Bibliographic Details
| Title: |
Quantum machine learning regression optimisation for full-scale sewage sludge anaerobic digestion. |
| Authors: |
Mohamed, Yomna, Elghadban, Ahmed, Lei, Hei I, Shih, Amelie Andrea, Lee, Po-Heng |
| Source: |
NPJ Clean Water; 3/5/2025, Vol. 8 Issue 1, p1-13, 13p |
| Subject Terms: |
CIRCULAR economy, BIOGAS production, QUANTUM superposition, WASTEWATER treatment, ANAEROBIC digestion |
| Abstract: |
Anaerobic digestion (AD) is a crucial bioenergy source widely applied in wastewater treatment. However, its efficiency improvement is hindered by complex microbial communities and sensitivity to feedstock properties. We, thus, propose a hybrid quantum-classical machine learning (Q-CML) regression algorithm using a quantum circuit learning (QCL) strategy. Combining a variational quantum circuit with a classical optimiser, this approach predicts biogas production from operational data of 18 full-scale mesophilic AD sites in the UK. Tailored for noisy intermediate-scale quantum (NISQ) devices, the low-depth QCL model outperforms conventional regression methods (R²: 0.53) and matches the performance of a classical multi-layer perceptron (MLP) regressor (R²: 0.959) with significantly fewer parameters and better scalability. Comparative analysis highlights the advantages of quantum superposition and entanglement in capturing intricate correlations in AD data. This study positions Q-CML as a cutting-edge solution for optimising AD processes, boosting energy recovery, and driving the circular economy. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
