Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating str...
Yu-Qi Zhang, Yuxin Yang, Fei-Xiong Cheng et al.· ACM Transactions on Quantum...· 0 citations
In this study, a round-synchronous distributed quantum computing framework is developed for coherent controlled islanding under a fixed per-circuit qubit budget and attains optimal and operationally feasible partitions on every platform under noise.
Yu-Qi Jiang, Zhi-Ding Liang, Qiang Guan et al.· 0 citations
A qubit-efficient hybrid quantum framework combining a physics-informed compact encoding with Lagrangian constraint handling and classical feasibility refinement is presented, offering a transferable approach for scaling constrained quantum optimization toward larger real-world applications on near-term hardware.
The proposed framework provides a feasible and scalable pathway for quantum optimization in large-scale power systems and substantially reduces quantum-resource demand and circuit complexity relative to monolithic QAOA, allowing large islanding problems to be addressed within current hardware limits.
Yu-Qi Jiang, Zhi-Ding Liang, Qiang Guan et al.· 0 citations
QSAD is presented, a quantum-classical framework that reformulates peptide structure prediction as amino-acid-level Hamiltonian sampling and replaces iterative optimization with non-iterative Hamiltonian evolution and establishes coarse-grained quantum sampling as a practical computational path for structure prediction...
Yuqi Zhang, Bo Fang, Yuxin Yang et al.· 1 citation
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