ABSTRACT
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, Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis 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.
Keywords: Agricultural policy, food self-sufficiency, Indonesia, precision agriculture, quantumcomputing, rural development, smart farming, sustainable food production, food security, technology integration.
INTRODUCTION
Indonesia, as the world’s fourth most populous country, faces increasing pressure to secure a stable and sustainable food supply for its citizens. The government has long promoted food self-sufficiency as a national priority, particularly in staple commodities such as rice, corn, and soybeans. However, achieving this goal has been challenged by rapid urbanization, climate variability, degraded natural resources, and inefficiencies in agricultural production and logistics (Siregar & Rachman, 2021). In response, there is a growing policy shift toward integrating advanced technologies to optimize food systems. Among these innovations, quantum computing emerges as a transformative frontier with the potential to revolutionize sustainable food production and strengthen Indonesia's food sovereignty.
Quantum computing, which leverages the principles of quantum mechanics to process information in fundamentally new ways, can solve highly complex optimization and simulation problems that are intractable for classical computers (Preskill, 2018). This capability is particularly relevant for agriculture, where variables such as climate models, soil conditions, crop genetics, pest outbreaks, and supply chain logistics require simultaneous consideration across dynamic environments. In the Indonesian context, quantum computing could be utilized to enhance precision agriculture, simulate climate-resilient crop properties, and optimize large-scale distribution networks—ultimately supporting the national objective of food self-sufficiency.
Recent global studies suggest that quantum computing could shorten the time needed for plant genome analysis, identify efficient fertilizer use, and forecast market demand with greater accuracy (Madsen et al., 2022). If adopted through inclusive and well-structured policies, Indonesia could leapfrog traditional development stages and create a quantum-empowered agricultural ecosystem, enabling sustainable food production that is both technologically advanced and environmentally responsible.
Furthermore, Indonesia’s digital transformation blueprint (RPJMN 2020–2024) and the Ministry of Agriculture’s vision for Smart Agriculture 4.0 provide fertile ground for integrating quantum technologies into national agricultural policy. Strategic collaboration with academic institutions, quantum computing firms, and international organizations is necessary to develop technical capabilities and regulatory frameworks (Badan Pusat Statistik, 2023; Ministry of National Development Planning, 2020). By embedding quantum computing within its agricultural modernization agenda, Indonesia can address food security risks while accelerating its transition to a low-carbon, knowledge-based economy.
This paper explores the intersection of quantum computing and Indonesian food policy reform, evaluating how this collaboration could strengthen the resilience and productivity of Indonesia’s food systems. It aims to provide policymakers and researchers with a conceptual foundation and practical roadmap for leveraging quantum technologies to fulfil the country's vision of a self-sufficient, sustainable, and technologically integrated agricultural future.
METHODOLOGY
Research design
This study adopts an in-depth literature review and a qualitative exploratory research design with a policy analysis 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 rests on three main theoretical supports: (1) Food Security Theory, where food security is defined as the condition where all people have physical and economic access to sufficient, safe, and nutritious food (FAO, 2021). The research applies this lens to analyze how quantum computing can support the four dimensions of food security: availability, access, utilization, and stability; (2) Innovation and Technology Adoption Theory—Based on Rogers’ Diffusion of Innovations (Roger, 2003 in Yadav et al., 2022), the study investigates the barriers and drivers of adopting quantum computing in agricultural contexts, especially among policymakers and institutions; and (3) Sustainability Transitions Theory—This theory (Geels, 2019) explains how socio-technical innovations disrupt and replace existing practices. It is applied to examine how quantum computing can accelerate Indonesia’s transition from resource-intensive farming models toward sustainable food production.
The main objectives of this methodological approach are to identify how quantum computing capabilities align with Indonesia’s agricultural policy goals, to assess the national readiness for adopting quantum solutions in the agricultural sector, and to propose a strategic roadmap for integrating quantum technology into sustainable food production policies.
This method is suitable for understanding complex and multidisciplinary intersections between emerging technologies and national development strategies, especially when empirical implementations are limited or still in planning phases.
Data sources
The study relies primarily on secondary data sources collected from a range of relevant and credible materials, including government policy documents: National Medium-Term Development Plan (RPJMN 2020–2024), Food Estate Program policy notes, Smart Agriculture 4.0 roadmap from the Ministry of Agriculture, and reports from Bappenas (Ministry of National Development Planning). Academic literature and journals: Peer-reviewed articles related to food security, quantum computing, precision agriculture, and digital transformation in developing countries. Industry and technology reports: White papers and technical publications from quantum computing companies (e.g., IBM, D-Wave, Google Quantum AI) and global institutions such as FAO and OECD on Agri-tech innovation. Media and press releases: Announcements, speeches, and progress updates from the Ministry of Agriculture and Ministry of Communication and Information Technology regarding digital transformation and quantum-related partnerships. If available, expert interviews and government roundtable discussions may be analysed in future iterations of this study to validate the findings and incorporate stakeholder perspectives.
Analytical framework
This research applies a step of technical framework to analyze the potential integration of quantum computing into Indonesia’s food self-sufficiency policy: policy gap analysis identifies weaknesses in Indonesia’s current sustainable food production strategies. Technology–Policy Integration Matrix, this matrix evaluates the match between quantum computing capabilities and policy needs across four domains: (1) Food Production: enhancing productivity through quantum-enhanced simulation and genome analysis; (2) Supply Chain Management: optimizing logistics, reducing food waste, and improving distribution efficiency; (3) Environmental Sustainability: modeling resource usage, soil health, and emissions under complex climate variables; and (4) Governance and Planning: supporting policy simulation and predictive modeling for long-term planning. Each domain is assessed based on relevance to national goals, technological feasibility, infrastructure readiness, and policy alignment.
RESULT AND DISCUSSION
Policy gap analysis
Indonesia's sustainable food production strategies face several key policy gaps that hinder the achievement of food self-sufficiency, particularly in rice production. These gaps stem from a lack of comprehensive approaches that integrate environmental sustainability, local food systems, and effective agricultural practices. The following sections outline the critical areas where policy improvements are necessary.
Overreliance on monocropping: Current policies heavily favor rice production, leading to low dietary diversity and neglect of local food systems (Nurhasan et al., 2021). The past Food Estate Programs (FEP) have shown that focusing on monocropping can damage the environment and overlook local agricultural capacities (Nurhasan et al., 2021). Ineffective agricultural support: Existing agricultural supports are often ineffective, failing to enhance productivity or farmer incomes (Wihardja et al., 2023). There is a need for policies that redirect subsidies to directly support farmers and promote sustainable practices (Wihardja et al., 2023). A lack of agricultural extension services and climate information hampers farmers' ability to adapt to changing conditions (Wihardja et al., 2023). Strengthening these services is crucial for improving productivity and resilience in food systems (Wihardja et al., 2023). Environmental considerations: Policies must address the environmental impacts of agriculture, including greenhouse gas emissions from land-use changes and deforestation (Wihardja et al., 2023). Implementing agroecological practices can enhance soil health and reduce reliance on chemical fertilizers (Susanti et al., 2024).
