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.
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.
The urgency to decarbonize digital infrastructure motivates the search for lowerfootprintcomputational methods in management analytics. This article evaluates,from sustainability and economic performance perspectives, the potential of neartermquantum computing [noisy intermediate-scale quantum (NISQ) computing]versus high-performance classical architectures (HPC) for management tasks suchas customer classification, resource allocation, and decision optimization. We proposean evaluation framework that integrates: (i) model performance metrics (areaunder the curve, recall, precision), (ii) energy and carbon metrics [kWh, kilogramsof CO2 equivalent (kgCO2e)] per experiment and per unit of business utility, and(iii) scalability under wall clock and queue constraints. Using a hybrid pipeline thatcombines quantum kernels with feature extraction and support vector machines(SVM) [quantum SVM plus quantum feature extraction (QSVM + QFE)], we observethat, for recallfirst use cases (e.g., marketing), shallowdepth circuits can maintainor improve sensitivity, enabling decisions with fewer false negatives. When classicaltraining would be heavy (e.g., extensive hyperparameter sweeps, large kernelmatrices), simulated quantum approaches or limited hardware runs can reducetotal energy by requiring fewer retraining cycles and allowing receiver operatingcharacteristic (ROC) thresholding without retraining. We present a practical measurementprotocol for modern HPC infrastructures (e.g., MareNostrum 5) and outlinescenarios where an environmental quantum advantage is plausible, especiallywith forthcoming accelerated partitions and fidelity improvements. This comparisonis theoretical, based on analytical models parameterized with literature-backedranges. We conclude with governance and environmental, social and governance(ESG) reporting recommendations and a research agenda to quantify “utility perkgCO2e” for datadriven business decisions.
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.
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.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.
This paper has a full-scale reference architecture for hybrid quantum-classical learning to include the integration of classical representation models with quantum neural networks (QNN), quantum support vector machines (QSVM), quantum principal component analysis (qPCA), variational quantum circuits (VQC), and QAOA-based combinatorial optimization to create next-generation predictive business intelligence and complex decision optimization subject to all real world operating constraints. The proposed structure has a layered system consisting of; (1) Quantum aware data encoding techniques through Amplitude, Basis and Angle Mapping; (2) Differentiable Hybrid Learning Pipelines optimized using Parameter Shift Gradient Methods across Simulators and Managed Quantum Processing Units; (3) Prescriptive Workflows that reformulate Multi-Objective Business Problems such as Supply Chain Coordination, Routing with Time Windows and Portfolio Construction into QUBO Representations Supported by Convex Warm Starts and Classical Post-Processing to Guarantee Feasibility and Explainability. An End-To-End Orchestration Layer will manage Circuit Templates, Data Hashes, Model Versions and Backend Execution Metadata to Provide Reproducibility, Traceability, Governance Compliance Across All Enterprise Pipelines. A Systematic Evaluation Protocol Will be Defined to Isolate Quantum Contribution Through Systematic Ablation Analysis of Classical-Only, Quantum-Only, and Hybrid Configurations Combined With Rolling Window Validation, Latency Profiling, Cost Efficiency Analysis, and Carbon Impact Accounting. Industry Standard Time Series and Probabilistic Accuracy Measures Including sMAPE, MASE, CRPS and Calibration Consistency Will Be Used to Evaluate the Performance of Predictive Models. Optimization Quality Will Be Evaluated Using Optimality Gap, Constraint Violation Rates, Time to Feasible Solution and Downstream Business Impact on Profit Margins and Service Levels. An Interpretability Layer Will Provide Transparency by Mapping Quantum Feature Spaces, Circuit Observables, and Kernel Alignment Structures to Domain-Specific Reasoning to Build Stakeholder Trust, Auditability and Regulatory Alignment. In Addition, The Framework Will Incorporate Adaptive Decision-Making Via Quantum Reinforcement Learning to Allow Continuous Optimization Under Dynamic and Uncertain Business Environments. An Energy Aware Execution Layer Will Dynamically Route Workloads Between Classical Simulators and Quantum Hardware According to Predefined Energy Budgets, Carbon Thresholds, and Performance Demands. The Overall Framework Provides a Scalable, Auditable and Ethically Aligned Blueprint for Deploying Hybrid Quantum-Classical Intelligence Within Existing Enterprise Ecosystems for Applications Such as Financial Forecasting, Retail Analytics, Dynamic Pricing, Market Risk Modeling, and Supply Chain Optimization.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.