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277Chapter 14 Quantum error mitigation for medical imaging noise reduction using GNNsgraph neural networks (GNNs)

E. Chandra Blessie, B Sundaravadivazhagan, V. Kumutha

Published Aug 9, 2025• 0 citations
Publisher

Abstract

Quantum computing is the latest emerging technology that is being used to solve complex problems more efficiently than classical computing technique classical computing technique s. It uses quantum principles of entanglement, superposition, and decoherence to achieve secure communication and computing. Quantum error mitigation quantum error mitigation comprises techniques to reduce the impact of errors in computations by enhancing the computational accuracy. In the current era of quantum computing known as the noisy intermediate-scale quantum (NISQ noisy intermediate-scale quantum (NISQ) ) era, it serves as an interim solution for researchers to develop and apply quantum algorithms for future advancement. Machine learning models are trained for correcting the errors in quantum computing. These models can learn the noise patterns and predict the necessary corrections, thereby enhancing the accuracy of quantum computations. Graph neural networks (GNNs graph neural networks (GNNs) ) offer a promising approach to this problem by exploiting their ability to model complex relationships and dependencies in data. Quantum-enhanced imaging quantum-enhanced imaging technologies promise significant advancements in medical diagnostics by offering improved resolution and sensitivity. However, these technologies are also prone to various forms of noise and errors, stemming from both quantum mechanical phenomena and conventional sources. This study explores the application of GNNs graph neural networks (GNNs) for quantum error mitigation to reduce noise in medical imaging, enhancing the accuracy and clarity of the results. GNNs graph neural networks (GNNs) are leveraged to model the complex structural relationships within medical imaging data, effectively learning and identifying noise patterns. By representing imaging data as a graph, where nodes correspond to pixels or voxels and edges represent spatial or relational connections, GNNs graph neural networks (GNNs) can predict and correct for errors. In this study, we review GNN based quantum error techniques for medical imaging. This work highlights the potential of GNNs graph neural networks (GNNs) as a powerful tool for enhancing quantum error resilience, providing a new direction for research and development in the quest for reliable and scalable quantum computing.

