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.
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.
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.
The rapid advancements in artificial intelligence (AI) and quantum computing have catalyzed an unprecedented shift in the methodologies utilized for healthcare diagnostics and treatment optimization. This research paper explores the integration of quantum neural networks (QNNs) with classical machine learning (ML) algorithms to enhance diagnostic accuracy, facilitate personalized treatment plans, and predict patient outcomes with a higher degree of precision. Quantum neural networks, leveraging the principles of quantum mechanics such as superposition, entanglement, and quantum parallelism, have demonstrated the potential to perform complex computations more efficiently than classical counterparts. When coupled with established machine learning algorithms, QNNs can overcome traditional limitations in data processing, enabling more sophisticated models capable of uncovering intricate patterns in large and high-dimensional datasets. Machine learning, with its vast applications in the medical field, has long been instrumental in improving diagnostics and tailoring treatment regimens to patient-specific characteristics. However, despite significant advancements, classical ML approaches face substantial challenges, particularly in terms of computational complexity and the ability to process large-scale, multi-modal healthcare data effectively. Quantum neural networks address these challenges by introducing quantum computational paradigms that facilitate exponentially faster processing, allowing for real-time analysis of vast and complex datasets. The synergy between QNNs and ML algorithms introduces novel approaches that are poised to revolutionize predictive analytics in healthcare, optimizing patient outcomes and enabling highly personalized treatment plans. A key aspect of integrating quantum neural networks into machine learning frameworks is the potential for improved precision in diagnostic systems. Traditional diagnostic procedures often rely on predefined models that may overlook nuanced correlations within patient data. Quantum neural networks, with their ability to represent and process data in a quantum space, provide a more robust framework that can adaptively learn from intricate relationships in patient information. For instance, QNNs can significantly enhance the efficacy of disease detection algorithms, such as those used for identifying early-stage cancers or predicting the onset of chronic conditions like diabetes and heart disease, by offering superior pattern recognition capabilities. Furthermore, QNNs combined with classical machine learning architectures facilitate the creation of hybrid models that harness the strengths of both approaches, leading to diagnostic tools that are not only more precise but also more adaptive to varied data sources. The integration of QNNs with machine learning extends beyond diagnostics to personalized treatment optimization. Traditional treatment planning methodologies, including rule-based and data-driven ML models, often face difficulties in accounting for the multifaceted nature of patient data and individual variability. Quantum neural networks enhance this process by leveraging quantum algorithms that provide an efficient search space for complex treatment optimization problems, allowing for a more detailed understanding of patient responses and potential treatment outcomes. The ability of QNNs to perform parallel processing enables the assessment of a wide range of treatment scenarios simultaneously, leading to more accurate predictions regarding patient reactions to specific drugs, therapies, or medical interventions. This facilitates an adaptive approach that can recommend personalized treatment regimens based on comprehensive patient profiles, ultimately enhancing patient outcomes and reducing the likelihood of adverse drug reactions. In addition to enhancing diagnostics and treatment recommendations, quantum neural networks show promise in forecasting patient outcomes by offering a more robust analysis of longitudinal patient data. Forecasting models that leverage the combined power of quantum and classical algorithms can process historical data more rapidly, allowing healthcare providers to anticipate potential health issues and intervene earlier. For example, predictive models utilizing QNNs can anticipate patient deterioration in critical care settings, facilitating timely interventions that mitigate risks and improve survival rates. Such predictive models can be instrumental in managing chronic diseases, monitoring recovery trajectories, and optimizing resource allocation within healthcare systems, thus contributing to overall efficiency and better resource management. Despite the promising capabilities of integrating QNNs with ML algorithms, there are notable challenges that need to be addressed to fully realize their potential. The practical implementation of quantum algorithms in a healthcare context faces hurdles related to hardware limitations, the need for high fidelity in quantum states, and the scalability of quantum systems to handle real-world clinical data. Additionally, the hybrid nature of combining classical and quantum approaches requires sophisticated algorithms that can bridge the gap between quantum computation and classical data processing pipelines. Solutions to these challenges may include advancements in quantum hardware, such as the development of more stable qubits and noise reduction techniques, as well