optimizing supply chains, and enhancing precision agriculture practices. The paper explores the theoretical framework, policy implications, and practical pathways for implementing quantum-enabled solutions, contributing to national food security and global sustainability agendas. The methodology of the research adopts an in-depth literature review and a qualitative analytical approach to explore the integration of quantum computing within Indonesia’s sustainable food production and self-sufficiency policy framework. Given the emerging nature of quantum technology and the evolving landscape of food security policies, a qualitative design enables in-depth examination of theoretical linkages, policy readiness, and potential use cases. The study emphasizes conceptual policy analysis, supported by secondary data, expert insights, and global benchmarking of technological trends. The research used technical framework analysis of the policy gap analysis, technology policy integration matrix, SWOT analysis (strengths, weaknesses, opportunities, threats), and case study analysis from other countries. The integration of quantum computing into Indonesia's sustainable food production policies could significantly enhance the country's food self-sufficiency program. By leveraging advanced computational capabilities, Indonesia can optimize agricultural practices, improve resource management, and address food security challenges posed by climate change and population growth. The following sections outline key aspects of this policy analysis and recommendations. ]]>
