An Integrated DFT-Wannier-Quantum Embedding Pipeline for Strongly Correlated Materials: Scaling Benchmarks in Li-hBN

• URL: http://arxiv.org/abs/2607.23590v1
• Date: Sun, 26 Jul 2026 10:40:27 GMT
• Title: An Integrated DFT-Wannier-Quantum Embedding Pipeline for Strongly Correlated Materials: Scaling Benchmarks in Li-hBN
• Abstract summary: We present an end-to-end computational pipeline for predicting strongly correlated materials.<n>We use DFT geometry relaxation, non-self-consistent field calculations, and Wannier-based orbital localization.<n>This study provides a rigorous performance baseline for the DFT-to-ADAPT-VQE workflow.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The seamless integration of Density Functional Theory (DFT) with quantum variational algorithms is essential for the predictive simulation of strongly correlated materials. In this work, we present an end-to-end computational pipeline - comprising DFT geometry relaxation, non-self-consistent field (NSCF) calculations, and Wannier-based orbital localization - to prepare active-space Hamiltonians for quantum embedding. We utilize the Adaptive Variational Quantum Eigensolver (ADAPT-VQE) framework, significantly enhanced by a Greedy-Operator Commutativity Partitioning (GOCP) approach and a Taylor-expanded O(5) operator evolution strategy to efficiently manage the exponential scaling of the Hilbert space. We demonstrate this framework through a systematic benchmark study of Li-hBN, mapping the system onto qubit registers and investigating the convergence behavior as the active space is expanded from 8 to 14 spatial orbitals. Our results quantify the relationship between active-space size and computational demand, identifying a critical "scaling wall" where classical simulation costs transition from manageable to intractable. This study provides a rigorous performance baseline for the DFT-to-ADAPT-VQE workflow and offers empirical insights into the memory and processing limits currently facing hybrid quantum-classical architectures using advanced co-processing strategies.

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