Size consistent excited states via algorithmic transformations between variational principles

TL;DR

This paper addresses size inconsistency in excited state variational principles and introduces a transformation-based approach to improve size consistency, state selectivity, and compatibility with quantum Monte Carlo methods.

Contribution

It develops a novel method transforming between variational principles to achieve size consistency and state selectivity in excited state calculations.

Findings

The proposed approach is size consistent and state selective.

Numerical examples confirm the method's effectiveness.

The approach enhances the black box applicability of quantum Monte Carlo for excited states.

Abstract

We demonstrate that a broad class of excited state variational principles is not size consistent. In light of this difficulty, we develop and test an approach to excited state optimization that transforms between variational principles in order to achieve state selectivity, size consistency, and compatibility with quantum Monte Carlo. To complement our formal analysis, we provide numerical examples that confirm these properties and demonstrate how they contribute to a more black box approach to excited states in quantum Monte Carlo.