Extensive experiments indicate that, despite the limited number of qubits that can be simulated, QuantumBoostNet consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification, achieving a relative improvement over the best competitor.
The hybrid classical-quantum architecture QuantumBoostNet is proposed, which combines a classical backbone with two heads: one classical and one quantum, a parametrized 10-qubit quantum circuit, which outperforms the implemented baselines under matched training conditions.
Mihai Udrescu-Milosav, S. Jura, M. Udrescu et al.· 0 citations
The hardware available today does not support general-purpose quantum computation in medical settings, but specific subtasks such as sensing, photon-resolved detection, or image reconstruction can already be handled by quantum components embedded in classical systems. This article surveys recent developments across several branches of medical applications, with a complementary look at an analogous trend in machine learning. Nitrogen-vacancy centers in nanodiamonds enable nanoscale thermometry, high-resolution magnetocardiography, and real-time monitoring of free radicals inside living cells. In radiological imaging, photon-counting computed tomography is already entering routine clinical use, while QUBO-based image reconstruction on quantum annealers and gate-model processors points toward dose reduction in low-dose CT. Quantum-enhancedpositron emission tomography exploits polarization correlations of entangled annihilation photons to suppress background events and opens a path toward positronium-based biomarkers such as tissue hypoxia. Nuclear magnetic resonance ensemble computing is included to illustrate the scalability limits of fully quantum systems. The same hybrid pattern is then identified outside medicine, in efficient fine-tuning of large language models, where localized quantum modules reduce parameter counts without degrading task accuracy. The reviewed work differs in technical maturity, from systems already deployed clinically to proof-ofprinciple experiments such as X-ray spontaneous parametric down-conversion. Across this range, the quantum component handles one well-defined stage of an otherwise classical system.
I. Woźniak, Mateusz Jangas, Mateusz Piątek et al.· International Journal of Ele...· 0 citations
Findings show that current quantum feature maps through the encode–measure–boost pipeline on NISQ hardware do not yet outperform a well-designed classical pipeline.
Gerard Edwards, Richard Stocker, Mohammed Alharbi et al.· Electronics· 0 citations
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Shahjalal Khan, Jahid Karim Fahim, P. Paul et al.· International Journal of Adv...· 0 citations
Quantum state tomography (QST) is of fundamental importance to characterize quantum systems in quantum information processing, but its practical implementation is severely hindered by the exponential scaling of measurement and computational costs. In this paper, we present a novel QST protocol that utilizes Kirkwood-Dirac (KD) quasiprobability to reconstruct quantum states. First, it enables state reconstruction with only two complementary rank-one projective measurements, thus significantly reducing the measurement cost. Then, a complex logistic regression estimator is proposed to process collected KD data, together with a projected gradient algorithm to mitigate numerical instability and to accelerate convergence. The product-operator structure of KD quasiprobability is further exploited to reduce the computational cost. Finally, extensive experiments are implemented to confirm the validity of our protocol. Notably, the full reconstruction of randomly generated 15-qubit mixed-state instances can be accomplished within 20 minutes under the GPU implementation. These results suggest a promising route toward scalable QST and benchmarking large-scale quantum systems.
Xiang Li, Yong Wang, Li-Jun Liu et al.· 0 citations
A novel physics-guided linear mapper for quantum error mitigation that uses seven distinct interpretable features derived from circuit complexity and device calibration data, which reveals that circuit depth and CNOT count dominate error prediction, consistent with decoherence mechanisms.
Tulsi Chaudhari, Krish Bhatia, Shalini Devendrababu et al.· Lecture Notes in Networks an...· 0 citations
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