Table 1. Indonesia’s sustainable food production strategies
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Policy area
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Current weakness / gap
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Evidence (Govt. data / reports)
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Relevance to quantum computing integration
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Productivity & yield stability
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Rice and maize yields stagnated in the last decade despite policy support.
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BPS (2022) reports rice productivity averages 5.1 ton/ha with slow growth; Ministry of Agriculture (2021) highlights reliance on traditional practices.
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Quantum computing can optimize crop models, predict yield outcomes under multiple scenarios, and guide precision interventions.
|
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Climate resilience
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Vulnerability to droughts and floods; limited adoption of climate-smart agriculture.
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Indonesia Climate Risk Profile (World Bank, 2021); Ministry of Agriculture (2020) notes climate hazards reduced harvest areas by 10–15%.
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Quantum models simulate climate–crop interactions at scale, supporting adaptive cropping and water management policies.
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Food distribution & logistics
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Post-harvest losses remain high (10–12% for rice). Cold chain systems underdeveloped.
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BPS (2021) shows food loss at ~11%; FAO (2020) confirms supply chain inefficiency.
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Quantum optimization applied to logistics networks reduces losses, improves cold-chain routing, and ensures food stability.
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Technology adoption
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Digital agriculture adoption remains low among smallholders; fragmented policy implementation.
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Ministry of Agriculture (2021) reports <15% of smallholders using digital tools.
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Quantum computing can integrate large datasets (soil, climate, market) to provide real-time decision-making support.
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Food security governance
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Policies are fragmented across ministries; weak coordination in food security programs.
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OECD (2022) Food and Agriculture Review: Indonesia; National Food Agency report (2022).
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Quantum-based policy simulations can help identify optimal policy mixes and strengthen inter-ministerial coordination.
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Sources: Badan Pusat Statistik (2022); Food and Agriculture Organization (2020); Ministry of Agriculture (2021); OECD (2022); World Bank (2021).
In contrast, some argue that focusing solely on increasing rice production may overlook the broader context of food security, which includes diverse dietary needs and environmental sustainability. A balanced approach that integrates these aspects is essential for long-term food self-sufficiency in Indonesia.
The analysis of Indonesia's current sustainable food production strategies reveals several weaknesses that could hinder the successful implementation of the proposed revolutionizing policy on sustainable food production with quantum computing. These weaknesses stem from systemic issues in agricultural practices, policy coherence, and resource management.
Ineffective agricultural practices by monocropping focus policies emphasize high-value commodities, particularly rice, which limits dietary diversity and environmental sustainability (Nurhasan et al., 2021). Small-scale farmer dominance complicates the transition to sustainable practices, as they often lack access to modern technologies and resources (Suryana, 2014).
Policy coherence and integration of stakeholders have significant overlap among various ministries involved in policy coherence and integration of stakeholders, leading to inefficiencies in policy implementation and a lack of unified direction in food security efforts (Dermoredjo et al., 2024). Existing agricultural support mechanisms are ineffective, failing to provide necessary resources and information to farmers (Wihardja et al., 2023.). Food Quality Issues despite sufficient food supply, the nutritional quality of food remains below recommended standards, indicating a need for policy adjustments to enhance food quality alongside quantity (Suryana, 2014).
Technology–policy integration matrix
The integration of quantum computing into Indonesia's policy framework for sustainable food production represents a transformative approach to achieving food self-sufficiency. This innovative technology can enhance agricultural productivity, optimize resource management, and improve food safety standards, thereby supporting the government's food self-sufficiency program. Agricultural productivity by quantum computing can analyze vast datasets to optimize crop yields and resource allocation, leading to more efficient farming practices. It enables predictive modeling for climate impacts on agriculture, allowing farmers to adapt to changing conditions effectively (Herawati et al., 2023).
Table 2. Technology - policy integration matrix
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Quantum computing application
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Agricultural use case
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Current policy status (Indonesia)
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Integration strategy
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Responsible institutions
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Quantum simulation of crop genomics
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Simulating plant responses to climate stress and nutrient optimization
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Not addressed in Smart Farming 4.0 or RIRN
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Include in BRIN’s research priorities and the Ministry of Agriculture’s R&D programs
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BRIN, Ministry of Agriculture, LIPI, IPB
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Quantum forecasting algorithms
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Early warning systems for drought, pest outbreaks, and yield forecasting
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Covered partially under BMKG and BPPT weather tools, but not quantum-enhanced
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Pilot quantum-enhanced models in collaboration with BMKG and international partners
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BMKG, BRIN, Ministry of Agriculture
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Quantum supply chain optimization
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Minimizing food spoilage and logistics cost in archipelagic regions
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Logistics optimization is addressed in Food Security Roadmap, not quantum-based
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Embed quantum route optimization in the Ministries of Trade and Agriculture's food logistics plans
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Ministry of Trade, Ministry of Agriculture, Kominfo, National Logistics Agency
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Quantum Resource Allocation Models
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Efficient irrigation, land use planning, and fertilizer allocation
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Smart Farming 4.0 uses basic AI-based models
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Upgrade digital farming models with quantum-enhanced optimization tools
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Ministry of Agriculture, Ministry of Environment and Forestry
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Quantum-Enhanced Climate Modeling
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Predictive modeling of long-term climate impacts on staple crops
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Partially supported via BRIN’s climate research and international climate collaborations
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Expand existing models to test scenarios using quantum algorithms
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BRIN, Ministry of Environment and Forestry, BMKG
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|
Quantum Secure Data Systems
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Secure farm and land data transmission in e-farming systems
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Cybersecurity is present in the Digital Economy Framework.
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Adopt quantum cryptography standards in agricultural IoT systems
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Kominfo, BSSN, Ministry of Agriculture
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Quantum Education and Capacity Building
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Upskilling agricultural researchers and tech developers in quantum tech
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Not yet developed in agricultural curriculum
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Create academic and vocational modules in collaboration with universities
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BRIN, LPDP, IPB, ITB, P4S Training Centers
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|
Public–Private Quantum R&D Programs
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Joint research and prototyping with international tech firms
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No formal programs yet
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Initiate PPP schemes with IBM, D-Wave, or AWS Braket under national innovation platforms
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BRIN, Ministry of Industry, BKPM
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Sources : (OECD, 2022; World Economic Forum, 2023; IBM Research, 2023; D-Wave Systems, 2023; Indian Institute of Science, 2022; Ministry of Agriculture, 2023; BRIN, 2022).
