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

TL;DR

The paper addresses the high energy costs of generative AI and LLMs and explores how quantum computing could enhance efficiency across the AI lifecycle. It proposes a lifecycle-aligned catalog of quantum algorithms—ranging from data curation and encoding to training, fine-tuning, and deployment—that could yield energy savings and performance gains, including industry examples and open problems. By outlining near-term and long-term opportunities, the work highlights where quantum methods may realistically contribute and what hardware and architectural advances are required. The study serves as a roadmap for integrating quantum computing into energy-conscious AI development and deployment.

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.