AI 中文总结
研究针对近期硬件上周期性固体变分量子能带结构计算中构建哈密顿量成本高的问题，结合SlaKoNet模型与VQD算法，实现通用神经哈密顿量生成，经多材料基准测试及扩展，可进行高通量VQA基准测试和协同优化以发现材料。
AI 中文摘要
变分量子算法如VQE和VQD在近期电子结构计算中很有前景，但对于周期性固体，构建忠实的二次量子化哈密顿量成本高昂。SlaKoNet通过将深度学习与斯莱特-科斯特紧束缚形式相结合来拟合65种元素的跳跃和重叠参数，实现任意晶体的哈密顿量构建。本文将基于JARVIS-TBmBJ训练的SlaKoNet模型与基于Qiskit的VQD算法结合，用通用神经哈密顿量生成器取代昂贵的哈密顿量构建。所得SlaKoNet-VQD工作流程与结构无关、可微且适用于高通量能带结构筛选。在硅上基准测试，在3量子比特模拟器上沿标准k路径恢复完整八能带结构，平均绝对偏差为1.78 meV，还扩展到五种常规超导体。在IBM量子硬件上对铝进行k点基态计算。进一步将哈密顿量推广到通过动态平均场理论求解的相关哈伯德模型，识别杂质问题为自然量子求解器目标。该流程实现跨周期表的高通量VQA基准测试和基于梯度的波函数-哈密顿量协同优化以进行材料发现。
英文摘要
Variational quantum algorithms such as VQE and VQD are promising for near-term electronic structure calculations, but for periodic solids their reach is limited by the cost of building a faithful second-quantized Hamiltonian, typically via DFT plus Wannierization or hand-fit tight-binding parameters. SlaKoNet addresses this by combining deep learning with the Slater-Koster tight-binding formalism to fit hopping and overlap parameters across 65 elements, enabling deterministic Hamiltonian construction for any crystal built from these elements. Here we couple a SlaKoNet model trained on JARVIS-TBmBJ with a Qiskit-based VQD algorithm, replacing costly Hamiltonian construction with a universal neural Hamiltonian generator. The resulting SlaKoNet-VQD workflow is structure-agnostic, differentiable, and suited to high-throughput bandstructure screening. We benchmark on silicon, recovering the full eight-band structure along the standard k-path with mean absolute deviation of 1.78 meV from exact diagonalization on a 3-qubit simulator, and extend to five conventional superconductors (Al, Ta, Nb, V, ZrN) with similar accuracy. We demonstrate execution on IBM Quantum hardware for a k-point ground-state calculation on aluminum (MAE ~0.37 eV). We further promote the Hamiltonian to a correlated Hubbard model solved via dynamical mean-field theory, recovering weakening correlations across group-5 metals and strong quasiparticle renormalization in La2CuO4, identifying the impurity problem as a natural quantum solver target. This pipeline enables high-throughput VQA benchmarking across the periodic table and gradient-based ansatz-Hamiltonian co-optimization for materials discovery. Web app: https://atomgpt.org/quantum.
