
{"id":63255,"date":"2025-04-14T20:36:46","date_gmt":"2025-04-14T20:36:46","guid":{"rendered":""},"modified":"2025-04-14T20:36:46","modified_gmt":"2025-04-14T20:36:46","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63255","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63255\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0627\u0633\u062a\u0627\u0628\u0644\u0627\u06cc\u0632\u0631 \u0628\u0648\u062a \u0627\u0633\u062a\u0631\u067e\u06cc\u0646\u06af: \u062f\u0633\u062a\u0648\u0631 \u0627\u0644\u0639\u0645\u0644\u06cc \u0628\u0631\u0627\u06cc \u062a\u0648\u0645\u0648\u06af\u0631\u0627\u0641\u06cc \u0622\u06af\u0646\u0648\u0633\u062a\u06cc\u06a9 \u06a9\u0627\u0631\u0622\u0645\u062f \u0648 \u062a\u062e\u0645\u06cc\u0646 \u062c\u0627\u062f\u0648\u06cc\u06cc"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0627\u0633\u062a\u0627\u0628\u0644\u0627\u06cc\u0632\u0631 \u0628\u0648\u062a \u0627\u0633\u062a\u0631\u067e\u06cc\u0646\u06af: \u062f\u0633\u062a\u0648\u0631 \u0627\u0644\u0639\u0645\u0644\u06cc \u0628\u0631\u0627\u06cc \u062a\u0648\u0645\u0648\u06af\u0631\u0627\u0641\u06cc \u0622\u06af\u0646\u0648\u0633\u062a\u06cc\u06a9 \u06a9\u0627\u0631\u0622\u0645\u062f \u0648 \u062a\u062e\u0645\u06cc\u0646 \u062c\u0627\u062f\u0648\u06cc\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n<td>\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n<td>\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n<td>\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>68<\/td>\n<\/tr>\n<tr>\n<td>\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<td><a href=\"https:\/\/arxiv.org\/pdf\/2408.06967\">\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Quantum Physics,Computational Complexity,Data Structures and Algorithms,Machine Learning,\u0641\u06cc\u0632\u06cc\u06a9 \u06a9\u0648\u0627\u0646\u062a\u0648\u0645\u06cc , \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc , \u0633\u0627\u062e\u062a\u0627\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627 , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 13 August, 2024; originally announced August 2024. , Comments: 68 pages<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 13 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f. \u060c \u0646\u0638\u0631\u0627\u062a: 68 \u0635\u0641\u062d\u0647<\/td>\n<\/tr>\n<tr>\n<td>\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc      <\/td>\n<td>\n            <a href=\"https:\/\/inspirehep.net\/arxiv\/2408.06967\">INSPIRE HEP<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/ui.adsabs.harvard.edu\/abs\/arXiv:2408.06967\">NASA ADS<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/scholar.google.com\/scholar_lookup?arxiv_id=2408.06967\">Google Scholar<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/api.semanticscholar.org\/arXiv:2408.06967\">Semantic Scholar<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/arxiv.org\/abs\/2408.06967>arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n<table class=\"table table-striped table-hover table-primary\">\r\n    <tr>\r\n        <td>\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n    <\/tr>\r\n    <tr>\r\n        <td>\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n    <\/tr>\r\n\t<tr>\r\n        <td>\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647  <\/td>\r\n        <td>\u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n    <\/tr>\r\n\t\t<tr>\r\n        <td>\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627  <\/td>\r\n        <td>\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n    <\/tr>\r\n<\/table>\r\n\r\n\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">We study the task of agnostic tomography: given copies of an unknown $n$-qubit state $\u03c1$ which has fidelity $\u03c4$ with some state in a given class $C$, find a state which has fidelity $\\ge \u03c4- \u03b5$ with $\u03c1$. We give a new framework, stabilizer bootstrapping, for designing computationally efficient protocols for this task, and use this to get new agnostic tomography protocols for the following classes: Stabilizer states: We give a protocol that runs in time $\\mathrm{poly}(n,1\/\u03b5)\\cdot (1\/\u03c4)^{O(\\log(1\/\u03c4))}$, answering an open question posed by Grewal, Iyer, Kretschmer, Liang [40] and Anshu and Arunachalam [6]. Previous protocols ran in time $\\mathrm{exp}(\u0398(n))$ or required $\u03c4>\\cos^2(\u03c0\/8)$. States with stabilizer dimension $n &#8211; t$: We give a protocol that runs in time $n^3\\cdot(2^t\/\u03c4)^{O(\\log(1\/\u03b5))}$, extending recent work on learning quantum states prepared by circuits with few non-Clifford gates, which only applied in the realizable setting where $\u03c4= 1$ [30, 37, 46, 61]. Discrete product states: If $C = K^{\\otimes n}$ for some $\u03bc$-separated discrete set $K$ of single-qubit states, we give a protocol that runs in time $(n\/\u03bc)^{O((1 + \\log (1\/\u03c4))\/\u03bc)}\/\u03b5^2$. This strictly generalizes a prior guarantee which applied to stabilizer product states [39]. For stabilizer product states, we give a further improved protocol that runs in time $(n^2\/\u03b5^2)\\cdot (1\/\u03c4)^{O(\\log(1\/\u03c4))}$. As a corollary, we give the first protocol for estimating stabilizer fidelity, a standard measure of magic for quantum states, to error $\u03b5$ in $n^3 \\mathrm{quasipoly}(1\/\u03b5)$ time.<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0645\u0627 \u0648\u0638\u06cc\u0641\u0647 \u062a\u0648\u0645\u0648\u06af\u0631\u0627\u0641\u06cc \u0622\u06af\u0646\u0648\u0633\u062a\u06cc\u06a9 \u0631\u0627 \u0645\u0637\u0627\u0644\u0639\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645: \u0628\u0627 \u062a\u0648\u062c\u0647 \u0628\u0647 \u0646\u0633\u062e\u0647 \u0647\u0627\u06cc \u0646\u0627\u0634\u0646\u0627\u062e\u062a\u0647 $ n $ -qubit $ \u03c1 $ \u06a9\u0647 \u062f\u0627\u0631\u0627\u06cc \u0648\u0641\u0627\u062f\u0627\u0631\u06cc $ \u03c4 $ \u0628\u0627 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0627\u06cc\u0627\u0644\u062a \u0647\u0627 \u062f\u0631 \u06cc\u06a9 \u06a9\u0644\u0627\u0633 \u062e\u0627\u0635 $ c $ \u0627\u0633\u062a \u060c \u0648\u0636\u0639\u06cc\u062a\u06cc \u0631\u0627 \u067e\u06cc\u062f\u0627 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u062f\u0627\u0631\u0627\u06cc \u0648\u0641\u0627\u062f\u0627\u0631\u06cc $ \\ ge \u03c4- \u0628\u0627\u0634\u062f\u03b5 $ \u0628\u0627 $ \u03c1 $.\u0645\u0627 \u0628\u0631\u0627\u06cc \u0637\u0631\u0627\u062d\u06cc \u067e\u0631\u0648\u062a\u06a9\u0644 \u0647\u0627\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u06a9\u0627\u0631\u0622\u0645\u062f \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u060c \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u062c\u062f\u06cc\u062f \u060c Bootstrapping \u062a\u062b\u0628\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u0648 \u0627\u0632 \u0627\u06cc\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u062a\u0627 \u067e\u0631\u0648\u062a\u06a9\u0644 \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u062a\u0648\u0645\u0648\u06af\u0631\u0627\u0641\u06cc \u0622\u06af\u0646\u0648\u0633\u062a\u06cc\u06a9 \u0631\u0627 \u0628\u0631\u0627\u06cc \u06a9\u0644\u0627\u0633\u0647\u0627\u06cc \u0632\u06cc\u0631 \u0628\u062f\u0633\u062a \u0622\u0648\u0631\u06cc\u0645: \u062d\u0627\u0644\u062a \u0647\u0627\u06cc \u062a\u062b\u0628\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647: \u0645\u0627 \u067e\u0631\u0648\u062a\u06a9\u0644 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 \u0628\u0647 \u0645\u0648\u0642\u0639 $ \\ Mathrm {Poly} \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f (n \u060c 1\/\u03b5) \\ cdot (1\/\u03c4)^{o (\\ log (1\/\u03c4))} $ \u060c \u067e\u0627\u0633\u062e \u062f\u0627\u062f\u0646 \u0628\u0647 \u06cc\u06a9 \u0633\u0624\u0627\u0644 \u0628\u0627\u0632 \u0645\u0637\u0631\u062d \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 Grewal \u060c Iyer \u060c Kretschmer \u060c Liang [40] \u0648 Anshu \u0648 Arunachalam [6].\u067e\u0631\u0648\u062a\u06a9\u0644 \u0647\u0627\u06cc \u0642\u0628\u0644\u06cc \u0628\u0647 \u0645\u0648\u0642\u0639 $ \\ mathrm {exp} (\u03b8 (n)) $ \u06cc\u0627 $ \u03c4> \\ cos^2 (\u03c0\/8) $ \u0646\u06cc\u0627\u0632 \u062f\u0627\u0634\u062a\u0646\u062f.\u0627\u06cc\u0627\u0644\u0627\u062a \u0628\u0627 \u0627\u0628\u0639\u0627\u062f \u062a\u062b\u0628\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 $ n &#8211; t $: \u0645\u0627 \u067e\u0631\u0648\u062a\u06a9\u0644 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 \u0628\u0647 \u0645\u0648\u0642\u0639 $ n^3 \\ cdot (2^t\/\u03c4)^{o (\\ log (1\/\u03b5))} $ \u060c \u06a9\u0627\u0631 \u0627\u062e\u06cc\u0631 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u06cc\u0645.