Quantum machine learning has gained significant momentum in recent years, particularly with the use of parameterized quantum circuits that exhibit a certain level of resilience to noise in current NISQ devices. However, the real-world applicability of QML remains an active field of research as transitioning from idealized simulations to real quantum hardware introduces challenges related to data size and noise. Our study empirically analyzes the performance of popular quantum neural networks in ideal, noise-free conditions, but also in the presence of realistic noise, which was modeled to mimic the noisy behavior of current IBM quantum devices. We carefully selected three medical classification tasks that cover common data modalities with varying numbers of features-from tabular cell data to signal sampling points and image pixels. Our findings statistically show that while complex parameterized circuits perform well in noise-free simulations, their performance significantly deteriorates with increasing qubit numbers under noisy conditions. Our study suggests that parameterized quantum circuits of moderate complexity are a favorable choice for future experiments on noisy quantum devices.
Quantum machine learning has gained significant momentum in recent years, particularly with the use of parameterized quantum circuits that exhibit a certain level of resilience to noise in current NISQ devices. However, the real-world applicability of QML remains an active field of research as transitioning from idealized simulations to real quantum hardware introduces challenges related to data size and noise. Our study empirically analyzes the performance of popular quantum neural networks in ideal, noise-free conditions, but also in the presence of realistic noise, which was modeled to mimic the noisy behavior of current IBM quantum devices. We carefully selected three medical classification tasks that cover common data modalities with varying numbers of features-from tabular cell data to signal sampling points and image pixels. Our findings statistically show that while complex parameterized circuits perform well in noise-free simulations, their performance significantly deteriorates with increasing qubit numbers under noisy conditions. Our study suggests that parameterized quantum circuits of moderate complexity are a favorable choice for future experiments on noisy quantum devices.
Quantum neural networks are a popular realization of quantum machine learning. Typically, classical data is first embedded into the state vector of a quantum system. A parameterized quantum circuit (PQC) then acts on the quantum state, with its trainable parameters continuously adjusted during the optimization process. However, current quantum hardware faces challenges, such as accumulating noise with increasing circuit complexity and a limited set of native gate operations. As a result, transpiling quantum circuits to real hardware often increases the number of the required gate instructions. These factors set considerable constraints on the accuracy of quantum machine learning models. To address these limitations, we propose a novel quantum neural network architecture, termed hardware-native parameterized quantum embedding (HN-PQE). Our approach offers two key advantages: First, HN-PQE exclusively employs the native gates of IBM's current Eagle quantum processor family, ensuring that the gate count remains stable during transpilation. Second, by combining data embedding with the PQC, HN-PQE achieves a highly compact circuit design. We empirically validate our approach on two classification tasks, simulating both idealized (no hardware noise) and noisy conditions (mimicking IBM's Brisbane device). When compared to two popular quantum neural networks, HN-PQE showed similar performance in idealized scenarios but demonstrated significant noise resilience under hardware-noise conditions. This resilience makes HN-PQE a promising candidate for future quantum machine learning experiments on real hardware.
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. Our methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling, and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. The proposed methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling (Twir), and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study under ideal, noise-free simulation. We systematically assess a selection of commonly used HQNN architectures on three binary medical classification tasks covering medical signal data as well as planar and volumetric images. In doing so, we examine the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. Within this experimental setting, we find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts. However, in most cases, performance metrics deteriorate when the investigated QNN components replace matched classical processing layers. Our multi-modal analysis provides empirical insights into the contributions of the tested quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.
This work focuses on the design and evaluation of Hybrid Quantum Neural Networks (HQNNs) that integrate the computational advantages of quantum circuits with the learning flexibility of classical deep neural networks to enhance machine learning performance in the Noisy Intermediate-Scale Quantum (NISQ) era.The study investigates two prominent HQNN architectures, namely Quantum Convolutional Neural Networks (QCNNs) and Quanvolutional Neural Networks (QuanNNs), with an emphasis on their robustness under realistic quantum noise conditions.It was evaluated using image classification tasks.Under various quantum noise channels, including Bit Flip, Phase Flip, Phase Damping, Amplitude Damping, and Depolarizing Noise, were introduced to reflect realistic hardware conditions.To reduce noise-induced performance loss, error mitigation techniques such as Zero-Noise Extrapolation, Probabilistic Error Cancellation, and Measurement Error Mitigation were applied.In addition, an AI-assisted adaptive framework is proposed to automatically recommend suitable mitigation strategies based on observed noise behavior and circuit characteristics.Experimental results indicate that QuanNN exhibits strong robustness under lowto-moderate noise levels and maintains stable performance even under high bit-flip noise, while QCNN demonstrates occasional performance gains under specific noisy configurations but degrades more rapidly as noise intensity increases.The proposed AI-guided mitigation mechanism further enhances model stability across varying noise regimes.This study provides practical insights into architecture selection, noise resilience, and intelligent error mitigation integration, offering a scalable framework for deploying HQNNs in real-world NISQ-era quantum machine learning applications.
Quantum machine learning (QML) models hold promise for leveraging quantum computers in data-driven tasks but their performance on today's noisy quantum hardware remains an open question. In this work, we investigate the impact of shot noise, the statistical noise due to finite sampling in quantum measurements, on the training and performance of quantum neural network (QNN) models. Using a dataset of dielectron collision events from CERN, we train QNNs under varying levels of simulated noise. Three ansatz circuit architectures are explored namely BasicEntanglerLayers (Ansatz-1), SimplifiedTwoDesign (Ansatz-2) and StronglyEntanglingLayers (Ansatz-3). Each model is repeatedly trained for multiple runs at noise levels ranging from zero up to significant noise fractions (0.5), with each training run consisting of 20 epochs of parameter optimization. We evaluate the convergence behavior, final error rates and variability across runs for each noise setting. Our results show a clear degradation of QNN performance as shot noise increases with training loss converging more slowly and to higher values and the model's prediction accuracy deteriorates with larger noise. Notably, the ansatz with fewer parametric gates demonstrates relatively better robustness to noise compared to deeper, more strongly entangling circuits which suffer sharper performance drops under noise. We provide detailed comparisons of the three circuit designs, including training loss curves and statistical summaries of performance at each noise level. These findings shed light on the practical limits of QML models in the Noisy Intermediate-Scale Quantum (NISQ) era and highlight the trade-offs between circuit expressiveness and noise resilience. Finally, we discuss implications for designing noise-aware quantum models and possible strategies to mitigate shot noise effects in quantum learning.