Quantum Algorithm for Estimating Gibbs Free Energy and Entropy via Energy Derivatives

TL;DR

This paper presents a quantum algorithm for estimating vibrational entropy by leveraging energy derivatives, promising faster computation of thermodynamic properties than classical methods under certain conditions.

Contribution

The paper introduces a novel quantum algorithm that estimates vibrational entropy using energy derivatives and quantum linear systems, improving efficiency with prior knowledge.

Findings

Quantum algorithm estimates vibrational entropy faster than classical methods.

Query complexity scales with the partition function, condition number, and temperature.

Prior knowledge reduces the number of queries needed significantly.

Abstract

Estimating vibrational entropy is a significant challenge in thermodynamics and statistical mechanics due to its reliance on quantum mechanical properties. This paper introduces a quantum algorithm designed to estimate vibrational entropy via energy derivatives. Our approach block encodes the exact expression for the second derivative of the energy and uses quantum linear systems algorithms to deal with the reciprocal powers of the gaps that appear in the expression. We further show that if prior knowledge about the values of the second derivative is used then our algorithm can $\epsilon$-approximate the entropy using a number of queries that scales with the condition number $\kappa$, the temperature $T$, error tolerance $\epsilon$ and an analogue of the partition function $\mathcal{Z}$, as $\widetilde{O}\left(\frac{\mathcal{Z}\kappa^2 }{\epsilon T}\right)$. We show that if sufficient prior knowledge is given about the second derivative then the query scales quadratically better than these results. This shows that, under reasonable assumptions of the temperature and a quantum computer can be used to compute the vibrational contributions to the entropy faster than analogous classical algorithms would be capable of. Our findings highlight the potential of quantum algorithms to enhance the prediction of thermodynamic properties, paving the way for advancements in fields such as material science, molecular biology, and chemical engineering.