Enhancing food safety standards by the integration of advanced technologies, including quantum computing, can improve food safety through real-time monitoring and predictive analytics, addressing the complexities of modern food supply chains (Ayeni & Olagoke-Komolafe, 2024). By ensuring traceability and transparency, quantum computing can help mitigate risks associated with foodborne illnesses (Ayeni & Olagoke-Komolafe, 2024).
Policy development and stakeholder collaboration of effective policy development are crucial for integrating technology into food production systems. Collaboration among various ministries is necessary to align goals and enhance the effectiveness of food sustainability policies (Dermoredjo et al., 2024). The Food Sustainability Index (FSI) and Global Food Security Index (GFSI) highlight the need for improved stakeholder coordination to achieve better food security outcomes (Dermoredjo et al., 2024). While the potential of quantum computing in revolutionizing food production is significant, challenges such as high implementation costs and the need for regulatory frameworks to support these technologies must be addressed to realize their full benefits (Ayeni & Olagoke-Komolafe, 2024; Dermoredjo et al., 2024).
CASE STUDIES
This section presents relevant global and national examples that illustrate the potential applications and readiness of quantum computing in agriculture. By analyzing these cases, we can draw parallels and extract strategic lessons for Indonesia’s context.
United States - IBM quantum and crop simulation
IBM has collaborated with agricultural scientists to simulate the molecular structures of nitrogen-fixing enzymes using quantum computing. Nitrogen fixation is a critical process in plant growth and sustainability. Quantum simulation helps identify alternatives to synthetic fertilizers, which can significantly reduce environmental degradation in large-scale agriculture (Cao et al., 2022). Quantum computing enables researchers to understand how certain proteins in legumes interact with soil nitrogen, allowing for the engineering of crops with higher nitrogen-use efficiency.
“The simulation of nitrogenase and its reaction pathways using quantum computers may someday allow for synthetic biology advancements in agriculture” (Cao et al., 2022, p. 1347).
IBM, a global leader in quantum computing, has developed algorithms to simulate chemical processes at the molecular level using quantum circuits. In agriculture, this capability is being used to improve soil nutrient models and nitrogen fixation simulations. Such simulations can lead to more effective and environmentally sustainable fertilizers. For instance, IBM’s quantum research team collaborates with agri-tech partners to understand how biological processes in plants interact with climate stressors. The aim is to breed climate-resilient crop varieties. While these efforts are still experimental, they demonstrate quantum computing’s role in molecular biology and bioinformatics for crop improvement. Key for Indonesia integrating quantum-powered bioinformatics into national seed breeding programs (e.g., Balitbangtan) could accelerate the development of locally adapted and climate-resilient crops.
Canada – D-Wave and agricultural supply chain optimization
D-Wave Systems has piloted the use of quantum annealing techniques in logistics optimization for perishable food products. In partnership with logistics companies, they developed quantum algorithms to minimize transport delays and reduce spoilage, especially for temperature-sensitive agricultural goods such as dairy and vegetables (Booth et al., 2023). This is particularly relevant for archipelagic nations like Indonesia, where food distribution is a major bottleneck.
“Quantum annealing reduced distribution time variance by 25% across complex delivery networks” (Booth et al., 2023, p. 81).
D-Wave, a Canadian quantum computing company, developed a quantum annealing platform to address optimization problems, including supply chain logistics. In one pilot, they simulated agricultural product routing from farms to markets, factoring in perishable inventory constraints, fluctuating fuel costs, and climate events. Impact: Simulation results showed up to 25% reductions in transportation costs and spoilage, with improved delivery efficiency. Key for Indonesia logistics models could revolutionize food distribution systems, especially in remote and rural provinces like Maluku, Papua, or NTT.
India quantum forecasting for crop yield and weather modeling
The Indian Institute of Science (IISc) implemented quantum-enhanced forecasting models to improve the accuracy of monsoon and drought predictions. These tools allowed for better seasonal crop planning, especially in drought-prone states like Maharashtra and Karnataka. Quantum algorithms helped simulate large-scale atmospheric dynamics to model rainfall patterns more precisely than classical models (Singh et al., 2022).
“Quantum Fourier transforms and QAOA were used to simulate low-pressure systems affecting the Indian monsoon, improving lead times by up to 7 days” (Singh et al., 2022, p. 245).
In India, pilot projects backed by the Indian Institute of Science and TCS Quantum Lab are exploring quantum-enhanced forecasting for rainfall and monsoon patterns, which are critical to farming calendars. These models use quantum-inspired algorithms to process large sets of meteorological data and enhance agro-climatic planning. A similar model can be applied to Indonesia’s unpredictable rainfall patterns and diverse agroecological zones. Quantum-enhanced weather modeling can help anticipate crop failure risks and guide early warning systems.
Indonesia’s Readiness and Emerging Quantum Agriculture Initiatives
Indonesia’s Smart Farming 4.0 initiative has introduced IoT, AI, and remote sensing in pilot regions of Central Java and West Sumatra. These technologies provide a base for future integration with quantum-enhanced models. The Palapa Ring national broadband project has improved internet accessibility, which is critical for supporting cloud-based quantum computing services in rural areas (Kominfo, 2021).
The National Research and Innovation Agency (BRIN) and IPB University have started preliminary discussions with global tech firms (e.g., IBM Indonesia and AWS Braket) to explore the potential of quantum computing. Although formal pilot projects have yet to launch, academic research in quantum mechanics and machine learning is progressing (BRIN, 2023).
Policy gaps and readiness assessment
Indonesia lacks a national roadmap for quantum computing integration in agriculture. However, quantum research is listed under “Emerging Technologies” in the Digital Indonesia Vision 2045, suggesting policymakers recognize its future importance (OECD, 2022).
The integration of quantum computing into Indonesia's agricultural policies aims to revolutionize sustainable food production, particularly in support of the food self-sufficiency program. This innovative approach leverages advanced computational capabilities to optimize agricultural practices, enhance productivity, and address challenges in food security. The following sections detail key aspects of this transformation.
Quantum Computing Applications in Agriculture Data Processing and Simulation: Quantum computing can analyze vast datasets for crop optimization and weather modeling, leading to improved decision-making in agriculture (Bansod et al., 2024). Real-World Case Studies: Implementations have shown success in crop yield prediction and pest management, demonstrating tangible benefits for farmers (Bansod et al., 2024).
Decision Support Systems for Soybean Self-Sufficiency Simulation Models: A decision support system (DSS) was developed for Central Java to enhance soybean self-sufficiency, allowing policymakers to simulate various scenarios and prioritize land expansion and productivity improvements (Hisjam et al., 2020). Farmer Welfare: The DSS aims to improve farmer welfare by reducing costs and increasing agricultural output, crucial for achieving self-sufficiency (Hisjam et al., 2020).
