A Joint Quantum Computing, Neural Network and Embedding Theory Approach for the Derivation of the Universal Functional

TL;DR

This paper presents a novel method combining quantum computing, neural networks, and embedding theory to derive a universal functional for quantum simulations, potentially enabling more efficient and reusable quantum chemistry calculations.

Contribution

It introduces a deep neural network trained with quantum algorithms and embedding techniques to derive a universal functional applicable across multiple quantum systems.

Findings

The approach leverages fragment-bath systems to expand Hamiltonian applicability.

The derived functional can be reused for systems with identical embedded interactions.

Potential for cumulative quantum advantage in quantum chemistry and condensed matter physics.

Abstract

We introduce a novel approach that exploits the intersection of quantum computing, machine learning and reduced density matrix functional theory to leverage the potential of quantum computing to improve simulations of interacting quantum particles. Our method focuses on obtaining the universal functional using a deep neural network trained with quantum algorithms. We also use fragment-bath systems defined by density matrix embedding theory to strengthen our approach by substantially expanding the space of Hamiltonians for which the obtained functional can be applied without the need for additional quantum resources. Given the fact that once obtained, the same universal functional can be reused for any system where the interactions within the embedded fragment are identical, our work demonstrates a way to potentially achieve a cumulative quantum advantage within quantum computing applications for quantum chemistry and condensed matter physics.