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Updated: May 14, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Molecular Properties from Quantum Krylov Subspace Diagonalization.

Oumarou Oumarou¹, Pauline J Ollitrault^2,3, Cristian L Cortes^2,3

¹Covestro Deutschland AG, Leverkusen, Nordrhein-Westfalen 51373, Germany.

Journal of Chemical Theory and Computation

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces methods to efficiently calculate properties of quantum systems beyond ground states. It reduces measurement costs for quantum Krylov methods, enabling broader quantum simulations.

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Area of Science:

Quantum computing
Quantum chemistry
Computational physics

Background:

Quantum Krylov subspace diagonalization is key for quantum simulations.
Current methods primarily focus on ground-state energies, limiting broader applications.

Purpose of the Study:

To extend quantum Krylov methods for calculating excited states and molecular properties.
To reduce the measurement complexity for obtaining reduced density matrices.

Main Methods:

Derivation of analytical first-order derivatives for quantum Krylov methods.
Application of quantum signal processing to prepare Krylov eigenstates efficiently.
Development of constant-scaling measurement schemes for reduced density matrices.

Main Results:

Successful derivation of relaxed one- and two-particle reduced density matrices for Krylov eigenstates.
Demonstrated reduction in measurement scaling from quadratic to constant with respect to Krylov dimension D.
Validated approach by computing molecular nuclear gradients.

Conclusions:

The developed methods significantly enhance the efficiency of quantum Krylov simulations.
This work paves the way for more comprehensive quantum simulations of molecular and many-body systems.