Artificial intelligence (AI) for quantum and quantum for AI

Zhu, Yingzhao; Yu, Kefeng

doi:10.1007/s11082-023-04914-6

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Furthermore, AI was leveraged to enhance quantum computing (QC) by developing algorithms that improve the control of quantum systems, a notoriously difficult task. AI techniques provided new insights and solutions in quantum physics, thereby accelerating the discovery of quantum laws and applications (Zhu & Yu, 2023). In educational contexts, AI showed potential in generating synthetic datasets for physics concept inventories, such as the Force Concept Inventory (FCI), to study and improve student understanding of fundamental physics concepts.…”

Section: Advantages Of Using Ai In Physicsmentioning

confidence: 99%

“…Furthermore, while AI's integration with quantum computing has shown promise, controlling quantum systems remains a significant obstacle. AI techniques, although helpful, were not yet fully reliable for the automated control of complex quantum systems, posing a barrier to widespread QC application (Zhu & Yu, 2023). Similarly, while AI could generate synthetic datasets for educational research, such as the FCI, it often fails to simulate the variability in responses that would be expected from different student cohorts, limiting its effectiveness in creating realistic educational scenarios (Kieser et al, 2023).…”

Section: Disadvantages Of Using Ai In Physicsmentioning

confidence: 99%

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Utilizing AI for Physics Problem Solving: A Literature Review and ChatGPT Experience

Mustofa,

Bilad,

Grendis

2024

Jurnal. Kependidikan. Fisika

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The integration of artificial intelligence (AI) tools in physics education is gaining traction, driven by their potential to enhance learning experiences and outcomes. This study aims to investigate the use of AI tools, particularly ChatGPT, in solving physics problems and enhancing educational practices. Utilizing a systematic literature review following PRISMA guidelines, the research identifies current trends and practical applications of AI in physics education. The results indicate that AI tools effectively support lesson planning, introduce innovative teaching methodologies, and assist in solving complex physics problems, significantly enhancing problem-solving skills and personalized learning experiences. However, challenges such as inaccuracies in handling advanced content, the lack of useful visual aids, and the need for human intervention to ensure the completeness and accuracy of AI-generated content were noted. Personal experiences, supplemented by an interview with a thermodynamics lecturer, revealed that while ChatGPT can simplify complex concepts and improve comprehension, it could not replace the mentorship and nuanced feedback provided by human educators. The study concludes with recommendations for integrating AI tools into physics education, emphasizing the need for balanced integration with traditional teaching methods, improved AI literacy among educators and students, and future developments focusing on personalized learning and enhanced visualization capabilities. The findings demonstrate the transformative potential of AI in physics education and highlight the importance of addressing its limitations to maximize educational outcomes.

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Section: Advantages Of Using Ai In Physicsmentioning

confidence: 99%

Section: Disadvantages Of Using Ai In Physicsmentioning

confidence: 99%

Utilizing AI for Physics Problem Solving: A Literature Review and ChatGPT Experience

Mustofa,

Bilad,

Grendis

2024

Jurnal. Kependidikan. Fisika

View full text Add to dashboard Cite

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“…The ML-based techniques have been widely used in our daily lives such as in image recognition [4][5][6][7], advertising [8], social networking [9][10][11], engineering [12] and designing medicine [13]. In physical sciences like astrophysics [14,15], high energy physics [16,17], and biological physics [18][19][20], the ML has extensive application, especially in condensed matter, this method is used to identify phase transitions [21][22][23][24][25][26][27][28][29][30]. Application of the ML is still in a nascent stage in the condensed matter, and reproducing the well-known results is still a primary goal.…”

Section: Introductionmentioning

confidence: 99%

Study of the Berezinskii–Kosterlitz–Thouless transition: an unsupervised machine learning approach

Haldar,

Rahaman,

Kumar

2024

J. Phys.: Condens. Matter

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The Berezinskii-Kosterlitz-Thouless (BKT) transition in magnetic systems is an intriguing phenomenon, and estimating the BKT transition temperature is a long-standing problem. In this work, we explore anisotropic classical Heisenberg XY and XXZ models with ferromagnetic exchange on a square lattice and antiferromagnetic exchange on a triangular lattice using an unsupervised machine learning approach called principal component analysis (PCA). The earlier PCA studies of the BKT transition temperature ($T_{BKT}$) using the vorticities as input fail to give any conclusive results, whereas, in this work, we show that the proper analysis of the first principal component-temperature curve can estimate $T_{BKT}$ which is consistent with the existing literature. This analysis works well for the anisotropic classical Heisenberg with a ferromagnetic exchange on a square lattice and frustrated antiferromagnetic exchange on a triangular lattice. The classical anisotropic Heisenberg antiferromagnetic model on the triangular lattice has two close transitions: the $T_{BKT}$ and Ising-like phase transition for chirality at $T_c$, and it is difficult to separate these transition points. It is also noted that using the PCA method and manipulation of their first principal component not only makes the separation of transition points possible but also determines transition temperature.

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How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

Pregowska,

Roszkiewicz,

Osial

et al. 2024

Microscopy Res & Technique

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The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic‐precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft‐surface materials. In this paper, we focus on the potential for supporting SPM‐based measurements, with an emphasis on the application of AI‐based algorithms, especially Machine Learning‐based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure–property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI‐based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI‐QC‐based approach were also discussed. Finally, we outline a research path for improving AI‐QC‐powered SPM.Research Highlights Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai‐qc‐powered scanning probe microscopy.

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Artificial intelligence (AI) for quantum and quantum for AI

Cited by 7 publications

References 44 publications

Utilizing AI for Physics Problem Solving: A Literature Review and ChatGPT Experience

Utilizing AI for Physics Problem Solving: A Literature Review and ChatGPT Experience

Study of the Berezinskii–Kosterlitz–Thouless transition: an unsupervised machine learning approach

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

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