Technological Innovations in Rice Production to Enhance Rice Productivity: Innovative agricultural technologies are essential for increasing rice production, which is vital for Indonesia's food security goals by 2045 (Hibatullah et al., 2024). Policy Formulation: The integration of technology in rice cultivation can inform policy decisions, ensuring a sustainable food future (Hibatullah et al., 2024). While the potential of quantum computing and technology-based innovations is promising, challenges such as accessibility and the need for government support remain critical for successful implementation in Indonesia's agricultural landscape (Prihadyanti & Aziz, 2022).
Enhanced crop management precision agriculture: Quantum computing can facilitate precision farming by providing real-time data analysis for optimal planting, irrigation, and harvesting schedules, thus maximizing yield while minimizing resource use (Maraveas et al., 2024). Disease prediction: Quantum-enhanced machine learning models can predict crop diseases more accurately, allowing for timely interventions that reduce losses and improve food security (Maraveas et al., 2024).
Addressing food security challenges in rice production: given that rice is a staple in Indonesia, quantum technologies can enhance rice productivity through innovative agricultural practices, addressing the challenges posed by decreasing harvest areas (Hibatullah et al., 2024). Support for diverse crops with the integration of quantum computing can also extend to other crops, such as soybeans, improving overall agricultural efficiency and contributing to self-sufficiency goals (Harnowo et al., 2024). While the integration of quantum computing in agriculture presents numerous advantages, challenges such as technology accessibility and data management must be addressed to fully realize its potential in enhancing food production in Indonesia.
POLICY ANALYSIS AND RECOMMENDATIONS
Quantum Computing Applications in Agriculture: Precision agriculture: Quantum computing can analyze vast datasets to optimize planting schedules, irrigation, and crop management, leading to increased yields and reduced resource waste. Climate resilience: By modeling climate impacts on agriculture, quantum computing can help develop adaptive strategies for local food production, ensuring sustainability despite environmental changes (Trisia et al., 2016).
The government should allocate funds for research and development in quantum technologies tailored for agriculture, enhancing productivity and sustainability (Wihardja et al., 2023). Education and training: Implement training programs for farmers on utilizing quantum computing tools, fostering a tech-savvy agricultural workforce (Saliem et al., 2021). While the potential of quantum computing is promising, challenges such as high implementation costs and the need for robust infrastructure must be addressed. Additionally, the reliance on traditional agricultural practices may hinder the adoption of innovative technologies, necessitating a cultural shift towards embracing modern solutions (Prayuginingsih et al., 2024).
Table 4. The introduction of quantum computing into Indonesia’s agricultural policies
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Policy objective
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Potential quantum outcome
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Increase rice productivity.
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Quantum-assisted seed genomics and weather simulation
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|
Reduce post-harvest losses.
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Quantum-optimized logistics and storage systems
|
|
Promote climate-smart agriculture.
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Real-time quantum modeling of crop-climate interactions
|
|
Improve food distribution equity.
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Quantum supply chain route optimization in rural provinces
|
|
Build farmer resilience.
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Early warning systems powered by quantum-enhanced forecasts
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The integration of quantum computing into policy analysis and recommendations can significantly enhance the efficiency of Indonesia's Food Self-Sufficiency Program, particularly in rice production. By leveraging advanced computational capabilities, policymakers can better analyze complex agricultural data, optimize resource allocation, and simulate various policy scenarios to identify the most effective strategies for achieving self-sufficiency.
Enhanced Data Analysis: Quantum computing can process vast datasets rapidly, allowing for real-time analysis of agricultural trends and consumer demands. This capability can help identify critical factors affecting rice production, such as land conversion rates and irrigation efficiency, which are essential for formulating effective policies (Hibatullah et al., 2024; Mubarokah & Miftah, 2023).
Optimized Resource Allocation: By utilizing quantum algorithms, policymakers can optimize the distribution of resources, such as fertilizers and seeds, to maximize productivity across different regions (Prayuginingsih et al., 2024). This targeted approach can lead to increased yields and better management of agricultural land, addressing the challenges of reduced harvest areas (Hibatullah et al., 2024).
Quantum computing enables the simulation of multiple policy scenarios, allowing for the assessment of potential outcomes before implementation (Altemeier et al., 1991). This can help in understanding the impacts of various strategies, such as expanding irrigated areas or adjusting import tariffs, on achieving a Self-Sufficiency Ratio (SSR) of 97.8% in rice production (Prayuginingsih et al., 2024). Conversely, while quantum computing presents promising advancements, the initial investment and infrastructure required for its implementation may pose challenges for Indonesia, particularly in rural areas where agricultural practices are most critical. Balancing technological innovation with practical accessibility remains a key consideration for policymakers.
Integrating quantum computing into Indonesia's food self-sufficiency program presents numerous potential benefits, particularly in enhancing agricultural productivity and sustainability. Quantum computing's ability to process vast datasets and optimize resource allocation can significantly transform agricultural practices, addressing challenges such as crop yield prediction, pest management, and environmental monitoring. Quantum-enhanced machine learning models can predict crop diseases and optimize farming practices, allowing for precise interventions that minimize resource waste (Maraveas et al., 2024). The ability to simulate complex agricultural systems can help farmers make informed decisions, ultimately increasing yields while reducing environmental impact (Bansod et al., 2024).
Production Planning Efficiency: Quantum computing can revolutionize production planning by solving complex optimization problems more effectively than traditional methods, leading to cost reductions and improved supply chain management (Riandari et al., 2021). This capability is essential for Indonesia, where rice production must keep pace with a growing population and decreasing arable land (Hibatullah et al., 2024). Conversely, while the integration of quantum computing holds promises, challenges such as technology accessibility, data management, and energy consumption must be addressed to fully realize its benefits in Indonesia's agricultural sector.
CONCLUSION
Indonesia stands at a historic crossroads in the evolution of its agricultural policy and technological modernization. Confronted with complex challenges ranging from climate change, land degradation, and food insecurity to inefficient logistics, the nation urgently requires transformative solutions. Quantum computing, though still emerging globally, holds immense promise for revolutionizing sustainable food production systems in ways that classical computing simply cannot match. Yet the transition is not merely technologic but fundamentally policy-driven. Indonesia must address significant gaps, particularly in coordination, human capital, infrastructure, and R&D funding. Strategic policy recommendations, such as establishing a Quantum Agriculture Task Force, launching pilot projects in food estates, and embedding quantum computing into national curricula, can accelerate progress toward a quantum-powered agri future.
Moreover, Indonesia’s demographic dividend, rich biodiversity, and strategic location in Southeast Asia position it uniquely to lead the Global South in quantum agricultural innovation. By aligning with national development roadmaps like Indonesia Emas 2045, the Digital Economy Roadmap 2030, and the Sustainable Development Goals (SDGs), quantum policy integration can be institutionalized for long-term impact.
In conclusion, the adoption of quantum computing is not a distant ambition but a timely imperative. If Indonesia acts decisively, it can not only achieve food self-sufficiency but also emerge as a global model for quantum-driven, sustainable agriculture, ensuring food security, rural prosperity, and ecological balance for generations to come.
