As quantum computing progresses from theory to practical systems, its environmental and economic sustainability remains underexplored.This paper analyzes both the carbon footprint and cost implications of quantum computing technologies, with attention to their alignment with global sustainability goals.Key contributors to energy consumption, emissions, and operational costs are identified across the quantum computing lifecycle: hardware manufacturing, cryogenic cooling, runtime power demands, and quantum circuit simulation.The study employs a qualitative methodology, integrating a comprehensive review of scientific literature, environmental assessments, cost analysis reports, and benchmarking frameworks.It further presents a comparative analysis of quantum and classical high-performance computing (HPC), evaluating energy efficiency, environmental impact, and cost-effectiveness across realistic scenarios.By addressing both sustainability and economic dimensions, this research provides new insights for developers and policymakers, supporting the advancement of greener and more costeffective quantum technologies.
As quantum computing progresses from theory to practical systems, its environmental and economic sustainability remains underexplored.This paper analyzes both the carbon footprint and cost implications of quantum computing technologies, with attention to their alignment with global sustainability goals.Key contributors to energy consumption, emissions, and operational costs are identified across the quantum computing lifecycle: hardware manufacturing, cryogenic cooling, runtime power demands, and quantum circuit simulation.The study employs a qualitative methodology, integrating a comprehensive review of scientific literature, environmental assessments, cost analysis reports, and benchmarking frameworks.It further presents a comparative analysis of quantum and classical high-performance computing (HPC), evaluating energy efficiency, environmental impact, and cost-effectiveness across realistic scenarios.By addressing both sustainability and economic dimensions, this research provides new insights for developers and policymakers, supporting the advancement of greener and more costeffective quantum technologies.
While researchers in both industry and academia are racing to build Quantum Computing (QC) platforms with viable performance and functionality, the environmental impacts of this endeavor, such as its carbon footprint, e-waste generation, mineral use, and water and energy consumption, remain largely unknown. A similar oversight occurred during the semiconductor revolution and continues to have disastrous consequences for the health of our planet. As we build the quantum computing stack from the ground up, it is crucial to comprehensively assess it through an environmental sustainability lens for its entire life-cycle: production, use, and disposal. In this paper, we highlight the need and challenges in establishing a QC sustainability benchmark that enables researchers to make informed architectural design decisions and celebrate the potential quantum environmental advantage. We propose a carbon-aware quantum computing (CQC) framework that provides the foundational methodology and open research questions for calculating the total life-cycle carbon footprint of a QC platform. Our call to action to the research community is the establishment of a new research direction known as, sustainable quantum computing that promotes both quantum computing for sustainability-oriented applications and the sustainability of quantum computing.
The rapid advancements in quantum computing hold transformative potential for addressing critical global challenges and achieving sustainable development. This paper presents a conceptual framework that aligns quantum computing innovations with the United Nations Sustainable Development Goals (SDGs). It explores key areas such as renewable energy optimization, smart city infrastructure, and climate change mitigation. The study also emphasizes sustainability challenges, including environmental impacts, ethical concerns, and socio-economic considerations like workforce transitions and digital equity. A novel Carbon-aware Quantum Computing model is introduced to minimize the environmental footprint of quantum systems. Finally, the paper proposes a roadmap for ethical and scalable adoption of quantum technologies, ensuring inclusive and resilient societal growth.
While researchers in both industry and academia are racing to build quantum computing (QC) platforms with viable performance and functionality, the environmental impacts of this endeavor, such as its carbon footprint, e-waste generation, mineral use, and water and energy consumption, remain largely unknown. A similar oversight occurred during the semiconductor revolution and continues to have disastrous consequences for the health of our planet. As we build the quantum computing stack from the ground up, it is crucial to comprehensively assess it through an environmental sustainability lens for its entire lifecycle: production, use, and disposal. In this article, we highlight the need and challenges in establishing a QC sustainability benchmark that enables researchers to make informed architectural design decisions and celebrate the potential “quantum climate advantage.” We propose a carbon-aware quantum computing (CQC) framework that provides the foundational methodology and open research questions in calculating the total life-cycle carbon footprint of a QC platform. Our call to action to the research community is the establishment of a new research direction known as sustainable quantum computing (SQC) that promotes quantum computing for both sustainability-oriented applications and the sustainability of quantum computing itself.
Quantum computing (QC) offers a pathway to solving certain problems that classical computers struggle with, such as advanced drug discovery, secure cryptography, and the design of new materials. While these capabilities are promising, the field also raises serious sustainability concerns. Running quantum processors demands significant amounts of energy, rare elements, and specialized infrastructure, often without clear guidelines for environmentally responsible design. This paper reviews the environmental impact of QC by examining inefficiencies in energy use, cooling, and hardware manufacturing. The findings indicate that integrating renewable energy sources, adopting energy-efficient quantum hardware designs, and developing optimized, low-power quantum algorithms are the most effective strategies for reducing QC’s environmental footprint. In addition, a set of potential sustainability metrics is proposed to guide future research and implementation. The discussion points to one clear message: if sustainability is not embedded into quantum technology now, we risk repeating the environmental mistakes made in past computing revolutions.
The rapid growth of data-intensive applications and cloud computing has significantly increased the energy consumption of modern computing infrastructures, particularly data centers, raising critical sustainability and environmental concerns. Green computing seeks to address these challenges by improving energy efficiency and reducing carbon footprints without sacrificing performance. Quantum computing has emerged as a promising paradigm due to its ability to exploit quantum mechanical principles such as superposition and entanglement to achieve computational speedups beyond classical limits. This paper examines the role of quantum computing in advancing green computing technologies by analyzing its algorithmic capabilities, application domains, and hardware-related energy implications. Key quantum algorithms, including Shor’s algorithm, Grover’s search, the Quantum Approximate Optimization Algorithm (QAOA), and quantum simulation methods, are reviewed with respect to their potential to reduce execution time and energy consumption for computationally intensive tasks such as cryptography, optimization, and system modeling. The study further explores sustainability-oriented applications, including energy systems optimization, materials discovery for renewable technologies, and quantum machine learning for resource forecasting and management. Comparative analysis indicates that quantum approaches may offer substantial energy-efficiency advantages over classical methods as hardware matures. However, significant challenges remain, including qubit decoherence, error correction overhead, cryogenic cooling requirements, and scalability limitations. The paper concludes that continued advances in hybrid quantum–classical architectures and fault-tolerant hardware are essential for realizing quantum computing’s potential contribution to sustainable and energy-efficient computing.
The greenhouse gas (GHG) emissions issue that is directly related to the 13th Sustainable Development Goals ; Climate Action has gained attention on a global scale, prompting the utilization of all available technological advancements, including quantum computing . This systematic literature review, employing Kitchenham’s method, explores the realm of quantum computing and its application to the pressing issue of GHG emissions. Through a meticulous analysis of scholarly articles, we identify key trends, influential authors, core sources, and relevant affiliations within this research domain. Notably, our findings underscore a robust connection between quantum computing studies and the fields of machine learning and optimization, where various optimization tasks attempt to minimize GHG emissions, predominantly in the Energy and Logistics problem domain using Quantum-inspired Evolutionary Algorithm , Quantum-inspired Swarm Optimization, or Quantum Annealing. An insightful map reveals the emergence of diverse quantum computing implementations for varied tasks, across various domains, providing nuanced perspectives and identifying potential research directions, particularly in optimization and prediction tasks. This study offers a foundational understanding of trends, challenges, and opportunities associated with quantum computing implementation in addressing GHG emissions, contributing to the ongoing establishment of sustainable technology.