[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124908-en":3,"doc-seo-124908-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124908,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Quantum Simulation of Physical and Chemical Systems Using Noisy Quantum Computers and Machine Learning - Dissertation","Quantum hardware has initiated a new computing era, yet present devices still lack the maturity needed for consistently reliable results. At the same time, quantum processors are already applied to simple tasks, while machine learning techniques are increasingly used across research domains. This dissertation develops concepts for efficient simulations combining quantum algorithms and machine learning, covering foundations in quantum computing and computational quantum chemistry, enhanced simulation methods for hardware or simulators, and a data-efficient learning approach for chemical reaction models with high interpolation accuracy.","Quantum Simulation of Physical and Chemical Systems Using Noisy Quantum Computers and Machine Learning  \nDissertation  \nzur Erlangung des Grades des Doktors der Naturwissenschaften  \nder Naturwissenschaftlich-Technischen Fakult¨at der Universit¨at des Saarlandes  \nvon  \nTomislav Piskor  \nSaarbr¨ucken  \n2023  \nTag des Kolloquiums: 03. Mai 2024  \nDekan: Prof. Dr. Michael Vielhaber  \nBerichterstatter: Prof. Dr. Frank Wilhelm-Mauch  \nProf. Dr. Peter P. Orth  \nVorsitz: Prof. Dr. Christoph Becher  \nAkad. Mitarbeiter: Dr. Adam Wysocki  \nAbstract   \nThe development of quantum computers has ushered in a new epoch of computing. Currently we are in an era, where the hardware is not yet sophisticated enough to produce reliable results. However, quantum hardware is already being used to compute simple problems. In addition, machine learning algorithms are gaining popularity and are being used in various areas. This thesis provides concepts for efficient simulations using quantum algorithms and machine learning methods.  \nThe first part of this thesis gives a brief overview on basic concepts of quantum computing and computational quantum chemistry.  \nThe next part is divided into two chapters and deals with methods for more efficient simulations when using quantum hardware or simulators. The first work describes the differences between numerical and analytical gradients on simple models. The second chapter of this part presents a post-processing method to improve results from quantum simulations.  \nIn the third and last part of this thesis, a method is presented that allows a machine learning model to be extracted from only a small number of data points from two different chemical reactions. It can be seen that with this model, for example, the interpolation of the potential energy surface can be achieved with an error below chemical accuracy.  \nZusammenfassung   \nDie Entwicklung von Quantencomputern hat eine neue Epoche der Datenverarbeitungeingel¨autet. Derzeit befinden wir uns noch in einer ¨Ara, in der die Hardware noch nicht ausgereift genug, um zuverl¨assige Ergebnisse zu erzielen. Allerdings wird Quantenhardware bereits zur Berechnung einfacher Probleme eingesetzt. Dar¨uber hinaus werden Algorithmen des maschinellen Lernens immer beliebter und in verschiedenen Bereicheneingesetzt. In dieser Arbeit werden Konzepte f¨ur effiziente Simulationen mit Quantenalgorithmen und Methoden des maschinellen Lernens vorgestellt.  \nDer erste Teil dieser Arbeit gibt einen kurzen ¨Uberblick ¨uber die grundlegenden Konzepte des Quantencomputings und der Quantenchemie.  \nDer n¨achste Teil ist in zwei Hauptkapitel unterteilt und befasst sich mit Methoden f¨ur effizientere Simulationen beim Einsatz von Quantenhardware oder-simulatoren. Dieerste Arbeit beschreibt die Unterschiede zwischen numerischen und analytischen Gradienten an einfachen Modellen. Das zweite Kapitel von diesem Teil stellt eine Methodevor, um die Ergebnisse von Quantensimulationen zu verbessern.  \nIm dritten und letzten Teil dieser Arbeit wird eine Methode vorgestellt, die es erm¨oglicht, ein Modell f¨ur maschinelles Lernen aus einer geringen Anzahl von Datenpunkten von zwei verschiedenen chemischen Reaktionen zu extrahieren. Es zeigt sich, dass mit diesem Modell zum Beispiel die Interpolation der potenziellen Energiefl¨ache mit einem Fehler unterhalb der chemischen Genauigkeit erreicht werden kann.  \nPublication List   \nPublished  \n• T. Piskor, J.-M. Reiner, S. Zanker, N. Vogt, M. Marthaler, F. K. Wilhelm, and F.  \nG. Eich  \nUsing gradient-based algorithms to determine ground-state energies on a quantum computer  \nPhys. Rev. A 105, 062415 (2022)  \nSubmitted  \n• T. Piskor, F. G. Eich, M. Marthaler, F. K. Wilhelm, and J.-M. Reiner  \nPost-processing noisy quantum computations utilising N-representability constraints arXiv:2304.13401 (2023)  \n• T. Piskor, P. Pinski, T. Mast, V. Rybkin  \nMulti-level protocol for mechanistic reaction studies using semi-local fitted potential energy surfaces  ","cbCain2tmvuMvMYD","https://ap.wps.com/l/cbCain2tmvuMvMYD","pdf",1643997,1,117,"English","en",105,"# Abstract\n# Introduction and Foundations\n## Quantum Computing Basics\n## Computational Quantum Chemistry\n# Efficient Simulation Methods\n## Numerical vs. Analytical Gradients\n## Post-processing for Improved Quantum Results\n# Machine Learning for Chemical Reactions\n## Data-efficient Model Extraction\n## Potential Energy Surface Interpolation\n# Publications\n## Published\n## Submitted\n## Previous Publications\n# Acknowledgements","[{\"question\":\"What problem does the thesis address regarding current quantum computers?\",\"answer\":\"It addresses the gap between the use of quantum hardware for simple problems and the current lack of hardware sophistication needed to produce reliable results.\"},{\"question\":\"What simulation approaches are covered in the thesis?\",\"answer\":\"It covers methods for more efficient simulations using quantum hardware or simulators, including comparisons of numerical vs. analytical gradients and a post-processing technique to improve noisy quantum simulation outputs.\"},{\"question\":\"How does the thesis use machine learning in the context of chemical reactions?\",\"answer\":\"It presents a method to extract a machine learning model from a small number of data points from two chemical reactions, enabling interpolation of a potential energy surface with an error below chemical accuracy.\"}]","Quantum Simulation of Physical and Chemical Systems Using Noisy Quantum Computers and Machine Learning - 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