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.
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.
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.
Abstract The emergence of noisy intermediate-scale quantum (NISQ) devices has fueled the development of hybrid quantum-classical algorithms for machine learning (ML), promising potential advantages over classical methods. However, the practical efficacy of these algorithms remains uncertain due to challenges like noise susceptibility, trainability issues, and a lack of rigorous benchmarking. This study presents a comprehensive empirical evaluation of two flagship hybrid algorithms the Variational Quantum Eigensolver (VQE) for classification and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization against state-of-the-art classical baselines under equivalent resource constraints. Through extensive simulations incorporating realistic noise models, we demonstrate that while these quantum approaches achieve competitive performance (e.g., 91.2% accuracy for VQE on a binary classification task), they consistently underperformed optimized classical models like Support Vector Machines (94.8% accuracy) and require orders of magnitude more computational time. Furthermore, we provide empirical validation of critical barriers to scalability, including the barren plateau phenomenon and significant performance degradation under noise, even with advanced error mitigation. Our results indicate that for the tasks studied, current NISQ era algorithms do not yet provide a tangible quantum advantage. This work contributes a rigorous benchmarking framework and a critical, realistic assessment of the field, suggesting that future progress hinges on fundamental algorithmic innovations and hardware improvements rather than incremental refinements of existing paradigms.
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.
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.
Abstract Quantum machine learning (QML) has attracted growing interest because quantum states and circuits may provide new representational primitives for learning, yet progress remains constrained by noisy intermediate-scale quantum (NISQ) hardware and heterogeneous evaluation practices. This article presents a critical review of recent QML architectures and representative empirical and theoretical studies, with emphasis on work published since 2022 alongside selected seminal references. To improve comparability, “quantum advantage” is assessed in three distinct senses: computational scaling under stated input-access assumptions, empirical predictive performance in the benchmark setting considered, and resource efficiency at a fixed budget accounting for both classical compute and quantum execution, including shot costs. Organized by architecture family and application domain, the review analyzes how baseline strength, hyperparameter optimization, preprocessing, and budget reporting shape conclusions. Overall, quantum feature maps and kernels, as well as variational (often hybrid) pipelines, can be competitive on selected small- to medium-scale benchmarks, but robust end-to-end evidence for a general practical advantage remains limited. Reported gains are often sensitive to budget matching and may be dominated by measurement overheads, dataset and preprocessing effects, and training instabilities. The review concludes that credible progress requires standardized benchmarking, transparent end-to-end resource accounting, and hardware-centered evaluations that test scalability and robustness beyond toy regimes.
Abstract The arrival of noisy intermediate-scale quantum (NISQ) devices defines an era in which quantum utility is fundamentally constrained by finite coherence times, limited qubit counts, gate infidelities, restricted connectivity, and measurement noise. Within this regime, hybrid quantum-classical frameworks have emerged as the dominant operational paradigm, particularly for quantum machine learning (QML). This review identifies a fundamental trade-off between expressivity, trainability, and noise resilience that governs the performance and scalability of NISQ-era QML systems. To contextualize these challenges, we introduce the resource-constrained iterative routing framework, a hardware-aware conceptual architecture that integrates topology-aware classical compression and distributed quantum orchestration to maintain circuit depths within device-dependent limits while routing representations toward symmetry-constrained Hilbert subspaces to mitigate noise-induced barren plateaus. Furthermore, we shift the discussion of quantum advantage away from asymptotic fault-tolerant complexity toward the notion of operational NISQ advantage based on finite-resource scaling, trainability preservation, and sampling complexity. We evaluate the practical resource efficiency of variational quantum algorithms, quantum kernel methods, and quantum neural networks across superconducting, trapped-ion, neutral-atom, and photonic platforms. The review also examines reproducibility challenges, emphasizing benchmarking against optimized classical baselines, and discusses strategies for managing the sampling overhead of error mitigation alongside distributed execution and quantum circuit compilation. Ultimately, NISQ in Practice positions hybrid QML as a physically constrained computational paradigm while outlining a pathway from contemporary noisy workflows toward early fault-tolerant quantum architectures.