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Journal articles on the topic 'Data structure for quantum machine learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Zhao, Zhikuan, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, and Joseph F. Fitzsimons. "Smooth input preparation for quantum and quantum-inspired machine learning." Quantum Machine Intelligence 3 (April 26, 2021): 14. https://doi.org/10.1007/s42484-021-00045-x.

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Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoot

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Schuhmacher, Julian, Guglielmo Mazzola, Francesco Tacchino, et al. "Extending the reach of quantum computing for materials science with machine learning potentials." AIP Advances 12, no. 11 (2022): 115321. http://dx.doi.org/10.1063/5.0099469.

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Solving electronic structure problems represents a promising field of applications for quantum computers. Currently, much effort is spent in devising and optimizing quantum algorithms for near-term quantum processors, with the aim of outperforming classical counterparts on selected problem instances using limited quantum resources. These methods are still expected to feature a runtime preventing quantum simulations of large scale and bulk systems. In this work, we propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potenti

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Peters, Evan, and Maria Schuld. "Generalization despite overfitting in quantum machine learning models." Quantum 7 (December 20, 2023): 1210. http://dx.doi.org/10.22331/q-2023-12-20-1210.

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The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting training data. This phenomenon of benign overfitting has been studied for a variety of classical models with the goal of better understanding the mechanisms behind deep learning. Characterizing the phenomenon in the context of quantum machine learning might similarly improve our understanding of the relationship between overfitting, overparameterization, and generalization. In this work, we provide a characterization of

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Balakumar, Arvind. "Quantum K-means Clustering and Classical k Means Clustering For Chest Pain Classification Using Qiskit." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 945–48. http://dx.doi.org/10.22214/ijraset.2022.47484.

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Abstract: Quantum computing is an upcoming field of technology which has a broad scope of increasing the current technology in a robust manner. The application area Quantum computing is very huge which ranges from battery research, protein structure research to advance computing and security areas like cryptography, quantum internet, quantum machine learning and quantum cyber security. The quantum machine learning area seems to be the most interesting because of the computing capability of a real time quantum computer. With the quantum machine learning algorithm, classical algorithm, processin

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Chakraborty, Sanjay, Soharab Hossain Shaikh, Sudhindu Bikash Mandal, Ranjan Ghosh, and Amlan Chakrabarti. "A study and analysis of a discrete quantum walk-based hybrid clustering approach using d-regular bipartite graph and 1D lattice." International Journal of Quantum Information 17, no. 02 (2019): 1950016. http://dx.doi.org/10.1142/s0219749919500163.

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Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data clustering is an important tool for machine learning where quantum computing plays a vital role in its inherent speed up capability. In this paper, a hybrid quantum algorithm for data clustering (quantum walk-based hybrid clustering (QWBHC)) is introduced where one-dimensional discrete time quantum walks (DTQW) play the central role to update the positions of data points according to their probability distr

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Ozpolat, Zeynep, and Murat Karabatak. "Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification." Diagnostics 13, no. 6 (2023): 1099. http://dx.doi.org/10.3390/diagnostics13061099.

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The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods

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Christensen, Anders S., and O. Anatole von Lilienfeld. "Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties." CHIMIA International Journal for Chemistry 73, no. 12 (2019): 1028–31. http://dx.doi.org/10.2533/chimia.2019.1028.

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The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves

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Zuev, S. V. "Geometric properties of quantum entanglement and machine learning." Russian Technological Journal 11, no. 5 (2023): 19–33. http://dx.doi.org/10.32362/2500-316x-2023-11-5-19-33.

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Objectives. Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. This paper aims to propose and verify a method of such analysis based on the representation of data in the form of a quantum state, or, alternatively, in the form of a geometric object in a space allowing online machine learning.Methods. This paper uses Feynman formalism to represent quantum states and operations on them, the representation of quantum computing in the form of quantum circuits, geometric transformations, topological classification, as well

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Convy, Ian, William Huggins, Haoran Liao, and K. Birgitta Whaley. "Mutual information scaling for tensor network machine learning." Machine Learning: Science and Technology 3, no. 1 (2022): 015017. http://dx.doi.org/10.1088/2632-2153/ac44a9.

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Abstract Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs favoring different scaling patterns. We demonstrate here how a related correlation analysis can be applied to tensor network machine learning, and explore whether classical data possess correlation scaling patterns similar to those f

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Raubitzek, Sebastian, and Kevin Mallinger. "On the Applicability of Quantum Machine Learning." Entropy 25, no. 7 (2023): 992. http://dx.doi.org/10.3390/e25070992.

