Accelerating Quantum Simulations of Materials Through Parameter and Ansatz Transfer Strategies

Abstract

Quantum computing offers a promising route for electronic-structure calculations, but practical condensed-matter applications often require solving large families of related Hamiltonians arising from $\mathbf{k}$-point sampling, compositional variation, defects, and changes in system size. Here, we develop strategies for transferring variational information between related Hamiltonians to accelerate quantum simulations. For Hamiltonians of the same embedded size, optimized variational parameters are transferred between related calculations. For different sizes, we introduce Augmented Ansatz Reuse for Target Initialization (AARTI), which transfers compatible Pauli-generator structure and optimized source parameters, then augments the ansatz with target-specific generators. Our workflow combines density functional theory, Wannier downfolding, embedded tight-binding Hamiltonians, variational quantum algorithms, and neural-network quantum states, with quantum-circuit simulations implemented using NVIDIA's CUDA-Q platform. Using Li$_x$CoO$_2$ as a representative battery cathode material, we study Hamiltonian families spanning multiple delithiation levels, dense $\mathbf{k}$-point meshes, and increasing sizes. Parameter and ansatz transfer substantially reduce the optimization steps required to solve these related Hamiltonians while maintaining the common target accuracy, with lower terminal errors in several cases. The calculated electronic-structure evolution with delithiation exhibits pronounced non-rigid-band behavior consistent with experiment. These results establish a framework for exploiting similarity among related Hamiltonians to improve the efficiency and scalability of quantum workflows for realistic condensed-matter systems.