A Quantum Multilayer Perceptron for Intraoperative Nociception Prediction

Pain pathways and interpatient variability represent an ongoing challenge for nociception monitoring. Inter- and intrapatient variability require large patient data for training conventional nociception predictive models. In this paper, we propose a quantum multilayer perceptron (QMLP) model for nociception prediction utilizing quantum features such as entanglement which enables the capturing of complex parameter dependencies using less data, by representing intralayer connections between parameters. Our QMLP architecture encodes input physiological parameters into quantum states which are processed through entangled quantum circuits, and optimized using parameter shift rule for gradient estimation. Our nociception prediction model is trained and evaluated on surgical data collected from two hospitals, with features extracted from electrocardiogram, photoplethysmography, and electroencephalogram. Through systematic comparative analysis across multiple datasets using two sampling approaches (patient-wise and downsampling), we demonstrate that our QMLP model consistently outperforms different classical baselines including deep learning models, tree-based ensembles, and linear models across all evaluation metrics. Clinical evaluation on different populations of patients confirms the QMLP model’s superior ability to predict nociceptive changes during surgical events including Intubation, Incision, and Extubation. Expressivity analysis reveals that QMLP models achieve approximately twice the local effective dimension of classical baselines with identical parameter counts. Entanglement topology analysis demonstrates that circular configuration consistently achieves lower training loss compared to linear, pairwise, and non-entangled architectures, with quantum advantage driven by entanglement structure independent of data quantity. Our findings suggest quantum neural networks might offer advantages for nociception monitoring in anesthesia applications, particularly in data-limited scenarios where complex interrelated parameters influence clinical outcomes. The code used in this research can be accessed from:https://github.com/oyanoth/qmlp-nociception.