Quantum computing's environmental footprint remains poorly understood relative to classical infrastructure, and as quantum computing moves toward cloud delivery, Quantum Cloud Service (QCS) providers lack actionable guidance beyond platform-level carbon-accounting frameworks. Objective: This study extends the carbon-aware quantum computing (CQC) framework from a platform-level to a service-level model that translates empirical life cycle assessment (LCA) findings of a superconducting quantum computer into guidance for QCS providers. Method: We modeled the CQC framework via service-level embodied-carbon allocation, load-independent and load-proportional operational decomposition, and a workload-resolved application offset on the basis of results acquired through a cradle-to-grave LCA of a superconducting quantum platform. Results: The five-year footprint is 583 t CO2e (GKP) and 10,570 t (surface-code), dominated by embodied carbon (77.3-85.2%), with operational-embodied parity not reached until 17.0-28.7 years versus 2.7 years for classical comparators. This reorders provider levers: utilisation yields the largest gain (19.7x), followed by service life extension (59.9%) and electricity supply (6.5x), while operational efficiency and renewable procurement offer limited leverage. Conclusion: Superconducting quantum computers are structurally embodied-carbon-dominated, inverting classical sustainability intuition and motivating direct power measurement and cross-architecture validation as quantum infrastructure scales.
Quantum computing's environmental footprint remains poorly understood relative to classical infrastructure, and as quantum computing moves toward cloud delivery, Quantum Cloud Service (QCS) providers lack actionable guidance beyond platform-level carbon-accounting frameworks. Objective: This study extends the carbon-aware quantum computing (CQC) framework from a platform-level to a service-level model that translates empirical life cycle assessment (LCA) findings of a superconducting quantum computer into guidance for QCS providers. Method: We modeled the CQC framework via service-level embodied-carbon allocation, load-independent and load-proportional operational decomposition, and a workload-resolved application offset on the basis of results acquired through a cradle-to-grave LCA of a superconducting quantum platform. Results: The five-year footprint is 583 t CO2e (GKP) and 10,570 t (surface-code), dominated by embodied carbon (77.3-85.2%), with operational-embodied parity not reached until 17.0-28.7 years versus 2.7 years for classical comparators. This reorders provider levers: utilisation yields the largest gain (19.7x), followed by service life extension (59.9%) and electricity supply (6.5x), while operational efficiency and renewable procurement offer limited leverage. Conclusion: Superconducting quantum computers are structurally embodied-carbon-dominated, inverting classical sustainability intuition and motivating direct power measurement and cross-architecture validation as quantum infrastructure scales.
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.
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.
The urgency to decarbonise digital infrastructure motivates the search for lower‑footprint computational methods in management analytics. This article evaluates, from sustainability and economic‑performance perspectives, the potential of near‑term quantum computing (NISQ) versus high‑performance classical architectures (HPC) for management tasks such as customer classification, resource allocation, and decision optimisation. We propose an evaluation framework that integrates: (i) model performance metrics (AUC, recall, precision), (ii) energy and carbon metrics (kWh, kgCO2e) per experiment and per unit of business utility, and (iii) scalability under wall‑clock and queue constraints. Using a hybrid pipeline that combines quantum kernels with feature extraction and SVM (Q‑SVM + QFE), we observe that, for recall‑first use cases (e.g., marketing), shallow‑depth circuits can maintain or improve sensitivity, enabling decisions with fewer false negatives. When classical training would be heavy (e.g., extensive hyperparameter sweeps, large kernel matrices), simulated quantum approaches or limited hardware runs can reduce total energy by requiring fewer retraining cycles and allowing ROC thresholding without retraining. We present a practical measurement protocol for modern HPC infrastructures (e.g., MareNostrum 5) and outline scenarios where an environmental quantum advantage is plausible, especially with forthcoming accelerated partitions and fidelity improvements. This comparison is theoretical, based on analytical models parameterised with literature‑backed ranges. We conclude with governance and ESG‑reporting recommendations and a research agenda to quantify “utility per kgCO2e” for data‑driven business decisions.
Cloud computing has fundamentally transformed digital infrastructure by providing on-demand access to scalable computational resources. Yet the inherent unpredictability of workloads alongside surging demands for efficient resource distribution continues to present substantial operational hurdles. This investigation introduces a Quantum-Driven Optimization framework that synergistically combines quantum and classical computational paradigms to improve resource allocation efficacy within cloud ecosystems. The proposed methodology employs Quantum Annealing alongside Variational Quantum Eigensolver algorithms to optimize resource distribution through simultaneous minimization of energy expenditure and operational overhead while maximizing throughput and utilization rates. Empirical validation demonstrates that our framework achieves measurable improvements over conventional heuristic methodologies, specifically showing enhanced resource utilization, reduced operational costs, and accelerated task completion times. The hybrid architecture effectively distributes computational burden by delegating preprocessing and decision-making functions to classical systems while quantum processors tackle inherently complex optimization problems. The solution demonstrates scalability and adaptability across diverse cloud deployment scenarios while exhibiting reduced Service Level Agreement violations, thereby ensuring superior Quality of Service for end-users. This research contributes meaningful insights regarding the practical implementation of hybrid quantum-classical approaches for addressing contemporary resource allocation challenges within cloud computing infrastructures.
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.
Artificial intelligence (AI) infrastructure is becoming increasingly constrained by electricity demand, heat generation, cooling requirements, water consumption, and the lifecycle impacts of specialized hardware. This article proposes a conceptual framework for Quantum AI Engineering for Sustainable Intelligence that treats energy, information, computation, communication, cooling, and water consequences as coupled system-design variables. The framework is informed by a structured narrative synthesis spanning sustainable AI, data-center energy and water use, photonic computing, energy harvesting, quantum sensing, quantum communication, quantum batteries, and hybrid computing. Its central theoretical contribution is the Quantum Energy-Information Interface (QEII): a future systems abstraction that connects observable physical-resource states to computation and communication decisions across classical, photonic, and, where justified, quantum components. The revised framework formalizes cross-layer energy and information flows, introduces state and control variables and a multi-objective sustainability function, separates technologies into deployable, emerging, and exploratory readiness categories, and provides a comparative validation protocol for classical, photonic, quantum-assisted, and hybrid architectures. Water sustainability is treated explicitly through direct cooling water, electricity-related water, embodied water, workload placement, and energy-water trade-offs. The framework does not assume quantum advantage or claim that quantum technologies create energy, eliminate electricity, or enable faster-than-light communication. Instead, it defines testable conditions under which a new component should be considered sustainable: measurable system-level improvement relative to an optimized classical baseline while maintaining accuracy, latency, reliability, security, cost, and lifecycle constraints. The resulting research agenda is intended to support reproducible simulation, prototype development, and eventually system-level evaluation of energy- and water-aware AI infrastructure.