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In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations,

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Ghavasieh, A., and M. De Domenico. "Statistical physics of network structure and information dynamics." Journal of Physics: Complexity 3, no. 1 (2022): 011001. http://dx.doi.org/10.1088/2632-072x/ac457a.

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Abstract In the last two decades, network science has proven to be an invaluable tool for the analysis of empirical systems across a wide spectrum of disciplines, with applications to data structures admitting a representation in terms of complex networks. On the one hand, especially in the last decade, an increasing number of applications based on geometric deep learning have been developed to exploit, at the same time, the rich information content of a complex network and the learning power of deep architectures, highlighting the potential of techniques at the edge between applied math and c

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Yudertha, Andreo, and Riski Dwimalida Putri. "Mapping Machine Learning Trends in Chemistry Research using LLM with Multi-Turn Prompting." SISTEMASI 14, no. 2 (2025): 587. https://doi.org/10.32520/stmsi.v14i2.4961.

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A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles fo

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Temitope Oluwatosin Fatunmbi. "Advanced frameworks for fraud detection leveraging quantum machine learning and data science in fintech ecosystems." World Journal of Advanced Engineering Technology and Sciences 12, no. 1 (2024): 495–513. https://doi.org/10.30574/wjaets.2024.12.1.0057.

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The rapid expansion of the fintech sector has brought with it an increasing demand for robust and sophisticated fraud detection systems capable of managing large volumes of financial transactions. Conventional machine learning (ML) approaches, while effective, often encounter limitations in terms of computational efficiency and the ability to model complex, high-dimensional data structures. Recent advancements in quantum computing have given rise to a promising paradigm known as quantum machine learning (QML), which leverages quantum mechanical principles to solve problems that are computation

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Jeng, Mingyoung, Alvir Nobel, Vinayak Jha, et al. "Leveraging Data Locality in Quantum Convolutional Classifiers." Entropy 26, no. 6 (2024): 461. http://dx.doi.org/10.3390/e26060461.

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Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than a fully connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multifeatures in multidimensional data. In this paper, we present an multid

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Li, Yuan, and Duan Huang. "Boosted Binary Quantum Classifier via Graphical Kernel." Entropy 25, no. 6 (2023): 870. http://dx.doi.org/10.3390/e25060870.

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In terms of the logical structure of data in machine learning (ML), we apply a novel graphical encoding method in quantum computing to build the mapping between feature space of sample data and two-level nested graph state that presents a kind of multi-partite entanglement state. By implementing swap-test circuit on the graphical training states, a binary quantum classifier to large-scale test states is effectively realized in this paper. In addition, for the error classification caused by noise, we further explored the subsequent processing scheme by adjusting the weights so that a strong cla

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Raghavender, Maddali. "Quantum Machine Learning for Ultra-Fast Query Execution in High-Dimensional SQL Data Systems." International Journal of Leading Research Publication 3, no. 4 (2022): 1–13. https://doi.org/10.5281/zenodo.15107548.

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The new Quantum Machine Learning (QML) paradigm for highly efficient query execution in high-dimensional SQL data systems and Conventional database query execution is plagued by performance bottlenecks because of the explosive nature of structured data and intricate query optimization issues. The new QML-based methodology uses quantum algorithms to accelerate query processing by exploiting parallel computation, quantum-aided indexing, and probabilistic data access. With the incorporation of quantum-enhanced optimization methods, the framework achieves remarkable query execution time reduction,

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Srikumar, Maiyuren, Charles D. Hill, and Lloyd C. L. Hollenberg. "Clustering and enhanced classification using a hybrid quantum autoencoder." Quantum Science and Technology 7, no. 1 (2021): 015020. http://dx.doi.org/10.1088/2058-9565/ac3c53.

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Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically

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Hossain, Forhad, Kamrul Hasan, Al Amin, and Shakik Mahmud. "Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions." Journal of Technologies Information and Communication 4, no. 1 (2024): 32222. https://doi.org/10.55267/rtic/15824.

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The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and machine learning to enhance cybersecurity across multiple dimensions. The research employs a structured methodology, beginning with the integration of Quantum Key Distribution (QKD) for secure key exchange and progressing through the deployment of Quantum Neural Networks (QNN) and Quantum Support Vector Machines (QSVM) for anomaly detection and ad

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Surdu, Vasile-Adrian, and Romuald Győrgy. "X-ray Diffraction Data Analysis by Machine Learning Methods—A Review." Applied Sciences 13, no. 17 (2023): 9992. http://dx.doi.org/10.3390/app13179992.

