[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121024-en":3,"doc-seo-121024-105":30,"detail-sidebar-cat-0-en-105":90},{"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},121024,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Application of machine learning to experimental design in quantum mechanics - Extended abstract","Reinforcement learning is used to optimize experimental design for quantum metrology and parameter estimation by exploiting known features of the quantum system. The method is model-aware: an agent learns an adaptive measurement strategy from previous outcomes while accounting for quantum-mechanical constraints. A Python framework, qsensoropt, encapsulates the physics abstraction and enables precision optimization via Monte Carlo simulation for both Bayesian and frequentist estimation. Applications include nitrogen-vacancy (NV) centers and photonic circuits, showing certified improvements over current state-of-the-art controls.","arXiv :2403 . 10317v1 [ quant-ph] 15 Mar 2024  \nApplication of machine learning to experimental design in quantum mechanics  \nFederico Belliardo  \nNEST, Scuola Normale Superiore, I-56126 Pisa, Italy  \nFabio Zoratti  \nScuola Normale Superiore, I-56126 Pisa, Italy  \nVittorio Giovannetti  \nNEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, I-56126 Pisa, Italy  \nThe recent advances in machine learning hold great promise for the fields of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system with which the agent is interacting, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system, and only some parameters of the evolution or the initial state are unknown. We present here a general machine learning technique that can optimize the precision of quantum sensors, exploiting the knowledge we have on the system through model-aware reinforcement learning. This framework has been implemented in the Python package qsensoropt, which is able to optimize a broad class of problems found in quantum metrology and quantum parameter estimation. The agent learnsan optimal adaptive strategy that, based on previous outcomes, decides the next measurements to perform. This approach works for both Bayesian estimation and frequentist estimation. The user is required to implement the physics of the system to be studied and state which parameters in the experiment are controllable and which are unknown. The functions of the library then allow the training of the agent to optimize the precision of the sensor in a Monte Carlo simulation of the experiment. We have explored some applications of this technique to NV centers and photonic circuits. So far, we have been able to certify better results than the current state-of-the-art controls for many cases. The machine learning technique developed here can be applied in all scenarios where the quantum system is well-characterized and relatively simple and small. In these cases, we can extract every last bit of information from a quantum sensor by appropriately controlling it with a trained neural network. The qsensoropt software is available on PyPI and can be installed with pip.  \nIntroduction. In recent times, there has been a growing focus on the intersection of machine learning and quantum information. The collaboration between these two technological realms holds promise for mutual benefits. Quantum technologies, particularly quantum computers, possess the capability to tackle conventional challenges in machine learning, such as classification and pattern recognition, whether handling classical or quantum data [1, 2] . Conversely, conventional machine learning can enhance tasks in quantum information, such as quantum control with feedback [3] and error correction [4] . Our research falls into the latter category. Specifically, we employ model-aware reinforcement learning to discover optimized adaptive and non-adaptive control strategies for tasks in quantum metrology and estimation. Through this approach, we investigate how machine learning has the potential to improve traditional methods in quantum physics and contribute to the advancement of new quantum information processing technologies. The present article serves as a three-pages extended abstract to the papers containing the theoretical development of this framework [5], and the applications [6] . We refer also to the online documentation of the qsensoropt library [7] for details on the implementation and the usage of the framework, and to the repository [8] for accessing the  \ncode. In a quantum me","cbCaigMajt59nnGj","https://ap.wps.com/l/cbCaigMajt59nnGj","pdf",513818,1,4,"English","en",105,"# Introduction\n## Model-aware reinforcement learning for quantum metrology\n## Precision optimization and error figures of merit\n## Implementation with the qsensoropt Python package\n## Applications: NV centers and photonic circuits","[{\"question\":\"How does reinforcement learning improve experimental design in quantum metrology?\",\"answer\":\"It learns an adaptive strategy that selects the next measurements based on prior outcomes, aiming to minimize an error figure of merit such as mean square error.\"},{\"question\":\"What makes the proposed approach “model-aware” reinforcement learning?\",\"answer\":\"The agent uses knowledge that the interacting system follows quantum mechanics, while only specific parameters (evolution or initial state) are treated as unknown.\"},{\"question\":\"What is qsensoropt and what problems does it address?\",\"answer\":\"qsensoropt is a Python library that abstracts the controlled estimation workflow and trains an agent in Monte Carlo simulations to optimize quantum sensor precision, supporting both Bayesian and frequentist estimation.\"}]","Application of machine learning to experimental design in quantum mechanics - Extended abstract | PDF",1785733368,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"application-of-machine-learning-to-experimental-design-in-quantum-mechanics-extended-abstract","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/application-of-machine-learning-to-experimental-design-in-quantum-mechanics-extended-abstract/121024/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does reinforcement learning improve experimental design in quantum metrology?","Question",{"text":74,"@type":75},"It learns an adaptive strategy that selects the next measurements based on prior outcomes, aiming to minimize an error figure of merit such as mean square error.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What makes the proposed approach “model-aware” reinforcement learning?",{"text":79,"@type":75},"The agent uses knowledge that the interacting system follows quantum mechanics, while only specific parameters (evolution or initial state) are treated as unknown.",{"name":81,"@type":72,"acceptedAnswer":82},"What is qsensoropt and what problems does it address?",{"text":83,"@type":75},"qsensoropt is a Python library that abstracts the controlled estimation workflow and trains an agent in Monte Carlo simulations to optimize quantum sensor precision, supporting both Bayesian and frequentist estimation.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]