as the optimization of hybrid algorithms that leverage both classical machine learning and quantum computing capabilities effectively. The exploration of quantum neural networks for healthcare applications also necessitates rigorous ethical considerations, particularly in ensuring data privacy and security. The incorporation of quantum computing must comply with healthcare data protection regulations, and quantum algorithms must be designed to maintain patient confidentiality while processing sensitive health data. Moreover, the interpretability of quantum models poses challenges that could hinder their acceptance in clinical practice. Advances in explainable AI and quantum algorithm transparency are crucial to foster trust among healthcare professionals and patients alike. Integration of quantum neural networks with classical machine learning models represents a transformative approach that could significantly advance healthcare diagnostics, personalized treatment strategies, and patient outcome prediction. By harnessing the computational advantages offered by quantum systems and the flexibility of machine learning algorithms, healthcare applications can achieve a new level of precision and adaptability. Despite current challenges, continued research into quantum algorithms, quantum hardware development, and hybrid computational models promises substantial strides in overcoming these limitations. The synergy between QNNs and ML algorithms could ultimately lead to more effective, personalized, and efficient healthcare solutions, ushering in a new era of data-driven medical care characterized by increased diagnostic accuracy and improved treatment outcomes. As the field evolves, interdisciplinary collaboration between quantum physicists, computer scientists, and healthcare professionals will be vital to unlock the full potential of these innovative computational techniques and bring them to mainstream clinical use.
Healthcare decision support demands models that are accurate on heterogeneous clinical data, fast enough for the point of care, and transparent enough to earn clinician trust. Classical learners meet parts of this brief but struggle to capture the high-order correlations that bind physiological variables together, and they typically expose little of their internal reasoning. This paper introduces QIDSS, a quantum-inspired machine-learning framework that runs entirely on commodity classical hardware yet borrows the mathematical machinery of quantum computation—amplitude encoding, parameterised unitary evolution, and entanglement-style feature interaction—to enrich the representation of clinical evidence. A patient record is mapped into a normalised state vector, a shallow variational circuit emulated through tensor operations transforms that state, and a Born-rule measurement yields a calibrated risk estimate together with a per-feature attribution. Training proceeds by a parameter-shift gradient over the emulated circuit, combined with cross-entropy and an entanglement-regularisation term that discourages spurious correlations. We evaluate the framework on a consolidated clinical dataset and benchmark it against logistic regression, gradient-boosted trees, a deep neural network, and a simulator-based variational quantum classifier. QIDSS reaches 94.5% accuracy with matching gains in precision and recall while holding average decision latency to 9.2 ms, comfortably below the quantum baseline. Beyond the headline numbers, the framework surfaces the features that most influenced each verdict and routes low-confidence cases to clinician review. Implementation overhead, qubit-count scaling, and deployment constraints are examined candidly.
The advent of quantum computing has introduced significant potential to revolutionize healthcare through quantum neural networks (QNNs), offering unprecedented capabilities in processing and analyzing multimodal medical data. Traditional computational methods, including classical machine learning models, face challenges when handling the complex, high-dimensional, and often heterogeneous nature of medical data. Quantum neural networks, leveraging quantum mechanics’ fundamental principles, provide a robust framework capable of handling such data with enhanced accuracy and efficiency. This paper investigates the transformative role of QNNs in healthcare diagnostics, treatment pathway optimization, and long-term prognostic analytics, emphasizing their potential to elevate current medical practices to new heights. We begin by providing an overview of the theoretical foundation of quantum computing and the specific mechanisms by which quantum neural networks operate, detailing the principles of quantum superposition, entanglement, and quantum parallelism. These principles empower QNNs to perform computations that are infeasible for classical counterparts, thereby paving the way for deeper, multidimensional insights into patient data. The integration of QNNs into healthcare presents opportunities for significantly enhancing diagnostic accuracy, particularly when analyzing multimodal inputs such as medical images, genomic data, electronic health records, and physiological signals. The capability of QNNs to operate on qubits, which can exist in multiple states simultaneously, enables them to identify complex patterns that classical models may overlook. Through the use of quantum circuits designed for classification and regression tasks, QNNs can facilitate more precise disease identification, including rare conditions that require intricate data synthesis for accurate diagnosis. Moreover, the inherent parallelism of