The Role of Quantum in Hybrid Quantum-Classical Neural Networks: An Empirical Assessment
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arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.
arXiv:2607. 21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning.
The paper introduces a hybrid quantum-classical neural network (HQNN) for predicting peptide-HLA binding, a key step in personalized cancer immunotherapy. HQNN combines biological feature encoding, quantum feature extractors, and a quantum-enhanced classifier, outperforming a classical CNN baseline on two HLA alleles across all training sizes, especially when data are scarce. Ablation studies and noise-aware simulations confirm the benefits of the quantum components and the robustness of the approach under realistic hardware noise.
QuantumBoostNet is a hybrid classical‑quantum architecture designed to improve accuracy in identifying cardiac ultrasound views. It combines 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 loss dynamics. 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.
The paper presents an evolutionary framework, EXAQC, that automatically discovers parameterized quantum circuits (PQCs) for use as intermediate modules in hybrid quantum‑classical neural networks for image classification. By evolving PQCs while keeping classical feature‑extraction and prediction layers fixed, the authors achieve high accuracies on MNIST, Fashion‑MNIST, and CIFAR‑10 with gate counts comparable to other quantum architecture‑search methods. The evolved hybrid models match or exceed the performance of classical networks while using far fewer trainable parameters, and the choice of encoding (rotation‑based vs amplitude) significantly impacts accuracy.