QuantumBoostNet is a hybrid classical‑quantum architecture designed to improve accuracy in identifying cardiac ultrasound views. The model combines a classical backbone with two heads—one classical and a 10‑qubit quantum circuit—using an adaptive mixing parameter to switch between them during training. Experiments show that, even with limited qubits, QuantumBoostNet outperforms state‑of‑the‑art classical and hybrid models on cardiac ultrasound view identification, image classification benchmarks, and demonstrates robustness to noise.
QuantumBoostNet is a hybrid classical‑quantum architecture designed to improve cardiac ultrasound view identification. It couples a classical backbone with two heads—one classical and a 10‑qubit quantum circuit—trained in two stages with an adaptive mixing parameter that balances the heads based on loss dynamics. Experiments demonstrate that QuantumBoostNet surpasses baseline models on standard benchmarks and shows statistically significant gains on FashionMNIST and MNIST, with a modest improvement on the echocardiography task and robustness to noise.
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