quantum algorithms allows for faster data processing, which could lead to more timely diagnoses and better patient outcomes. This paper delves into the practical application of QNNs in devising and optimizing treatment pathways. The precision with which QNNs analyze patient-specific data can inform personalized medicine approaches, guiding tailored treatment plans based on real-time patient feedback and evolving clinical parameters. We explore how QNNs can leverage their ability to integrate diverse data sources—such as genetic information, medical history, and lifestyle factors—to model patient responses to different treatments with a higher degree of accuracy. This modeling is essential for predicting potential drug interactions, side effects, and overall efficacy, leading to improved patient care by minimizing trial-and-error approaches and reducing healthcare costs. Long-term prognostic analysis, a critical component of chronic disease management and preventive healthcare, also stands to benefit from the integration of quantum neural networks. By synthesizing longitudinal data and identifying temporal trends, QNNs can uncover latent predictive factors that influence the progression of diseases. The paper presents an analysis of how QNNs can provide accurate predictions for patient outcomes over extended periods, taking into account not only individual health records but also epidemiological data and environmental influences. This type of analysis can assist healthcare providers in developing proactive care strategies that are both more effective and resource-efficient. The potential of QNNs to enhance prognostic assessments is not limited to predicting survival rates but extends to forecasting disease remission and recurrence patterns, contributing to informed decision-making in clinical settings. The paper further highlights current challenges and limitations in implementing QNNs in practical healthcare scenarios. These include technological constraints such as qubit coherence times, quantum error correction, and the scalability of quantum algorithms. The integration of quantum hardware with classical infrastructure also poses significant hurdles, requiring robust hybrid systems that can bridge the gap between quantum and classical data processing. We examine ongoing research efforts aimed at overcoming these challenges, including advancements in quantum algorithm development, quantum hardware improvements, and the development of hybrid quantum-classical architectures that can maximize computational power while mitigating current limitations. Furthermore, ethical considerations related to patient data privacy and security are scrutinized, given the novel nature of quantum computing and the sensitivity of medical data. Ensuring data security in quantum-enhanced healthcare systems is paramount, as the encryption methods that safeguard classical systems may not be sufficient against quantum threats. We discuss the integration of quantum cryptography techniques that can complement QNN applications, providing a comprehensive approach to data protection that aligns with healthcare regulations. This paper concludes by outlining future directions for quantum neural network applications in healthcare. The potential for QNNs to revolutionize the field hinges not only on advancements in quantum hardware but also on the development of interdisciplinary research and collaboration between quantum physicists, computer scientists, and healthcare professionals. The roadmap for widespread adoption includes education and training to equip healthcare practitioners with the necessary skills to work with quantum-enhanced systems, as well as strategic partnerships aimed at integrating quantum technologies into existing healthcare infrastructures. The implications for global health are profound, as quantum neural networks stand to make healthcare more accessible, efficient, and personalized. By transforming diagnostics, optimizing treatment pathways, and providing predictive insights, QNNs could catalyze a paradigm shift in the way medical services are delivered, enhancing both the quality of care and patient outcomes on a global scale.
With the limitations of current noisy intermediate scale quantum (NISQ) devices, there is a need for hybrid quantum-classical learning models to operate more efficiently within existing hardware constraints. The design of cost-effective hybrid quantum-classical learning systems is therefore imperative for various applications, including biosignal processing in the healthcare sector. This motivates the investigation into the reduction of computational complexity of a hybrid quantumclassical neural network. We propose a new hybrid model combining a quantum neural network (QNN) with a binary neural network (BNN), known as quBNN, with the aim to reduce model complexity. For benchmarking, we consider a classical multilayer perceptron (MLP) and a standard nonbinarised version of quBNN that combines a QNN with an MLP, named as quilp. We evaluated all models on a multi-class dataset containing multimodal biosignals. We find that quMLP can outperform the classical MLP with minimal additional complexity, while quBNN maintains performance comparable with the classical MLP, with reduced computational complexity. We apply pruning and quantisation techniques to the QNN within the proposed quBNN model, aiming to further reduce its complexity. This compression yields substantial reductions in parameter and quantum gate count when compiled for quantum hardware, albeit with performance degradation.
Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.