2025/08/22 by Marco Majland, Rasmus Berg Jensen, Majland, Marco +9 · 1 voice
Computer Science · Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2508.16253
openalex publication_date 2025/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Quantum computation of vibrational properties of molecules is a promising platform to obtain computational advantages for computational chemistry. However, fault-tolerant quantum computations of vibrational properties remain a relatively unexplored field in quantum computing. In this work, we present different algorithms for efficient encodings of vibrational Hamiltonians using qubitization. Specifically, we investigate different encoding representations, high order tensor decomposition to obtain low rank approximations for the vibrational Hamiltonian, rectilinear and polyspherical coordinate systems, parallelization and grouping algorithms. To investigate the performance of the different methods, we perform benchmark computations for both small and large molecules with more than one hundred vibrational modes.