Towards Sustainable Management: Environmental and Operational Advantages of Quantum Computing over Classical HPC in the NISQ Era

Abstract

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.