Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality

被引:50
作者
Ajagekar, Akshay [1 ]
You, Fengqi [1 ,2 ]
机构
[1] Cornell Univ, Coll Engn, Syst Engn, Ithaca, NY 14853 USA
[2] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA
关键词
Quantum computing; Artificial intelligence; Chemistry; Renewable energy; Sustainability; Climate-neutrality; POWER-GENERATION; COMPUTATION; CHEMISTRY; ALGORITHMS;
D O I
10.1016/j.rser.2022.112493
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Transitioning from fossil fuels to renewable sources and developing sustainable energy materials for energy production and storage are critical factors in achieving climate neutrality. These can be realized through innovative strategies to provide viable, economically competitive, and scalable technologies ranging across various sectors. Quantum computing (QC) has the potential to revolutionize various domains of science and engineering, including macro-energy systems and sustainable energy materials design. Conventional approaches for renewable and sustainable energy systems solely rely on classical computing techniques that may not scale well with the increasing size and complexity of applications. Owing to the advancements in quantum hardware and algorithms, QC and quantum artificial intelligence make promising tools to handle renewable and sustainable energy systems even at larger scales. In this review, we discuss the prospects of QC for various areas of applications in energy sustainability to help address climate change. In addition to providing a brief background on the operations of quantum computers, the constituent segments of widely adopted QC-based techniques that improve the computational efficiency of quantum chemistry calculations for sustainable energy materials along with quantum artificial intelligence methods that can address complex optimization and machine learning problems arising in renewable energy systems are also introduced in this paper. We screen the presented quantum algorithms based on their performance on current quantum devices despite their promising potential. Furthermore, sustainable energy applications that may draw advantages from QC-based strategies are identified in this work while simultaneously setting realistic expectations over the potential improvements offered over classical techniques.
引用
收藏
页数:12
相关论文
共 141 条
[1]   Read the fine print [J].
Aaronson, Scott .
NATURE PHYSICS, 2015, 11 (04) :291-293
[2]  
Adedoyin A, 2018, ARXIV PREPRINT ARXIV
[3]   Adiabatic Quantum Computation Is Equivalent to Standard Quantum Computation [J].
Aharonov, Dorit ;
van Dam, Wim ;
Kempe, Julia ;
Landau, Zeph ;
Lloyd, Seth ;
Regev, Oded .
SIAM REVIEW, 2008, 50 (04) :755-787
[4]   Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities [J].
Ahmad, Tanveer ;
Zhang, Dongdong ;
Huang, Chao ;
Zhang, Hongcai ;
Dai, Ningyi ;
Song, Yonghua ;
Chen, Huanxin .
JOURNAL OF CLEANER PRODUCTION, 2021, 289
[5]   New frontiers of quantum computing in chemical engineering [J].
Ajagekar, Akshay ;
You, Fengqi .
KOREAN JOURNAL OF CHEMICAL ENGINEERING, 2022, 39 (04) :811-820
[6]   Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems [J].
Ajagekar, Akshay ;
You, Fengqi .
APPLIED ENERGY, 2021, 303
[7]   Quantum computing assisted deep learning for fault detection and diagnosis in industrial process systems [J].
Ajagekar, Akshay ;
You, Fengqi .
COMPUTERS & CHEMICAL ENGINEERING, 2020, 143
[8]   Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems [J].
Ajagekar, Akshay ;
Humble, Travis ;
You, Fengqi .
COMPUTERS & CHEMICAL ENGINEERING, 2020, 132
[9]   Quantum computing for energy systems optimization: Challenges and opportunities [J].
Ajagekar, Akshay ;
You, Fengqi .
ENERGY, 2019, 179 :76-89
[10]   Adiabatic quantum computation [J].
Albash, Tameem ;
Lidar, Daniel A. .
REVIEWS OF MODERN PHYSICS, 2018, 90 (01)