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Carbon efficient quantum AI: an empirical study of ansätz design trade-offs in QNN and QLSTM models
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Carbon efficient quantum AI: an empirical study of ansätz design trade-offs in QNN and QLSTM models
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Journal Article
Carbon efficient quantum AI: an empirical study of ansätz design trade-offs in QNN and QLSTM models
2025
Overview
The rising environmental cost of deep learning has placed Green AI, which promotes focus on reducing the carbon footprint of AI, at the forefront of sustainable computing. In this study, we investigate Quantum Machine Learning (QML) as a novel and energy-efficient alternative by benchmarking two quantum models, the Quantum Neural Network (QNN) and Quantum Long Short-Term Memory (QLSTM), on the N-BaIoT anomaly detection dataset. Our first phase of experiments compares the QNN and QLSTM models using ten distinct quantum circuit designs (ansätze A1–A10). We systematically compare trade-offs between classification performance, model complexity, training time, and energy consumption. The results indicate that simpler QNN ansätze can achieve accuracy comparable to more complex ones while consuming significantly less energy and converging faster. In particular, QNN with ansatz A4 provided the optimal balance between performance and energy efficiency, consistently outperforming QLSTM across most metrics. A detailed energy breakdown confirmed GPU usage as the dominant source of power consumption, underscoring the importance of circuit-efficient quantum design. To contextualize QML’s viability, we conducted a second phase of experiments comparing quantum models with three benchmark classical machine learning models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and CatBoost. We find that the classical models demonstrated faster training times and lower energy consumption, highlighting and contrasting the maturity of algorithmic development that classical ML algorithms have already seen. Finally, we examined the energy implications of developing quantum models on actual quantum hardware. This third phase of experiments compared training on IBM Qiskit’s emulation environment (running on GPU servers) versus execution on real IBM Quantum hardware. Highlighting the significant differences in execution time and energy footprint, extrapolated results indicate that quantum hardware still incurs higher energy costs. This suggests that further hardware-aware ansätz optimization and improvements in quantum infrastructure are essential to realizing carbon-efficient QML at scale.