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X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to an

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Parker, Amanda J., and Amanda S. Barnard. "Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks." Nanoscale Horizons 6, no. 3 (2021): 277–82. http://dx.doi.org/10.1039/d0nh00637h.

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Scientific intuition can help anticipate the outcome of experiments, but machine learning based on data does not always support these assumptions. A direct comparison of human intelligence (HI) and AI suggests domain knowledge is not always enough.

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Tretiak, Sergei. "(Invited) Machine Learning in Chemistry: Reactive Force Fields for Carbon Structure Formation." ECS Meeting Abstracts MA2024-01, no. 9 (2024): 881. http://dx.doi.org/10.1149/ma2024-019881mtgabs.

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Machine learning (ML) became a premier tool for modeling chemical processes and materials properties. ML interatomic potentials have become an efficient alternative to computationally expensive quantum chemistry simulations. In the case of reactive chemistry designing high-quality training data sets is crucial to overall model accuracy. To address this challenge, we develop a general reactive ML interatomic potential through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting model is then applied to study five distinct conden

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Vlasic, Andrew, and Anh Pham. "Understanding the mapping of encode data through an implementation of quantum topological analysis." Quantum Information and Computation 23, no. 13&14 (2023): 1091–104. http://dx.doi.org/10.26421/qic23.13-14-2.

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A potential advantage of quantum machine learning stems from the ability of encoding classical data into high dimensional complex Hilbert space using quantum circuits. Recent studies exhibit that not all encoding methods are the same when representing classical data since certain parameterized circuit structures are more expressive than the others. In this study, we show the difference in encoding techniques can be visualized by investigating the topology of the data embedded in complex Hilbert space. The technique for visualization is a hybrid quantum based topological analysis which uses sim

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Alonge, Enoch Oluwabusayo, Nsisong Louis Eyo-Udo, Bright Chibunna Ubanadu, Andrew Ifesinachi Daraojimba, Emmanuel Damilare Balogun, and Kolade Olusola Ogunsola. "Enhancing Data Security with Machine Learning: A Study on Fraud Detection Algorithms." Journal of Frontiers in Multidisciplinary Research 2, no. 1 (2021): 19–31. https://doi.org/10.54660/.ijfmr.2021.2.1.19-31.

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As cyber threats and financial fraud continue to evolve, organizations are increasingly leveraging machine learning (ML) to enhance data security and detect fraudulent activities in real time. Traditional rule-based fraud detection systems struggle to adapt to sophisticated fraud patterns, necessitating the adoption of ML-driven approaches. This paper explores how machine learning algorithms improve fraud detection by analyzing large datasets, identifying anomalies, and mitigating security risks with greater accuracy and efficiency. The study examines various machine learning techniques employ

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Kallem, Bharat Kumar Reddy. "The Role of AI and Machine Learning in Financial Data Engineering." European Journal of Computer Science and Information Technology 13, no. 12 (2025): 75–84. https://doi.org/10.37745/ejcsit.2013/vol13n127584.

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The integration of artificial intelligence and machine learning technologies is fundamentally reshaping financial data engineering practices, enabling institutions to process complex structured and unstructured data while deriving more accurate predictive insights. This comprehensive exploration examines how AI-powered systems have transformed data processing efficiency, enhanced decision accuracy, and reduced regulatory compliance costs across the financial sector. The discussion progresses through the integration of AI/ML models into financial data pipelines, highlighting improvements in pre

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POLYAKOV, IGOR V., ALEXANDRA V. KRIVITSKAYA, and MARIA G. KHRENOVA. "STRUCTURE AND DYNAMICS OF THE ENZYME-SUBSTRATE COMPLEX OF N-ACETYLASPARTYLGLUTAMATE SYNTHASE ACCORDING TO COMPUTER SIMULATION DATA." Lomonosov chemistry journal 65, no. 4, 2024 (2024): 284–91. http://dx.doi.org/10.55959/msu0579-9384-2-2024-65-4-284-291.

