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Related papers: Machine Learning Mutation-Acyclicity of Quivers

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Several methods are available to compute the anharmonicity in semi-rigid molecules. However, such methods are not routinely employed yet because of their large computational cost, especially for large molecules. The potential energy surface…

Chemical Physics · Physics 2020-10-26 Julien Lam , Saleh Abdul-Al , Abdul-Rahman Allouche

Machine learning (ML) can process large sets of data generated from complex systems, which is ideal for classification tasks as often appeared in critical phenomena. Meanwhile ML techniques have been found effective in detecting critical…

Computational Physics · Physics 2024-05-07 Shen Jianmin , Wang Shanshan , Li Wei , Xu Dian , Yang Yuxiang , Wang Yanyang , Gao Feng , Zhu Yueying , Tuo Kui

Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to…

Quantum Physics · Physics 2025-06-11 Kamila Zaman , Alberto Marchisio , Muhammad Abdullah Hanif , Muhammad Shafique

Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current noisy intermediate-scale quantum devices. This paper performs…

In machine learning (ML) workflows, determining the invariance qualities of an ML model is a common testing procedure. Traditionally, invariance qualities are evaluated using simple formula-based scores, e.g., accuracy. In this paper, we…

Machine Learning · Computer Science 2022-07-04 Zukang Liao , Pengfei Zhang , Min Chen

Quantum Machine Learning (QML) represents a promising frontier at the intersection of quantum computing and artificial intelligence, aiming to leverage quantum computational advantages to enhance data-driven tasks. This review explores the…

Machine Learning · Computer Science 2025-07-14 Samarth Kashyap , Rohit K Ramakrishnan , Kumari Jyoti , Apoorva D Patel

Recent advancements in quantum computing (QC) and machine learning (ML) have garnered significant attention, leading to substantial efforts toward the development of quantum machine learning (QML) algorithms to address a variety of complex…

Quantum Physics · Physics 2024-12-18 Samuel Yen-Chi Chen

The current revolution in the field of machine learning (ML) is leading to many interesting developments in a wide range of areas, including fluid mechanics. Here we review recent and emerging possibilities in the context of predictions,…

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Machine Learning (ML) has been a foundational topic in artificial intelligence (AI), providing both theoretical groundwork and practical tools for its exciting advancements. From ResNet for visual recognition to Transformer for…

Machine Learning · Computer Science 2025-12-30 Zhuo Huang

Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to…

We give an explicit description of the mutation classes of quivers of type \tilde{A}_n. Furthermore, we provide a complete classification of cluster tilted algebras of type \tilde{A}_n up to derived equivalence. We show that the bounded…

Representation Theory · Mathematics 2012-02-15 Janine Bastian

We consider quivers/skew-symmetric matrices under the action of mutation (in the cluster algebra sense). We classify those which are isomorphic to their own mutation via a cycle permuting all the vertices, and give families of quivers which…

Combinatorics · Mathematics 2020-12-21 Allan P. Fordy , Bethany Marsh

Quantum machine learning (QML) has emerged as a promising area of research for enhancing the performance of classical machine learning systems by leveraging quantum computational principles. However, practical deployment of QML remains…

Quantum Physics · Physics 2025-10-21 Amena Khatun , Muhammad Usman

Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of…

Statistical Mechanics · Physics 2017-12-06 Pedro Ponte , Roger G. Melko

In recent years, many scholars praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models (ABM). To get a more comprehensive understanding of these possibilities, we…

Theoretical Economics · Economics 2020-03-27 Johannes Dahlke , Kristina Bogner , Matthias Mueller , Thomas Berger , Andreas Pyka , Bernd Ebersberger

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related…

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases,…

Machine Learning · Computer Science 2026-03-27 Michelangelo Domina , Joseph William Abbott , Paolo Pegolo , Filippo Bigi , Michele Ceriotti

Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC).…

Machine Learning · Computer Science 2024-11-14 Jun Qi , Chao-Han Yang , Samuel Yen-Chi Chen , Pin-Yu Chen , Hector Zenil , Jesper Tegner

The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the importance of training models without human supervision, while…

Machine Learning · Computer Science 2025-05-26 Alexey Boldyrev , Fedor Ratnikov , Andrey Shevelev

Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space.…

Machine Learning · Computer Science 2023-03-23 Phong C. H. Nguyen , Joseph B. Choi , H. S. Udaykumar , Stephen Baek
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