# Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms

## TL;DR

This paper explores how quantum computing algorithms could improve energy efficiency in the development and deployment of large language models, addressing sustainability concerns in AI.

## Contribution

It provides a detailed analysis of potential quantum algorithm applications across the lifecycle of large language models to enhance energy efficiency and sustainability.

## Key findings

- Quantum algorithms may reduce energy consumption in AI training and inference.
- Identification of open research problems in quantum-enhanced AI.
- Discussion of industry applications for energy-efficient AI.

## Abstract

Research and usage of artificial intelligence, particularly generative and large language models, have rapidly progressed over the last years. This has, however, given rise to issues due to high energy consumption. While quantum computing is not (yet) mainstream, its intersection with machine learning is especially promising, and the technology could alleviate some of these energy challenges. In this perspective article, we break down the lifecycle stages of large language models and discuss relevant enhancements based on quantum algorithms that may aid energy efficiency and sustainability, including industry application examples and open research problems.