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The Introduction of Indonesia's Revolutionizing Policy on Sustainable Food Production with Quantum Computing to Support the Food Self-Sufficiency Programme
ABSTRACT
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, Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis 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.
Keywords: Agricultural policy, food self-sufficiency, Indonesia, precision agriculture, quantumcomputing, rural development, smart farming, sustainable food production, food security, technology integration.
INTRODUCTION
Indonesia, as the world’s fourth most populous country, faces increasing pressure to secure a stable and sustainable food supply for its citizens. The government has long promoted food self-sufficiency as a national priority, particularly in staple commodities such as rice, corn, and soybeans. However, achieving this goal has been challenged by rapid urbanization, climate variability, degraded natural resources, and inefficiencies in agricultural production and logistics (Siregar & Rachman, 2021). In response, there is a growing policy shift toward integrating advanced technologies to optimize food systems. Among these innovations, quantum computing emerges as a transformative frontier with the potential to revolutionize sustainable food production and strengthen Indonesia's food sovereignty.
Quantum computing, which leverages the principles of quantum mechanics to process information in fundamentally new ways, can solve highly complex optimization and simulation problems that are intractable for classical computers (Preskill, 2018). This capability is particularly relevant for agriculture, where variables such as climate models, soil conditions, crop genetics, pest outbreaks, and supply chain logistics require simultaneous consideration across dynamic environments. In the Indonesian context, quantum computing could be utilized to enhance precision agriculture, simulate climate-resilient crop properties, and optimize large-scale distribution networks—ultimately supporting the national objective of food self-sufficiency.
Recent global studies suggest that quantum computing could shorten the time needed for plant genome analysis, identify efficient fertilizer use, and forecast market demand with greater accuracy (Madsen et al., 2022). If adopted through inclusive and well-structured policies, Indonesia could leapfrog traditional development stages and create a quantum-empowered agricultural ecosystem, enabling sustainable food production that is both technologically advanced and environmentally responsible.
Furthermore, Indonesia’s digital transformation blueprint (RPJMN 2020–2024) and the Ministry of Agriculture’s vision for Smart Agriculture 4.0 provide fertile ground for integrating quantum technologies into national agricultural policy. Strategic collaboration with academic institutions, quantum computing firms, and international organizations is necessary to develop technical capabilities and regulatory frameworks (Badan Pusat Statistik, 2023; Ministry of National Development Planning, 2020). By embedding quantum computing within its agricultural modernization agenda, Indonesia can address food security risks while accelerating its transition to a low-carbon, knowledge-based economy.
This paper explores the intersection of quantum computing and Indonesian food policy reform, evaluating how this collaboration could strengthen the resilience and productivity of Indonesia’s food systems. It aims to provide policymakers and researchers with a conceptual foundation and practical roadmap for leveraging quantum technologies to fulfil the country's vision of a self-sufficient, sustainable, and technologically integrated agricultural future.
METHODOLOGY
Research design
This study adopts an in-depth literature review and a qualitative exploratory research design with a policy analysis 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 rests on three main theoretical supports: (1) Food Security Theory, where food security is defined as the condition where all people have physical and economic access to sufficient, safe, and nutritious food (FAO, 2021). The research applies this lens to analyze how quantum computing can support the four dimensions of food security: availability, access, utilization, and stability; (2) Innovation and Technology Adoption Theory—Based on Rogers’ Diffusion of Innovations (Roger, 2003 in Yadav et al., 2022), the study investigates the barriers and drivers of adopting quantum computing in agricultural contexts, especially among policymakers and institutions; and (3) Sustainability Transitions Theory—This theory (Geels, 2019) explains how socio-technical innovations disrupt and replace existing practices. It is applied to examine how quantum computing can accelerate Indonesia’s transition from resource-intensive farming models toward sustainable food production.
The main objectives of this methodological approach are to identify how quantum computing capabilities align with Indonesia’s agricultural policy goals, to assess the national readiness for adopting quantum solutions in the agricultural sector, and to propose a strategic roadmap for integrating quantum technology into sustainable food production policies.
This method is suitable for understanding complex and multidisciplinary intersections between emerging technologies and national development strategies, especially when empirical implementations are limited or still in planning phases.
Data sources
The study relies primarily on secondary data sources collected from a range of relevant and credible materials, including government policy documents: National Medium-Term Development Plan (RPJMN 2020–2024), Food Estate Program policy notes, Smart Agriculture 4.0 roadmap from the Ministry of Agriculture, and reports from Bappenas (Ministry of National Development Planning). Academic literature and journals: Peer-reviewed articles related to food security, quantum computing, precision agriculture, and digital transformation in developing countries. Industry and technology reports: White papers and technical publications from quantum computing companies (e.g., IBM, D-Wave, Google Quantum AI) and global institutions such as FAO and OECD on Agri-tech innovation. Media and press releases: Announcements, speeches, and progress updates from the Ministry of Agriculture and Ministry of Communication and Information Technology regarding digital transformation and quantum-related partnerships. If available, expert interviews and government roundtable discussions may be analysed in future iterations of this study to validate the findings and incorporate stakeholder perspectives.
Analytical framework
This research applies a step of technical framework to analyze the potential integration of quantum computing into Indonesia’s food self-sufficiency policy: policy gap analysis identifies weaknesses in Indonesia’s current sustainable food production strategies. Technology–Policy Integration Matrix, this matrix evaluates the match between quantum computing capabilities and policy needs across four domains: (1) Food Production: enhancing productivity through quantum-enhanced simulation and genome analysis; (2) Supply Chain Management: optimizing logistics, reducing food waste, and improving distribution efficiency; (3) Environmental Sustainability: modeling resource usage, soil health, and emissions under complex climate variables; and (4) Governance and Planning: supporting policy simulation and predictive modeling for long-term planning. Each domain is assessed based on relevance to national goals, technological feasibility, infrastructure readiness, and policy alignment.
RESULT AND DISCUSSION
Policy gap analysis
Indonesia's sustainable food production strategies face several key policy gaps that hinder the achievement of food self-sufficiency, particularly in rice production. These gaps stem from a lack of comprehensive approaches that integrate environmental sustainability, local food systems, and effective agricultural practices. The following sections outline the critical areas where policy improvements are necessary.
Overreliance on monocropping: Current policies heavily favor rice production, leading to low dietary diversity and neglect of local food systems (Nurhasan et al., 2021). The past Food Estate Programs (FEP) have shown that focusing on monocropping can damage the environment and overlook local agricultural capacities (Nurhasan et al., 2021). Ineffective agricultural support: Existing agricultural supports are often ineffective, failing to enhance productivity or farmer incomes (Wihardja et al., 2023). There is a need for policies that redirect subsidies to directly support farmers and promote sustainable practices (Wihardja et al., 2023). A lack of agricultural extension services and climate information hampers farmers' ability to adapt to changing conditions (Wihardja et al., 2023). Strengthening these services is crucial for improving productivity and resilience in food systems (Wihardja et al., 2023). Environmental considerations: Policies must address the environmental impacts of agriculture, including greenhouse gas emissions from land-use changes and deforestation (Wihardja et al., 2023). Implementing agroecological practices can enhance soil health and reduce reliance on chemical fertilizers (Susanti et al., 2024).
