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 sev...
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.
Muhammad Minoar Hossain, Safiul Haque Chowdhury, Md. Hasibul Hassan Himal et al.· Electronics· 0 citations
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
A novel QST protocol that utilizes Kirkwood-Dirac (KD) quasiprobability to reconstruct quantum states, which enables state reconstruction with only two complementary rank-one projective measurements, thus significantly reducing the measurement cost.
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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