Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

IF 13 Q1 ENERGY & FUELS Advances in Applied Energy Pub Date : 2024-05-11 DOI:10.1016/j.adapen.2024.100179
Akshay Ajagekar , Fengqi You
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引用次数: 0

Abstract

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

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变式量子电路学习为人工智能数据中心能源控制和去碳化提供稳健优化
随着对人工智能(AI)模型和应用的需求不断增长,处理 AI 工作负载的数据中心正经历着能耗和相关碳足迹的上升。本研究提出了一种基于变量子计算的鲁棒优化(VQC-RO)框架,用于大规模数据中心的控制和能源管理,以应对计算挑战并克服传统的基于模型和无模型策略的局限性。VQC-RO 框架将变分量子电路 (VQC) 与经典优化相结合,实现了对人工智能数据中心能源系统的高效和不确定性感知控制。在噪声中量子(NISQ)设备上执行的量子算法被用于通过 Q-learning 训练的值函数估计,从而提出了一个具有不确定系数的鲁棒优化问题。基于量子计算的稳健控制策略旨在解决与天气条件和可再生能源发电相关的不确定性问题,同时优化人工智能数据中心的能源消耗。这项工作还概述了在美国多个人工智能数据中心地点进行的计算实验,以分析与拟议的基于量子计算的鲁棒控制框架相关的电力消耗和碳排放水平的降低情况。这项工作为高能效和可持续的数据中心运营提供了一种新方法,有望将处理人工智能工作负载的大型数据中心的碳排放和能耗分别降低 9.8% 和 12.5%。
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来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
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