Table 1. Indonesia’s sustainable food production strategies
Policy area
Current weakness / gap
Evidence (Govt. data / reports)
Relevance to quantum computing integration
Productivity & yield stability
Rice and maize yields stagnated in the last decade despite policy support.
BPS (2022) reports rice productivity averages 5.1 ton/ha with slow growth; Ministry of Agriculture (2021) highlights reliance on traditional practices.
Quantum computing can optimize crop models, predict yield outcomes under multiple scenarios, and guide precision interventions.
Climate resilience
Vulnerability to droughts and floods; limited adoption of climate-smart agriculture.
Indonesia Climate Risk Profile (World Bank, 2021); Ministry of Agriculture (2020) notes climate hazards reduced harvest areas by 10–15%.
Quantum models simulate climate–crop interactions at scale, supporting adaptive cropping and water management policies.
Food distribution & logistics
Post-harvest losses remain high (10–12% for rice). Cold chain systems underdeveloped.
BPS (2021) shows food loss at ~11%; FAO (2020) confirms supply chain inefficiency.
Quantum optimization applied to logistics networks reduces losses, improves cold-chain routing, and ensures food stability.
Technology adoption
Digital agriculture adoption remains low among smallholders; fragmented policy implementation.
Ministry of Agriculture (2021) reports <15% of smallholders using digital tools.
Quantum computing can integrate large datasets (soil, climate, market) to provide real-time decision-making support.
Food security governance
Policies are fragmented across ministries; weak coordination in food security programs.
OECD (2022) Food and Agriculture Review: Indonesia; National Food Agency report (2022).
Quantum-based policy simulations can help identify optimal policy mixes and strengthen inter-ministerial coordination.
Sources: Badan Pusat Statistik (2022); Food and Agriculture Organization (2020); Ministry of Agriculture (2021); OECD (2022); World Bank (2021).
In contrast, some argue that focusing solely on increasing rice production may overlook the broader context of food security, which includes diverse dietary needs and environmental sustainability. A balanced approach that integrates these aspects is essential for long-term food self-sufficiency in Indonesia.
The analysis of Indonesia's current sustainable food production strategies reveals several weaknesses that could hinder the successful implementation of the proposed revolutionizing policy on sustainable food production with quantum computing. These weaknesses stem from systemic issues in agricultural practices, policy coherence, and resource management.
Ineffective agricultural practices by monocropping focus policies emphasize high-value commodities, particularly rice, which limits dietary diversity and environmental sustainability (Nurhasan et al., 2021). Small-scale farmer dominance complicates the transition to sustainable practices, as they often lack access to modern technologies and resources (Suryana, 2014).
Policy coherence and integration of stakeholders have significant overlap among various ministries involved in policy coherence and integration of stakeholders, leading to inefficiencies in policy implementation and a lack of unified direction in food security efforts (Dermoredjo et al., 2024). Existing agricultural support mechanisms are ineffective, failing to provide necessary resources and information to farmers (Wihardja et al., 2023.). Food Quality Issues despite sufficient food supply, the nutritional quality of food remains below recommended standards, indicating a need for policy adjustments to enhance food quality alongside quantity (Suryana, 2014).
Technology–policy integration matrix
The integration of quantum computing into Indonesia's policy framework for sustainable food production represents a transformative approach to achieving food self-sufficiency. This innovative technology can enhance agricultural productivity, optimize resource management, and improve food safety standards, thereby supporting the government's food self-sufficiency program. Agricultural productivity by quantum computing can analyze vast datasets to optimize crop yields and resource allocation, leading to more efficient farming practices. It enables predictive modeling for climate impacts on agriculture, allowing farmers to adapt to changing conditions effectively (Herawati et al., 2023).
Table 2. Technology - policy integration matrix
Quantum computing application
Agricultural use case
Current policy status (Indonesia)
Integration strategy
Responsible institutions
Quantum simulation of crop genomics
Simulating plant responses to climate stress and nutrient optimization
Not addressed in Smart Farming 4.0 or RIRN
Include in BRIN’s research priorities and the Ministry of Agriculture’s R&D programs
BRIN, Ministry of Agriculture, LIPI, IPB
Quantum forecasting algorithms
Early warning systems for drought, pest outbreaks, and yield forecasting
Covered partially under BMKG and BPPT weather tools, but not quantum-enhanced
Pilot quantum-enhanced models in collaboration with BMKG and international partners
BMKG, BRIN, Ministry of Agriculture
Quantum supply chain optimization
Minimizing food spoilage and logistics cost in archipelagic regions
Logistics optimization is addressed in Food Security Roadmap, not quantum-based
Embed quantum route optimization in the Ministries of Trade and Agriculture's food logistics plans
Ministry of Trade, Ministry of Agriculture, Kominfo, National Logistics Agency
Quantum Resource Allocation Models
Efficient irrigation, land use planning, and fertilizer allocation
Smart Farming 4.0 uses basic AI-based models
Upgrade digital farming models with quantum-enhanced optimization tools
Ministry of Agriculture, Ministry of Environment and Forestry
Quantum-Enhanced Climate Modeling
Predictive modeling of long-term climate impacts on staple crops
Partially supported via BRIN’s climate research and international climate collaborations
Expand existing models to test scenarios using quantum algorithms
BRIN, Ministry of Environment and Forestry, BMKG
Quantum Secure Data Systems
Secure farm and land data transmission in e-farming systems
Cybersecurity is present in the Digital Economy Framework.
Adopt quantum cryptography standards in agricultural IoT systems
Kominfo, BSSN, Ministry of Agriculture
Quantum Education and Capacity Building
Upskilling agricultural researchers and tech developers in quantum tech
Not yet developed in agricultural curriculum
Create academic and vocational modules in collaboration with universities
BRIN, LPDP, IPB, ITB, P4S Training Centers
Public–Private Quantum R&D Programs
Joint research and prototyping with international tech firms
No formal programs yet
Initiate PPP schemes with IBM, D-Wave, or AWS Braket under national innovation platforms
BRIN, Ministry of Industry, BKPM
Sources : (OECD, 2022; World Economic Forum, 2023; IBM Research, 2023; D-Wave Systems, 2023; Indian Institute of Science, 2022; Ministry of Agriculture, 2023; BRIN, 2022).
