[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117771-en":3,"doc-seo-117771-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117771,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Quantum circuit fidelity estimation using machine learning","Real-world quantum computers face performance limits due to operational errors, making accuracy measurement essential for tasks that cannot be efficiently simulated classically. A machine-learning method is presented to estimate fidelity between the state from a noisy quantum circuit and the ideal target state. The supervised model is trained on smaller circuits whose fidelity can be computed via techniques such as direct fidelity estimation and quantum state tomography, then applied to predict fidelities of more complex circuits where those methods are infeasible.","Springer Nature 2021 LATEX template  \narXiv :2212 .00677v4 [ quant-ph] 13 Mar 2023  \nQuantum circuit 􀀌delity estimation using machine learning  \nAvi Vadali 1,2*, Rutuja Kshirsagar3,4*, Prasanth Shyamsundar5 and Gabriel N. Perdue5  \n1 The Latin School of Chicago, 59 W North Blvd, Chicago, 60610,  \nIL, USA.  \n2* California Institute of Technology, 1200 E California Blvd,  \nPasadena, 91125, CA, USA.  \n3Virginia Polytechnic Institute and State University, Blacksburg,  \n24061-0002, VA, USA.  \n4* Fujitsu Research of America, Inc. , 350 Cobalt Way, Sunnyvale,  \n94085, CA, USA.  \n5 Fermilab Quantum Institute, Fermi National Accelerator Laboratory, PO Box 500, Batavia, 60510-0500, IL, USA.  \n*Corresponding author(s). E-mail(s): [avadali@caltech.edu](avadali@caltech.edu) ; [rkshirsagar@fujitsu.com](rkshirsagar@fujitsu.com) ;  \nAbstract  \nThe computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be e􀀎ciently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work we introduce a machine-learning-based technique to estimate the 􀀌delity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the 􀀌delity can be estimated using other techniques like direct 􀀌delity estimation and quantum state tomography. We demonstrate that, for simulated random quantum circuits with a realistic noise model, the trained model can predict the 􀀌delities of more complicated circuits for which such methods are infeasible. In particular, we show the  \nSpringer Nature 2021 LATEX template  \n2 Quantum circuit 􀀌delity estimation using machine learning  \ntrained model may make predictions for circuits with higher degrees of entanglement than were available in the training set, and that the model may make predictions for non-Cli􀀋ord circuits even when the training set included only Cli􀀋ord-reducible circuits. This empirical demonstration suggests classical machine learning may be useful for making predictions about beyond-classical quantum circuits for some non-trivial problems.  \nKeywords: quantum computing, circuit 􀀌delity, quantum noise, neural  \nnetworks  \n1 Introduction  \nEstimating the quality of computations performed by a noisy quantum computer is an important task. It allows us to benchmark current and future quantum computers and calculate the credibility of their computations [14] . Furthermore, knowledge of the characteristics of various di􀀋erent circuit implementations of a given quantum algorithm can inform optimal implementation in a noise-aware manner. Computation quality may be quanti􀀌ed using the 􀀌delity between the state produced by the physical circuit and the target state corresponding to an ideal (noiseless) circuit. Henceforth, we will refer to this 􀀌delity (assuming that all the qubits are initialized to j0i prior to the computation) simply as the 􀀌delity of the circuit. Several techniques exist in the literature to estimate this 􀀌delity, e.g., quantum state tomography [12] and direct 􀀌delity estimation (DFE) [4, 15] . Mirror Circuit Fidelity Estimation (MCFE) is a technique for estimating the entanglement or process 􀀌delity, which is related, but not identical, to state 􀀌delity considered in this paper [14] .  \nThe available techniques vary both in a) situations where they are applicable, and b) their classical and quantum computational cost. Techniques like quantum state tomography and direct 􀀌delity estimation can accurately estimate 􀀌delities for all noise models. However, they require access to the target  \nSpringer Nature 2021 LATEX template  \nQuantum circuit 􀀌delity estimation using machine learning 3 quantum state from classical simulations and they involve running the circuit under consideration multiple times on the qu","cbCaivzGuYJEyEIM","https://ap.wps.com/l/cbCaivzGuYJEyEIM","pdf",1075543,1,27,"English","en",105,"# Introduction\n## Fidelity and benchmarking\n## Existing fidelity estimation methods\n## Limits of tomography and DFE\n## Fully-classical fidelity estimation\n## Motivation for ML-based prediction","[{\"question\":\"What does the paper propose for evaluating noisy quantum computations?\",\"answer\":\"It introduces a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the ideal noise-free target state.\"},{\"question\":\"How is the machine-learning model trained and validated?\",\"answer\":\"The model is trained in a supervised manner using smaller or simpler circuits whose fidelities can be estimated with methods such as direct fidelity estimation and quantum state tomography.\"},{\"question\":\"Why are existing techniques like tomography and direct fidelity estimation not always practical?\",\"answer\":\"They require access to target states from classical simulations and involve running the circuit multiple times on quantum hardware, making their classical and quantum costs prohibitive for large circuits.\"}]","Quantum circuit fidelity estimation using machine learning | 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does the paper propose for evaluating noisy quantum computations?","Question",{"text":76,"@type":77},"It introduces a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the ideal noise-free target state.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the machine-learning model trained and validated?",{"text":81,"@type":77},"The model is trained in a supervised manner using smaller or simpler circuits whose fidelities can be estimated with methods such as direct fidelity estimation and quantum state tomography.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are existing techniques like tomography and direct fidelity estimation not always practical?",{"text":85,"@type":77},"They require access to target states from classical simulations and involve running the circuit multiple times on quantum hardware, making their classical and quantum costs prohibitive for large 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