Entangling Intelligence: AI-Quantum Crossovers and Perspectives

The intersection of artificial intelligence (AI) and quantum physics has sparked significant advancements in both fields. This paper first reviews the application of AI techniques, particularly neural networks, to quantum simulation. We discuss key developments in representing quantum states, efficient sampling methods, and optimization techniques for ground states and dynamics. The review also explores how quantum principles enhances AI algorithms, including quantum-inspired neural architectures and physics-based optimization methods. We highlight challenges and open questions in this rapidly evolving interdisciplinary field, emphasizing the potential for future innovations that combine AI and quantum approaches to address complex problems in both domains.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/tps-isa62245.2024.00069
Citations
1
Primary Topic
Computability, Logic, AI Algorithms
Type
article
Field-Weighted Citation Impact
0.36
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article

Entangling Intelligence: AI-Quantum Crossovers and Perspectives

Di Luo, Zhuo Chen
1 citations
Computability, Logic, AI Algorithms
0.36
article

Entangling Intelligence: AI-Quantum Crossovers and Perspectives

Di Luo, Zhuo Chen
article en
1 citations

Abstract

The intersection of artificial intelligence (AI) and quantum physics has sparked significant advancements in both fields. This paper first reviews the application of AI techniques, particularly neural networks, to quantum simulation. We discuss key developments in representing quantum states, efficient sampling methods, and optimization techniques for ground states and dynamics. The review also explores how quantum principles enhances AI algorithms, including quantum-inspired neural architectures and physics-based optimization methods. We highlight challenges and open questions in this rapidly evolving interdisciplinary field, emphasizing the potential for future innovations that combine AI and quantum approaches to address complex problems in both domains.

Massachusetts Institute of Technology (US)
Openalex Percentile: Top 32%
Computability, Logic, AI Algorithms
0.36
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