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N-acetylaspartilglutamate is the most common dipeptide in brain cells, which is synthesized using the enzyme N-acetylaspartilglutamate synthase. Herein we utilize bioinformatics methods to predict the protein structure from the primary sequence of the coding gene, classical molecular dynamics to obtain a stable protein complex with N-acetylaspartate and glutamate ligands within the trajectory, as well as machine learning methods to analyze, describe and select potential reactive and non-reactive conformations of the model system describing the enzyme-substrate complex. Molecular dynamics simul

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Pomarico, Domenico, Annarita Fanizzi, Nicola Amoroso, et al. "A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case." Mathematics 9, no. 4 (2021): 410. http://dx.doi.org/10.3390/math9040410.

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Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier a

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Chen, Yiwei, Yu Pan, Guofeng Zhang, and Shuming Cheng. "Detecting quantum entanglement with unsupervised learning." Quantum Science and Technology 7, no. 1 (2021): 015005. http://dx.doi.org/10.1088/2058-9565/ac310f.

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Abstract Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed

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28

Pang, Shan, Xinyi Yang, and Xiaofeng Zhang. "Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure." International Journal of Aerospace Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1329561.

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A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM) was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algori

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Jorner, Kjell. "Putting Chemical Knowledge to Work in Machine Learning for Reactivity." CHIMIA 77, no. 1/2 (2023): 22. http://dx.doi.org/10.2533/chimia.2023.22.

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Machine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances in computer science have resulted in deep neural networks that can learn directly from the molecular structure. Neural networks are a good choice when large amounts of data are available. However, many datasets in chemistry are small, and models utilizing chemical knowledge are required for good performance. Adding chemical knowledge can be achieved either by adding more information about the molecules or by adjusting the mo

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Dağıstanlı, Hamdi. "Strain Effect and Artificial Inteligence Applications on Electronic Band Structure of MoS2." Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi 4, no. 1 (2025): 38–50. https://doi.org/10.69560/cujast.1618074.

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Ab initio density functional theory (DFT) calculations have been used to determine the band gap of the 2D layered material MoS2 under uniaxial strain. This involves finding optimised lattice parameters and calculating the electronic band structure. We also note that by applying strains ranging from -15% to 15%, a wide range of band gaps can be obtained to study the behaviour of the semimetal and metal. The results gained are applied to machine learning. Initially, PR, which is polynomial regression, is a machine learning method that it could be studied with numpy, sklearn and scipy modules, an

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Amisha G, Govindarao Kamala, Chandrika D, et al. "Quantitative Structure-Activity Relationship (QSAR) in Drug Discovery and Development." Journal of Pharma Insights and Research 3, no. 1 (2025): 241–51. https://doi.org/10.69613/d091zy53.

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Quantitative structure-activity relationship (QSAR) analysis represents a cornerstone approach in modern drug discovery and development. QSAR methodologies establish mathematical correlations between molecular structures and their biological activities, enabling the prediction of compound properties and behaviors. Recent advances in computational capabilities, coupled with the emergence of sophisticated machine learning algorithms, have revolutionized traditional QSAR approaches. The integration of deep learning architectures, including graph neural networks and convolutional neural networks,

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Beer, Kerstin, Megha Khosla, Julius Köhler, Tobias J. Osborne, and Tianqi Zhao. "Quantum machine learning of graph-structured data." Physical Review A 108, no. 1 (2023). http://dx.doi.org/10.1103/physreva.108.012410.

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Ray, Shawn. "Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence." Sakarya University Journal of Computer and Information Sciences, December 5, 2024. https://doi.org/10.35377/saucis...1564497.

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This paper introduces an innovative theoretical framework for quantum-inspired data embeddings, grounded in foundational concepts of quantum mechanics such as superposition and entanglement. This approach aims to advance semi-supervised learning in contexts characterized by limited labeled data by enabling more intricate and expressive embeddings that capture the underlying structure of the data effectively. Grounded in foundational quantum mechanics concepts such as superposition and entanglement, this approach redefines data representation by enabling more intricate and expressive embeddings

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Rapp, Frederic, David A. Kreplin, Marco F. Huber, and Marco Roth. "Reinforcement learning-based architecture search for quantum machine learning." Machine Learning: Science and Technology, January 28, 2025. https://doi.org/10.1088/2632-2153/adaf75.

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Abstract Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting model, they are often chosen heuristically. In this work, we present a approach using reinforcement learning techniques to generate problem-specific encoding circuits to improve the performance of quantum machine learning models. &#xD;By specifically using a model-based reinforcement learning algorithm, we reduce the number of necessary circuit evaluations during the

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Lewis, James P., Pengju Ren, Xiaodong Wen, Yongwang Li, and Guanhua Chen. "Machine learning meets quantum mechanics in catalysis." Frontiers in Quantum Science and Technology 2 (August 31, 2023). http://dx.doi.org/10.3389/frqst.2023.1232903.