DOI
10.1515/9783111354934-014

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Quantum computing is the latest emerging technology that is being used to solve complex problems more efficiently than classical computing technique classical computing technique s. It uses quantum principles of entanglement, superposition, and decoherence to achieve secure communication and computing. Quantum error mitigation quantum error mitigation comprises techniques to reduce the impact of errors in computations by enhancing the computational accuracy. In the current era of quantum computing known as the noisy intermediate-scale quantum (NISQ noisy intermediate-scale quantum (NISQ) ) era, it serves as an interim solution for researchers to develop and apply quantum algorithms for future advancement. Machine learning models are trained for correcting the errors in quantum computing. These models can learn the noise patterns and predict the necessary corrections, thereby enhancing the accuracy of quantum computations. Graph neural networks (GNNs graph neural networks (GNNs) ) offer a promising approach to this problem by exploiting their ability to model complex relationships and dependencies in data. Quantum-enhanced imaging quantum-enhanced imaging technologies promise significant advancements in medical diagnostics by offering improved resolution and sensitivity. However, these technologies are also prone to various forms of noise and errors, stemming from both quantum mechanical phenomena and conventional sources. This study explores the application of GNNs graph neural networks (GNNs) for quantum error mitigation to reduce noise in medical imaging, enhancing the accuracy and clarity of the results. GNNs graph neural networks (GNNs) are leveraged to model the complex structural relationships within medical imaging data, effectively learning and identifying noise patterns. By representing imaging data as a graph, where nodes correspond to pixels or voxels and edges represent spatial or relational connections, GNNs graph neural networks (GNNs) can predict and correct for errors. In this study, we review GNN based quantum error techniques for medical imaging. This work highlights the potential of GNNs graph neural networks (GNNs) as a powerful tool for enhancing quantum error resilience, providing a new direction for research and development in the quest for reliable and scalable quantum computing.
Abstract Quantum computing holds transformative promise, but its realization is hindered by the inherent susceptibility of quantum computers to errors. Quantum error mitigation has proved to be an enabling way to reduce computational error in present noisy intermediate scale quantum computers. This research introduces an innovative approach to quantum error mitigation by leveraging machine learning, specifically employing adaptive neural networks. With experiment and simulations done on 127-qubit IBM superconducting quantum computer, we were able to develop and train a neural network architecture to dynamically adjust output expectation values based on error characteristics. The model leverages a prior classifier module outcome on simulated quantum circuits with errors, and the antecedent neural network regression module adapts its parameters and response to each error characteristics. Results demonstrate the adaptive neural network’s efficacy in mitigating errors across diverse quantum circuits and noise models, showcasing its potential to surpass traditional error mitigation techniques with an accuracy of 99% using the fully adaptive neural network for quantum error mitigation. This work presents a significant application of classical machine learning methods towards enhancing the robustness and reliability of quantum computations, providing a pathway for the practical realization of quantum computing technologies.
The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as intermediate scale quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this article, we propose a machine learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using graph neural network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.
Quantum error mitigation (QEM) provides a practical route for estimating reliable observables on noisy intermediate-scale quantum (NISQ) devices. Traditional QEM strategies, including zero-noise extrapolation (ZNE) and Clifford data regression (CDR), rely on noise scaling or global regression, and their performance is constrained by the exponential growth of the system degrees of freedom. We construct a graph-enhanced mitigation (GEM) framework, which incorporates physical information into the model representation. In this work, quantum circuits are encoded as attributed graphs. Hardware-level physical information is mapped to node and edge features: local noise parameters such as calibration parameters T1T_1T1​, T2T_2T2​, and readout errors are encoded at nodes, while coupling-related information such as two-qubit gate errors is encoded as edge features. Graph neural networks are used to model how errors propagate along the physical coupling structure and build up into non-local correlations. This allows the model to capture local interactions and part of the resulting non-local correlations across qubits. A dual-branch affine correction is applied to maintain consistency with physical constraints. Experiments on 10-qubit and 16-qubit random circuits executed on superconducting quantum processors show that GEM provides a level of accuracy comparable to CDR at small scales, while yielding lower mean absolute error and improved stability in zero-shot transfer to larger systems. Results of the traditional QEM strategy indicate that global regression methods remain effective in low-dimensional settings but become less reliable as system degrees of freedom grow. In contrast, GEM makes use of local physical structures to show better scalability and generalization, while preserving the overall error propagation patterns. This work provides a practical scalable approach to QEM for NISQ devices.
Abstract Noisy Intermediate-Scale Quantum (NISQ) computing is fundamentally constrained by hardware noise that is not only large but also temporally drifting and structurally complex. Traditional quantum error mitigation (QEM) techniques, such as Zero-Noise Extrapolation (ZNE), often rely on simplified noise assumptions that degrade under coherent errors and deep circuit structures. Conversely, existing machine learning-based QEM approaches typically treat device noise as a static background, leading to poor generalization when hardware calibration shifts. In this work, we propose \textbf{Calib-GNN}, a novel framework that explicitly integrates real-time hardware calibration data into a Graph Neural Network (GNN) architecture. By embedding gate-specific error rates directly into the graph representation, our model learns a dynamic mapping from noisy measurements to ideal expectation values. Through extensive simulations based on real noise profiles from the IBM Quantum \texttt{ibm\_fez} backend, we demonstrate that Calib-GNN significantly outperforms standard baselines. Specifically, it reduces the Root Mean Square Error (RMSE) by approximately 14\% compared to ZNE on random circuits and exhibits superior robustness against severe noise drift where traditional models fail. Furthermore, we show that Calib-GNN effectively scales to deeper circuits and recovers the ground state energy of the H2H_2H2​ molecule in Variational Quantum Eigensolver (VQE) tasks with high fidelity, overcoming the limitations of ZNE in the presence of coherent noise.
Quantum computing is quickly revolutionizing medicine, promising unprecedented levels of processing, analysis, and decision-making abilities. This review discusses recent developments in applying quantum computing to medical imaging, diagnostics, and treatment planning. Quantum machine learning and hybrid quantum-classical models have been used by researchers to show improved accuracy, speed, and scalability in medical image classification, segmentation, and disease prediction. Quantum-improved algorithms are being implemented for early detection of multifactorial diseases like cancer and neurologic disorders with potential for advancing clinical prognoses and biomarker discovery. Moreover, quantum cryptography methods are under development to enhance security for private medical information with concerns regarding expanding privacy and security risks. Improvements in quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) also hold promise in enhancing diagnostic efficiency and resource savings. This review synthesizes existing applications, identifies new trends, and delineates challenges and future directions of quantum computing in medicine. It underscores the importance of explainable and interpretable quantum AI systems to maintain clinical trust and utility. The results indicate that quantum technologies, although in their infancy, have transformative potential for precision medicine, personalized therapy, and sophisticated medical decision-making.
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.