Deterministic Preparation of Arbitrary Spin Eigenfunctions
2026-08-24 • Data Structures and Algorithms
Data Structures and Algorithms
AI summaryⓘ
The authors explore special quantum states called spin eigenfunctions, which are important in chemistry and physics. Previously, scientists could efficiently create a specific type called Dicke states, but it was unclear if other spin eigenfunctions could be prepared in a reliable way. The authors extended existing methods to successfully create these more general spin states using a mathematical framework involving branching paths and binary spin trees. They also developed classical algorithms to reconstruct these states from their descriptions.
quantum statesspin eigenfunctionsDicke statesquantum algorithmsbranching pathsbinary spin treesquantum chemistrymany-body physics
Authors
Wenxuan Tao, Jianan Wang, Fen Zuo
Abstract
Quantum states with conserved total spins, or spin eigenfunctions, are important for studying quantum chemistry and quantum manybody physics problems. A typical class of spin eigenfunctions are Dicke states, which attain maximal spins. While we already have many efficient quantum algorithms to prepare Dicke states, it is not yet clear if we could do so for arbitrary spin eigenfunctions deterministically. Generalizing Bärtschi and Eidenbenz's elegant algorithms for Dicke state preparation, we successfully prepare arbitrary spin eigenfunctions characterized by branching paths and binary spin trees. As a byproduct, we also develop the corresponding classical algorithms to reconstruct all these spin states.