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Over the past decade many researchers have applied machine learning algorithms with computational chemistry and materials science tools to explore properties of catalysts. There is a rapid increase in publications demonstrating the use of machine learning for rational catalyst design. In our perspective, targeted tools for rational catalyst design will continue to make significant contributions. However, the community should focus on developing high-throughput simulation tools that utilize molecular dynamics capabilities for thorough exploration of the complex potential energy surfaces that ex

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Ullah, Arif, Yuxinxin Chen, and Pavlo O. Dral. "Molecular quantum chemical data sets and databases for machine learning potentials." Machine Learning: Science and Technology, November 5, 2024. http://dx.doi.org/10.1088/2632-2153/ad8f13.

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Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for de

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Siebenmorgen, Till, Filipe Menezes, Sabrina Benassou, et al. "MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery." Nature Computational Science, May 10, 2024. http://dx.doi.org/10.1038/s43588-024-00627-2.

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AbstractLarge language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule–ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein–ligand complexes with extensive validation of experimental data. Starting from the existing experimenta

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Diao, Enhu, Yurong He, Xuhong Liu, et al. "First principles data-driven potentials for prediction of iron carbide clusters." Frontiers in Quantum Science and Technology 2 (May 25, 2023). http://dx.doi.org/10.3389/frqst.2023.1190522.

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Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structur

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Gili, Kaitlin, Guillermo Alonso, and Maria Schuld. "An inductive bias from quantum mechanics: learning order effects with non-commuting measurements." Quantum Machine Intelligence 6, no. 2 (2024). http://dx.doi.org/10.1007/s42484-024-00200-0.

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AbstractThere are two major approaches to building good machine learning algorithms: feeding lots of data into large models or picking a model class with an “inductive bias” that suits the structure of the data. When taking the second approach as a starting point to design quantum algorithms for machine learning, it is important to understand how mathematical structures in quantum mechanics can lead to useful inductive biases in quantum models. In this work, we bring a collection of theoretical evidence from the quantum cognition literature to the field of quantum machine learning to investiga

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Coates, Tom, Alexander M. Kasprzyk, and Sara Veneziale. "Machine learning the dimension of a Fano variety." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-41157-1.

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AbstractFano varieties are basic building blocks in geometry – they are ‘atomic pieces’ of mathematical shapes. Recent progress in the classification of Fano varieties involves analysing an invariant called the quantum period. This is a sequence of integers which gives a numerical fingerprint for a Fano variety. It is conjectured that a Fano variety is uniquely determined by its quantum period. If this is true, one should be able to recover geometric properties of a Fano variety directly from its quantum period. We apply machine learning to the question: does the quantum period of X know the d

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Wach, Noah L., Manuel S. Rudolph, Fred Jendrzejewski, and Sebastian Schmitt. "Data re-uploading with a single qudit." Quantum Machine Intelligence 5, no. 2 (2023). http://dx.doi.org/10.1007/s42484-023-00125-0.

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AbstractQuantum two-level systems, i.e., qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantum systems constitute a promising alternative and are increasingly explored in theory and practice. Here, we explore the capabilities of multi-level quantum systems, so-called qudits, for their use in a quantum machine learning context. We formulate classification and regression problems with the data re-uploading approach and demonstrate that a quantum circuit operating on a single qudit is able to successfu

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SURESH, ABHINAV, Henning Schloemer, Baran Hashemi, and Annabelle Bohrdt. "Interpretable correlator Transformer for image-like quantum matter data." Machine Learning: Science and Technology, March 13, 2025. https://doi.org/10.1088/2632-2153/adc071.

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Abstract Due to their inherent capabilities of capturing non-local dependencies, Transformer neural networks have quickly been established as the paradigmatic architecture for large language models and image processing. Next to these traditional applications, machine learning methods have also been demonstrated to be versatile tools in the analysis of image-like data of quantum phases of matter, e.g. given snapshots of many-body wave functions obtained in ultracold atom experiments. While local correlation structures in image-like data of physical systems can reliably be detected, identifying

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Ran, Runheng, and Haozhen Situ. "Investigating the Generalization Ability of Parameterized Quantum Circuits with Hierarchical Structures." Artificial Intelligence Evolution, May 14, 2021, 11–22. http://dx.doi.org/10.37256/aie.212021826.