Enhancing food safety standards by the integration of advanced technologies, including quantum computing, can improve food safety through real-time monitoring and predictive analytics, addressing the complexities of modern food supply chains (Ayeni & Olagoke-Komolafe, 2024). By ensuring traceability and transparency, quantum computing can help mitigate risks associated with foodborne illnesses (Ayeni & Olagoke-Komolafe, 2024).
Policy development and stakeholder collaboration of effective policy development are crucial for integrating technology into food production systems. Collaboration among various ministries is necessary to align goals and enhance the effectiveness of food sustainability policies (Dermoredjo et al., 2024). The Food Sustainability Index (FSI) and Global Food Security Index (GFSI) highlight the need for improved stakeholder coordination to achieve better food security outcomes (Dermoredjo et al., 2024). While the potential of quantum computing in revolutionizing food production is significant, challenges such as high implementation costs and the need for regulatory frameworks to support these technologies must be addressed to realize their full benefits (Ayeni & Olagoke-Komolafe, 2024; Dermoredjo et al., 2024).
CASE STUDIES
This section presents relevant global and national examples that illustrate the potential applications and readiness of quantum computing in agriculture. By analyzing these cases, we can draw parallels and extract strategic lessons for Indonesia’s context.
United States - IBM quantum and crop simulation
IBM has collaborated with agricultural scientists to simulate the molecular structures of nitrogen-fixing enzymes using quantum computing. Nitrogen fixation is a critical process in plant growth and sustainability. Quantum simulation helps identify alternatives to synthetic fertilizers, which can significantly reduce environmental degradation in large-scale agriculture (Cao et al., 2022). Quantum computing enables researchers to understand how certain proteins in legumes interact with soil nitrogen, allowing for the engineering of crops with higher nitrogen-use efficiency.
“The simulation of nitrogenase and its reaction pathways using quantum computers may someday allow for synthetic biology advancements in agriculture” (Cao et al., 2022, p. 1347).
IBM, a global leader in quantum computing, has developed algorithms to simulate chemical processes at the molecular level using quantum circuits. In agriculture, this capability is being used to improve soil nutrient models and nitrogen fixation simulations. Such simulations can lead to more effective and environmentally sustainable fertilizers. For instance, IBM’s quantum research team collaborates with agri-tech partners to understand how biological processes in plants interact with climate stressors. The aim is to breed climate-resilient crop varieties. While these efforts are still experimental, they demonstrate quantum computing’s role in molecular biology and bioinformatics for crop improvement. Key for Indonesia integrating quantum-powered bioinformatics into national seed breeding programs (e.g., Balitbangtan) could accelerate the development of locally adapted and climate-resilient crops.
Canada – D-Wave and agricultural supply chain optimization
D-Wave Systems has piloted the use of quantum annealing techniques in logistics optimization for perishable food products. In partnership with logistics companies, they developed quantum algorithms to minimize transport delays and reduce spoilage, especially for temperature-sensitive agricultural goods such as dairy and vegetables (Booth et al., 2023). This is particularly relevant for archipelagic nations like Indonesia, where food distribution is a major bottleneck.
“Quantum annealing reduced distribution time variance by 25% across complex delivery networks” (Booth et al., 2023, p. 81).
D-Wave, a Canadian quantum computing company, developed a quantum annealing platform to address optimization problems, including supply chain logistics. In one pilot, they simulated agricultural product routing from farms to markets, factoring in perishable inventory constraints, fluctuating fuel costs, and climate events. Impact: Simulation results showed up to 25% reductions in transportation costs and spoilage, with improved delivery efficiency. Key for Indonesia logistics models could revolutionize food distribution systems, especially in remote and rural provinces like Maluku, Papua, or NTT.
India quantum forecasting for crop yield and weather modeling
The Indian Institute of Science (IISc) implemented quantum-enhanced forecasting models to improve the accuracy of monsoon and drought predictions. These tools allowed for better seasonal crop planning, especially in drought-prone states like Maharashtra and Karnataka. Quantum algorithms helped simulate large-scale atmospheric dynamics to model rainfall patterns more precisely than classical models (Singh et al., 2022).
“Quantum Fourier transforms and QAOA were used to simulate low-pressure systems affecting the Indian monsoon, improving lead times by up to 7 days” (Singh et al., 2022, p. 245).
In India, pilot projects backed by the Indian Institute of Science and TCS Quantum Lab are exploring quantum-enhanced forecasting for rainfall and monsoon patterns, which are critical to farming calendars. These models use quantum-inspired algorithms to process large sets of meteorological data and enhance agro-climatic planning. A similar model can be applied to Indonesia’s unpredictable rainfall patterns and diverse agroecological zones. Quantum-enhanced weather modeling can help anticipate crop failure risks and guide early warning systems.
Indonesia’s Readiness and Emerging Quantum Agriculture Initiatives
Indonesia’s Smart Farming 4.0 initiative has introduced IoT, AI, and remote sensing in pilot regions of Central Java and West Sumatra. These technologies provide a base for future integration with quantum-enhanced models. The Palapa Ring national broadband project has improved internet accessibility, which is critical for supporting cloud-based quantum computing services in rural areas (Kominfo, 2021).
The National Research and Innovation Agency (BRIN) and IPB University have started preliminary discussions with global tech firms (e.g., IBM Indonesia and AWS Braket) to explore the potential of quantum computing. Although formal pilot projects have yet to launch, academic research in quantum mechanics and machine learning is progressing (BRIN, 2023).
Policy gaps and readiness assessment
Indonesia lacks a national roadmap for quantum computing integration in agriculture. However, quantum research is listed under “Emerging Technologies” in the Digital Indonesia Vision 2045, suggesting policymakers recognize its future importance (OECD, 2022).
The integration of quantum computing into Indonesia's agricultural policies aims to revolutionize sustainable food production, particularly in support of the food self-sufficiency program. This innovative approach leverages advanced computational capabilities to optimize agricultural practices, enhance productivity, and address challenges in food security. The following sections detail key aspects of this transformation.
Quantum Computing Applications in Agriculture Data Processing and Simulation: Quantum computing can analyze vast datasets for crop optimization and weather modeling, leading to improved decision-making in agriculture (Bansod et al., 2024). Real-World Case Studies: Implementations have shown success in crop yield prediction and pest management, demonstrating tangible benefits for farmers (Bansod et al., 2024).
Decision Support Systems for Soybean Self-Sufficiency Simulation Models: A decision support system (DSS) was developed for Central Java to enhance soybean self-sufficiency, allowing policymakers to simulate various scenarios and prioritize land expansion and productivity improvements (Hisjam et al., 2020). Farmer Welfare: The DSS aims to improve farmer welfare by reducing costs and increasing agricultural output, crucial for achieving self-sufficiency (Hisjam et al., 2020).
