Zhendong Li

Quantum computation offers significant potential for accelerating the simulation of molecules and materials through algorithms such as quantum phase estimation (QPE). However, the expected speedup in ground-state energy estimation depends critically on the ability to efficiently prepare an initial state with high overlap with the true ground state. For strongly correlated molecules such as iron-sulfur clusters, this overlap is demonstrated to decay exponentially with system size. To alleviate this problem, we introduce an efficient classical algorithm to find entanglement-minimized orbitals (EMOs) using spin-adapted matrix product states (MPS) with small bond dimensions. The EMO basis yields a more compact ground-state representation, significantly easing initial state preparation for challenging systems. Our algorithm improves initial state overlap by nearly an order of magnitude over prior orbital optimization approaches for an iron-sulfur cluster with four irons, and is scalable to larger systems with many unpaired electrons, including the P-cluster and FeMo-cofactor in nitrogenase with eight transition metal centers. For these systems, we achieve substantial enhancements on initial state overlap by factors of $O(10^2)$ and $O(10^5)$, respectively, compared to results obtained using localized orbitals. Our results show that initial state preparation for these challenging systems requires far fewer resources than prior estimates suggested.