\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062d\u0627\u0644\u062a\u0647\u0627\u06cc \u06a9\u0648\u0627\u0646\u062a\u0648\u0645\u06cc \u062a\u0647\u06cc\u0647 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 \u0645\u062f\u0627\u0631\u0647\u0627 \u0628\u0627 \u062a\u0639\u062f\u0627\u062f \u06a9\u0645\u06cc \u0627\u0632 \u062f\u0631\u0648\u0627\u0632\u0647 \u0647\u0627\u06cc \u063a\u06cc\u0631 \u06a9\u06cc\u0641\u0648\u0631\u062f \u060c \u06a9\u0647 \u0641\u0642\u0637 \u062f\u0631 \u0645\u062d\u06cc\u0637 \u0642\u0627\u0628\u0644 \u062a\u062d\u0642\u0642 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 $ \u03c4 = 1 $ [30 \u060c 37 \u060c 46 \u060c 61].\u0645\u062d\u0635\u0648\u0644 \u06af\u0633\u0633\u062a\u0647: \u0627\u06af\u0631 $ c = k^{\\ otimes n} $ \u0628\u0631\u0627\u06cc \u0628\u0631\u062e\u06cc \u0627\u0632 $ $ $ \u062c\u062f\u0627 \u0634\u062f\u0647 $ $ k $ \u0627\u0632 \u0627\u06cc\u0627\u0644\u062a \u0647\u0627\u06cc \u062a\u06a9 \u06a9\u0628\u0648\u062a\u0631 \u060c \u0645\u0627 \u06cc\u06a9 \u067e\u0631\u0648\u062a\u06a9\u0644 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 \u0628\u0647 \u0645\u0648\u0642\u0639 $ (n\/\u03bc)^{\u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f.o ((1 + \\ log (1\/\u03c4))\/\u03bc)}\/\u03b5^2 $.\u0627\u06cc\u0646 \u0628\u0647 \u0634\u062f\u062a \u0636\u0645\u0627\u0646\u062a \u0642\u0628\u0644\u06cc \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u062d\u0627\u0644\u062a \u0647\u0627\u06cc \u0645\u062d\u0635\u0648\u0644 \u062a\u062b\u0628\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f \u060c \u062a\u0639\u0645\u06cc\u0645 \u0645\u06cc \u062f\u0647\u062f [39].\u0628\u0631\u0627\u06cc \u062d\u0627\u0644\u062a \u0647\u0627\u06cc \u0645\u062d\u0635\u0648\u0644 \u062a\u062b\u0628\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u060c \u0645\u0627 \u06cc\u06a9 \u067e\u0631\u0648\u062a\u06a9\u0644 \u0628\u0647\u0628\u0648\u062f \u06cc\u0627\u0641\u062a\u0647 \u0628\u06cc\u0634\u062a\u0631 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 \u0628\u0647 \u0645\u0648\u0642\u0639 $ (n^2\/\u03b5^2) \\ cdot (1\/\u03c4)^{o (\\ log (1\/\u03c4))} $.\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0646\u062a\u06cc\u062c\u0647 \u060c \u0645\u0627 \u0627\u0648\u0644\u06cc\u0646 \u067e\u0631\u0648\u062a\u06a9\u0644 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0628\u0631\u0622\u0648\u0631\u062f \u0648\u0641\u0627\u062f\u0627\u0631\u06cc \u062a\u062b\u0628\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u060c \u06cc\u06a9 \u0645\u0639\u06cc\u0627\u0631 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0627\u0632 \u062c\u0627\u062f\u0648 \u0628\u0631\u0627\u06cc \u062d\u0627\u0644\u062a \u0647\u0627\u06cc \u06a9\u0648\u0627\u0646\u062a\u0648\u0645\u06cc \u060c \u0628\u0631\u0627\u06cc \u062e\u0637\u0627 $ \u03b5 $ \u062f\u0631 $ n^3 \\ mathrm {quasipoly} (1\/\u03b5) $.<\/p>\n\r\n<table class=\"table table-striped table-hover table-primary\">\r\n    <tr>\r\n        <td>\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n    <\/tr>\r\n    <tr>\r\n        <td>\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n    <\/tr>\r\n\t<tr>\r\n        <td>\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647  <\/td>\r\n        <td>\u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n    <\/tr>\r\n\t\t<tr>\r\n        <td>\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627  <\/td>\r\n        <td>\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n    <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 [&hellip;]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"open","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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\u062f\u0633\u062a\u0648\u0631 \u0627\u0644\u0639\u0645\u0644\u06cc \u0628\u0631\u0627\u06cc \u062a\u0648\u0645\u0648\u06af\u0631\u0627\u0641\u06cc \u0622\u06af\u0646\u0648\u0633\u062a\u06cc\u06a9 \u06a9\u0627\u0631\u0622\u0645\u062f \u0648 \u062a\u062e\u0645\u06cc\u0646 \u062c\u0627\u062f\u0648\u06cc\u06cc - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:description\" content=\"\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/express24.ir\/d\/product\/\u062a\u0631\u062c\u0645\u0647-\u0641\u0627\u0631\u0633\u06cc-\u0645\u0642\u0627\u0644\u0647-63255\/\" \/>\n<meta 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