Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings. The code is available at https://github.com/arco-group/quantum-ml.
Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings. The code is available at https://github.com/arco-group/quantum-ml.
This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F). The proposed framework investigates the complexity-fidelity trade-off of quantum forecasting under near-term NISQ hardware constraints. Continuous time-series signals are transformed into binary representations through uniform quantization and encoded into quantum states using angle encoding with parameterized RY rotation gates. Cross-channel entanglement layers capture dependencies among multiple variables. QRC-F utilizes a fixed random unitary quantum reservoir for stable, gradient-free temporal feature extraction, whereas VQF-F employs a trainable variational quantum circuit optimized through the parameter-shift rule to learn temporal and inter-variable patterns from Pauli expectation values. Both models replace computationally expensive quadratic self-attention with efficient linear transformations, reducing parameter complexity. A shared MIMO-based multi-horizon prediction head simultaneously generates forecasts across multiple horizons, avoiding error accumulation in recursive forecasting. Experimental evaluations on benchmark datasets, including ETTh1, ETTh2, ETTm1, ETTm2, Weather, electricity, and exchange-rate, demonstrate that VQF-F achieves superior training stability and parameter efficiency, while QRC-F provides enhanced robustness and circuit fidelity under quantum noise. The results establish a practical quantum-native forecasting framework with strong potential for deployment on near-term NISQ devices.
Abstract Heart disease remains to be a primary cause of death globally, requiring precise and timely predictive methodologies. Traditional machine learning methods that depend exclusively on classical clinical variables frequently have restricted predictive accuracy and do not adequately use the intricate nonlinear interactions present in patient data. This work examines the effectiveness of hybrid quantum-classical models for predicting heart disease by combining classical clinical data with quantum-derived representations via Variational Quantum Classifier (VQC), Estimator, and Sampler models. Twelve hybrid configurations, integrating three quantum feature maps (Z, ZZ, Pauli) with four ansatz architectures (RealAmplitudes, TwoLocal, EfficientSU2, PauliTwoDesign), were assessed inside the PennyLane and Qiskit frameworks. The results indicate that the VQC model in PennyLane reached optimal performance with Z–TwoLocal and ZZ–PauliTwoDesign configurations, achieving accuracy and F1-scores of up to 0.95. The Estimator model consistently produced reliable predictions, with Pauli–EfficientSU2 and Z–TwoLocal attaining accuracies of up to 0.91 and F1-scores of 0.92 across several frameworks. The Sampler model demonstrated 1reasonable performance, with the Pauli–EfficientSU2 configuration achieving an accuracy of 0.65 and an F1-score of 0.73, underscoring the supplementary role of stochastic quantum sampling. The analysis of feature significance indicated that the quantum-derived feature provided the most robust predictive signal, exceeding classical variables like Age, Blood Pressure, and Cholesterol. These findings demonstrate that meticulous alignment of feature maps and ansatz circuits is essential for optimizing model expressivity, convergence, and predictive effectiveness. In summary, hybrid quantum-classical models, especially the Estimator and VQC architectures, significantly improve heart disease prediction, providing a feasible method for precision clinical decision support and potential incorporation into privacy-preserving, multi-institutional healthcare systems.
In this paper, we address the challenge of multivariate time-series forecasting using quantum machine learning techniques. We introduce adaptation strategies that extend variational quantum circuit models, traditionally limited to univariate data, toward the multivariate setting, exploring both purely quantum and hybrid quantum-classical formulations. First, we extend and benchmark several VQC-based and hybrid architectures to systematically evaluate their capacity to model cross-variable dependencies. Second, building upon these foundations, we introduce the iQTransformer, a novel quantum transformer architecture that integrates a quantum self-attention mechanism within the iTransformer framework, enabling a quantum-native representation of inter-variable relationships. Third, we provide a comprehensive empirical evaluation on both synthetic and real-world datasets, showing that quantum-based models may achieve competitive or superior forecasting accuracy with fewer trainable parameters and faster convergence than state-of-the-art classical and quantum baselines in some cases. These contributions highlight the potential of quantum-enhanced architectures as efficient and scalable tools for advancing multivariate time-series forecasting.
Abstract In this paper, we address the challenge of multivariate time-series forecasting through quantum-enhanced machine learning architectures. We propose adaptation strategies that extend variational quantum circuit models (VQC), traditionally constrained to univariate data, toward the multivariate setting, exploring both purely quantum and hybrid quantum-classical formulations. First, we extend and benchmark several VQC-based and hybrid models to systematically evaluate their ability to capture cross-variable dependencies. Building on these foundations, we introduce the iQTransformer, a novel quantum transformer architecture that integrates a quantum self-attention mechanism within the iTransformer framework, enabling a quantum-native representation of inter-variable relationships. Finally, we present a comprehensive empirical evaluation on both synthetic and real-world datasets, demonstrating that quantum-enhanced models can achieve competitive or superior forecasting accuracy with fewer trainable parameters and faster convergence than state-of-the-art classical and quantum baselines. These findings highlight the potential of quantum-enhanced architectures as efficient and scalable tools for advancing multivariate time-series forecasting.
Cardiovascular disease remains one of the leading causes of death worldwide, making early and accurate risk prediction essential. Conventional machine learning models often struggle to handle complex medical data, resulting in limited accuracy and reduced interpretability. To address these challenges, this study presents a hybrid prediction framework that combines Quantum Machine Learning (QML) with ensemble learning techniques. Classical models such as Support Vector Classifier (SVC) and Artificial Neural Network (ANN) are used as baseline approaches. In addition, quantum models including QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) are employed to capture complex non-linear patterns present in clinical data. To further enhance performance, a Bagging-QSVC ensemble model is proposed, improving robustness and overall classification accuracy. Interpretability is incorporated using SHAP, allowing the model’s predictions to be more transparent and easier to understand in a clinical context. Experimental results indicate that the proposed quantum ensemble achieves an accuracy of around 90%, outperforming both classical models and individual quantum models. Furthermore, a real-time user interface is developed, enabling clinicians to input patient data and receive immediate risk predictions. Overall, the system provides a reliable and interpretable solution suitable for practical clinical decision support.
To forecast heart problems, an innovative hybrid technique combining quantum computing with machine learning approaches is presented. For the preprocessing stage, normalization and dimensionality reduction techniques are used on a dataset of 270 patients' records and 13 clinical variables to make them appropriate for a 4-qubit quantum computer. Some traditional machine learning algorithms, including convolutional neural networks (CNN), support vector machines (SVM), and XGBoost, are trained to establish baselines in relation to other methods. Under the guidance of the SPSA algorithm, a quantum-inspired classifier called VQC is developed using a parameterized quantum circuit that includes rotation and entanglement operations. A quantitative study of crucial performance metrics reveals that the suggested quantum technique outperforms conventional methods in tests. The VQC classifier has the highest accuracy (85.2%), precision (86.4%), and F1 score (82.6%), while matching the SVM classifier's ROC-AUC score of 88.9%. Although with lower dimensionality, this research shows how quantum-based machine learning may find complex correlations among variables. This study is intended to serve as a preliminary concept for using quantum machine learning for healthcare applications, especially for increasing the accuracy rate and lowering misdiagnosis errors.