Technological Innovations in Rice Production to Enhance Rice Productivity: Innovative agricultural technologies are essential for increasing rice production, which is vital for Indonesia's food security goals by 2045 (Hibatullah et al., 2024). Policy Formulation: The integration of technology in rice cultivation can inform policy decisions, ensuring a sustainable food future (Hibatullah et al., 2024). While the potential of quantum computing and technology-based innovations is promising, challenges such as accessibility and the need for government support remain critical for successful implementation in Indonesia's agricultural landscape (Prihadyanti & Aziz, 2022).
Enhanced crop management precision agriculture: Quantum computing can facilitate precision farming by providing real-time data analysis for optimal planting, irrigation, and harvesting schedules, thus maximizing yield while minimizing resource use (Maraveas et al., 2024). Disease prediction: Quantum-enhanced machine learning models can predict crop diseases more accurately, allowing for timely interventions that reduce losses and improve food security (Maraveas et al., 2024).
Addressing food security challenges in rice production: given that rice is a staple in Indonesia, quantum technologies can enhance rice productivity through innovative agricultural practices, addressing the challenges posed by decreasing harvest areas (Hibatullah et al., 2024). Support for diverse crops with the integration of quantum computing can also extend to other crops, such as soybeans, improving overall agricultural efficiency and contributing to self-sufficiency goals (Harnowo et al., 2024). While the integration of quantum computing in agriculture presents numerous advantages, challenges such as technology accessibility and data management must be addressed to fully realize its potential in enhancing food production in Indonesia.
POLICY ANALYSIS AND RECOMMENDATIONS
Quantum Computing Applications in Agriculture: Precision agriculture: Quantum computing can analyze vast datasets to optimize planting schedules, irrigation, and crop management, leading to increased yields and reduced resource waste. Climate resilience: By modeling climate impacts on agriculture, quantum computing can help develop adaptive strategies for local food production, ensuring sustainability despite environmental changes (Trisia et al., 2016).
The government should allocate funds for research and development in quantum technologies tailored for agriculture, enhancing productivity and sustainability (Wihardja et al., 2023). Education and training: Implement training programs for farmers on utilizing quantum computing tools, fostering a tech-savvy agricultural workforce (Saliem et al., 2021). While the potential of quantum computing is promising, challenges such as high implementation costs and the need for robust infrastructure must be addressed. Additionally, the reliance on traditional agricultural practices may hinder the adoption of innovative technologies, necessitating a cultural shift towards embracing modern solutions (Prayuginingsih et al., 2024).
Table 4. The introduction of quantum computing into Indonesia’s agricultural policies
Policy objective
Potential quantum outcome
Increase rice productivity.
Quantum-assisted seed genomics and weather simulation
Reduce post-harvest losses.
Quantum-optimized logistics and storage systems
Promote climate-smart agriculture.
Real-time quantum modeling of crop-climate interactions
Improve food distribution equity.
Quantum supply chain route optimization in rural provinces
Build farmer resilience.
Early warning systems powered by quantum-enhanced forecasts
The integration of quantum computing into policy analysis and recommendations can significantly enhance the efficiency of Indonesia's Food Self-Sufficiency Program, particularly in rice production. By leveraging advanced computational capabilities, policymakers can better analyze complex agricultural data, optimize resource allocation, and simulate various policy scenarios to identify the most effective strategies for achieving self-sufficiency.
Enhanced Data Analysis: Quantum computing can process vast datasets rapidly, allowing for real-time analysis of agricultural trends and consumer demands. This capability can help identify critical factors affecting rice production, such as land conversion rates and irrigation efficiency, which are essential for formulating effective policies (Hibatullah et al., 2024; Mubarokah & Miftah, 2023).
Optimized Resource Allocation: By utilizing quantum algorithms, policymakers can optimize the distribution of resources, such as fertilizers and seeds, to maximize productivity across different regions (Prayuginingsih et al., 2024). This targeted approach can lead to increased yields and better management of agricultural land, addressing the challenges of reduced harvest areas (Hibatullah et al., 2024).
Quantum computing enables the simulation of multiple policy scenarios, allowing for the assessment of potential outcomes before implementation (Altemeier et al., 1991). This can help in understanding the impacts of various strategies, such as expanding irrigated areas or adjusting import tariffs, on achieving a Self-Sufficiency Ratio (SSR) of 97.8% in rice production (Prayuginingsih et al., 2024). Conversely, while quantum computing presents promising advancements, the initial investment and infrastructure required for its implementation may pose challenges for Indonesia, particularly in rural areas where agricultural practices are most critical. Balancing technological innovation with practical accessibility remains a key consideration for policymakers.
Integrating quantum computing into Indonesia's food self-sufficiency program presents numerous potential benefits, particularly in enhancing agricultural productivity and sustainability. Quantum computing's ability to process vast datasets and optimize resource allocation can significantly transform agricultural practices, addressing challenges such as crop yield prediction, pest management, and environmental monitoring. Quantum-enhanced machine learning models can predict crop diseases and optimize farming practices, allowing for precise interventions that minimize resource waste (Maraveas et al., 2024). The ability to simulate complex agricultural systems can help farmers make informed decisions, ultimately increasing yields while reducing environmental impact (Bansod et al., 2024).
Production Planning Efficiency: Quantum computing can revolutionize production planning by solving complex optimization problems more effectively than traditional methods, leading to cost reductions and improved supply chain management (Riandari et al., 2021). This capability is essential for Indonesia, where rice production must keep pace with a growing population and decreasing arable land (Hibatullah et al., 2024). Conversely, while the integration of quantum computing holds promises, challenges such as technology accessibility, data management, and energy consumption must be addressed to fully realize its benefits in Indonesia's agricultural sector.
CONCLUSION
Indonesia stands at a historic crossroads in the evolution of its agricultural policy and technological modernization. Confronted with complex challenges ranging from climate change, land degradation, and food insecurity to inefficient logistics, the nation urgently requires transformative solutions. Quantum computing, though still emerging globally, holds immense promise for revolutionizing sustainable food production systems in ways that classical computing simply cannot match. Yet the transition is not merely technologic but fundamentally policy-driven. Indonesia must address significant gaps, particularly in coordination, human capital, infrastructure, and R&D funding. Strategic policy recommendations, such as establishing a Quantum Agriculture Task Force, launching pilot projects in food estates, and embedding quantum computing into national curricula, can accelerate progress toward a quantum-powered agri future.
Moreover, Indonesia’s demographic dividend, rich biodiversity, and strategic location in Southeast Asia position it uniquely to lead the Global South in quantum agricultural innovation. By aligning with national development roadmaps like Indonesia Emas 2045, the Digital Economy Roadmap 2030, and the Sustainable Development Goals (SDGs), quantum policy integration can be institutionalized for long-term impact.
In conclusion, the adoption of quantum computing is not a distant ambition but a timely imperative. If Indonesia acts decisively, it can not only achieve food self-sufficiency but also emerge as a global model for quantum-driven, sustainable agriculture, ensuring food security, rural prosperity, and ecological balance for generations to come.
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