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Quantum computing provides prospects for improving machine learning, which are mainly achieved through two aspects, one is to accelerate the calculation, and the other is to improve the performance of the model. As an important feature of machine learning models, generalization ability characterizes models' ability to predict unknown data. Aiming at the question of whether the quantum machine learning model provides reliable generalization ability, quantum circuits with hierarchical structures are explored to classify classical data as well as quantum state data. We also compare three differen

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Pierre Lourenço, Maicon, Mosayeb Naseri, Lizandra Barrios Herrera, et al. "Quantum Active Learning for Structural Determination of Doped Nanoparticles - A Case Study of 4Al@Si11." Journal of the Brazilian Chemical Society, 2025. https://doi.org/10.21577/0103-5053.20250054.

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Active learning (AL) has been widely applied in chemistry and materials science. In this work, we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by Density Functional Theory (DFT) or Density Functional Based Tight Binding (DFTB) and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/ Agent for Material Design and Discovery (QMLMat

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Corcione, Emilio, Fabian Jakob, Lukas Wagner, et al. "Machine learning enhanced evaluation of semiconductor quantum dots." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-54615-7.

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AbstractA key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properti

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Aikebaier, Faluke, Teemu Ojanen, and Jose Lado. "Machine learning the Kondo entanglement cloud from local measurements." November 9, 2023. https://doi.org/10.5281/zenodo.10090477.

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This portfolio contains code and data regarding the paper "Machine learning the Kondo entanglement cloud from local measurements". Abstract of the paper:A quantum coherent screening cloud around a magnetic impurity in metallic systems is the hallmark of the antiferromagnetic Kondo effect. Despite the central role of the Kondo effect in quantum materials, the structure of quantum correlations of the screening cloud has defied direct observations. In this work, we introduce a machine-learning algorithm that allows to spatially map the entangled electronic modes in the vicinity of the impuri

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Deng Xiang-Wen, Wu Li-Yuan, Zhao Rui, Wang Jia-Ou, and Zhao Li-Na. "Application and Prospect of Machine Learning in Photoelectron Spectroscopy." Acta Physica Sinica, 2024, 0. http://dx.doi.org/10.7498/aps.73.20240957.

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Photoelectron spectroscopy serves as a prevalent characterization technique within the realm of material science. Specifically, angle-resolved photoelectron spectroscopy (ARPES) provides a direct method for determining the energy-momentum dispersion relationship and Fermi surface structure of electrons within a material system. This makes ARPES a potent tool for the investigation of many-body interactions and correlated quantum materials. The field of photoelectron spectroscopy has seen continuous advancements, with the emergence of technologies such as time-resolved ARPES and nano-ARPES. Conc

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Gupta, Riddhi S., Carolyn E. Wood, Teyl Engstrom, Jason D. Pole, and Sally Shrapnel. "A systematic review of quantum machine learning for digital health." npj Digital Medicine 8, no. 1 (2025). https://doi.org/10.1038/s41746-025-01597-z.

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Abstract The growth in digitization of health data provides opportunities for using algorithmic techniques for data analysis. This systematic review assesses whether quantum machine learning (QML) algorithms outperform existing classical methods for clinical decisioning or health service delivery. Included studies use electronic health/medical records, or reasonable proxy data, and QML algorithms designed for quantum computing hardware. Databases PubMed, Embase, IEEE, Scopus, and preprint server arXiv were searched for studies dated 01/01/2015–10/06/2024. Of an initial 4915 studies, 169 were e

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Skolik, Andrea, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, and Vedran Dunjko. "Equivariant quantum circuits for learning on weighted graphs." npj Quantum Information 9, no. 1 (2023). http://dx.doi.org/10.1038/s41534-023-00710-y.

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AbstractVariational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm. In quantum machine learning (QML), however, the literature on ansatzes that are motivated by the training data structure is scarce. In this work, we introduce an ansatz for learning tasks on weighted graphs that respects an important graph symmetry, namely equivariance under

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Skolik, Andrea, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, and Vedran Dunjko. "Equivariant quantum circuits for learning on weighted graphs." npj Quantum Information 9, no. 47 (2023). https://doi.org/10.1038/s41534-023-00710-y.

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Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm. In quantum machine learning (QML), however, the literature on ansatzes that are motivated by the training data structure is scarce. In this work, we introduce an ansatz for learning tasks on weighted graphs that respects an important graph symmetry, namely equivariance under node pe

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