{"id":174004,"date":"2023-10-11T21:23:15","date_gmt":"2023-10-12T02:23:15","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2023\/10\/exploring-parameter-shift-for-quantum-fisher-information"},"modified":"2023-10-11T21:23:15","modified_gmt":"2023-10-12T02:23:15","slug":"exploring-parameter-shift-for-quantum-fisher-information","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2023\/10\/exploring-parameter-shift-for-quantum-fisher-information","title":{"rendered":"Exploring parameter shift for quantum Fisher information"},"content":{"rendered":"

<\/a><\/p>\n

In a recent publication<\/a> in EPJ Quantum Technology<\/i>, Le Bin Ho from Tohoku University\u2019s Frontier Institute for Interdisciplinary Sciences has developed a technique called time-dependent stochastic parameter shift in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.<\/p>\n

Typically, computing derivatives requires dissecting the function and calculating the rate of change over a small interval. But even classical computers<\/a> cannot keep dividing indefinitely. In contrast, quantum computers can accomplish this task without having to discrete the function. This feature is achievable because quantum computers operate in a realm known as \u201cquantum space,\u201d characterized by periodicity, and no need for endless subdivisions.<\/p>\n

One way to illustrate this concept is by comparing the sizes of two elementary schools<\/a> on a map. To do this, one might print out maps of the schools and then cut them into smaller pieces<\/a>. After cutting, these pieces can be arranged into a line, with their total length compared (see Figure 1a). However, the pieces may not form a perfect rectangle, leading to inaccuracies. An infinite subdivision would be required to minimize these errors, an impractical solution, even for classical computers.<\/p>\n","protected":false},"excerpt":{"rendered":"

In a recent publication in EPJ Quantum Technology, Le Bin Ho from Tohoku University\u2019s Frontier Institute for Interdisciplinary Sciences has developed a technique called time-dependent stochastic parameter shift in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32,1965,1617,6],"tags":[],"class_list":["post-174004","post","type-post","status-publish","format-standard","hentry","category-education","category-mapping","category-quantum-physics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/174004","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=174004"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/174004\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=174004"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=174004"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=174004"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}