Toward Sustainable Management: Environmental and Operational Advantagesof Quantum Computing over Classical HPC in the NISQ Era

Abstract

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.