{"id":16684,"date":"2021-12-22T10:58:54","date_gmt":"2021-12-22T10:58:54","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=16684"},"modified":"2024-09-04T20:11:53","modified_gmt":"2024-09-04T19:11:53","slug":"machine-learning-technique-models-quantum-devices","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/machine-learning-technique-models-quantum-devices\/16684\/","title":{"rendered":"Machine learning technique models quantum devices\u00a0"},"content":{"rendered":"

A new algorithm developed by\u00a0researchers at the University of Tokyo\u00a0enables efficient and accurate verification of quantum devices.<\/span>\u00a0<\/span><\/h2>\n

Technologies that utilise quantum mechanical behaviours \u2013 such as devices that use quantum information as input and output data\u00a0\u2013\u00a0are likely to have more mainstream applications\u00a0in the near future.\u00a0<\/span>\u00a0<\/span><\/p>\n

These quantum devices necessitate careful verification\u00a0as a result of\u00a0inherent uncertainties. This\u00a0is because the\u00a0verification process proves more difficult if the device is time dependent when the output depends on past inputs.\u00a0<\/span>\u00a0<\/span><\/p>\n

Verifying time-dependent quantum devices<\/span>\u00a0<\/span><\/h3>\n

Now, for the first time,\u00a0researchers\u00a0have used machine learning to\u00a0significantly\u00a0enhance the efficiency of verification for time-dependent quantum devices\u00a0by integrating\u00a0a certain memory effect present in these systems.\u00a0<\/span>\u00a0<\/span><\/p>\n

While quantum computers are of significant interest to the scientific community, these fascinating machines are still in their infancy. However, a quantum internet may be more realisable. This would provide important security advantages over the current internet. However, this would rely on technologies that are\u00a0currently\u00a0not used outside of a lab setting.\u00a0<\/span>\u00a0<\/span><\/p>\n

While many\u00a0tenets\u00a0of the devices that can\u00a0build\u00a0our quantum internet may have been worked out, there are many engineering challenges\u00a0that need to be\u00a0considered\u00a0in\u00a0order to\u00a0recognise\u00a0these as products.\u00a0However, there is\u00a0a lot of\u00a0research underway to\u00a0develop\u00a0tools for the design of quantum devices.<\/span>\u00a0<\/span><\/p>\n

Postdoctoral researcher Quoc\u00a0Hoan\u00a0Tran and Associate Professor Kohei Nakajima from the Graduate School of Information Science and Technology at the University of Tokyo<\/a> have\u00a0established\u00a0such a tool, which they\u00a0believe\u00a0could make verifying the\u00a0behaviour\u00a0of quantum devices a more\u00a0effective\u00a0and\u00a0accurate\u00a0undertaking than it is at present.\u00a0<\/span>\u00a0<\/span><\/p>\n

Machine learning tool<\/span>\u00a0<\/span><\/h3>\n

The tool they have developed\u00a0is an algorithm that can\u00a0restructure\u00a0the workings of a time-dependent quantum device by\u00a0studying\u00a0the relationship between the quantum inputs and outputs. This\u00a0method\u00a0is commonplace when\u00a0investigating\u00a0a classical physical system, but quantum information is\u00a0more difficult\u00a0to store, which usually makes it impossible.<\/span>\u00a0<\/span><\/p>\n

\u201cThe technique to describe a quantum system based on its inputs and outputs is called quantum process tomography,\u201d\u00a0explained\u00a0Tran. \u201cHowever, many researchers now report that their quantum systems exhibit\u00a0some kind of memory\u00a0effect where present states are affected by previous ones. This means that a simple inspection of input and output states cannot describe the time-dependent nature of the system. You could model the system repeatedly after every change in time, but this would be extremely computationally inefficient. Our aim was to embrace this memory effect and use it to our advantage rather than use brute force to overcome it.\u201d<\/span>\u00a0<\/span><\/p>\n

Tran and Nakajima turned to machine learning<\/a> and a\u00a0method\u00a0known as\u00a0quantum reservoir computing to\u00a0develop\u00a0their novel algorithm. This learns patterns of inputs and outputs that change over time in a quantum system and\u00a0efficiently\u00a0predicts\u00a0how these patterns will change, even in situations the algorithm has not yet witnessed.\u00a0<\/span>\u00a0<\/span><\/p>\n

As it does not need to know the inner workings of a quantum system as a more empirical method might, but only the inputs and outputs, the\u00a0researchers\u2019\u00a0algorithm can be simpler and\u00a0generate\u00a0results faster as well.<\/span>\u00a0<\/span><\/p>\n

\u201cAt present, our algorithm can emulate a certain kind of quantum system, but hypothetical devices may vary widely in their processing ability and have different memory effects.\u00a0So\u00a0the next stage of research will be to broaden the capabilities of our algorithms, essentially making something more general purpose and thus more useful,\u201d\u00a0added\u00a0Tran. \u201cI am excited by what quantum machine learning methods could do, by the hypothetical devices they might lead to.\u201d<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

A new algorithm developed by\u00a0researchers at the University of Tokyo\u00a0enables efficient and accurate verification of quantum devices.\u00a0 Technologies that utilise quantum mechanical behaviours \u2013 such as devices that use quantum information as input and output data\u00a0\u2013\u00a0are likely to have more mainstream applications\u00a0in the near future.\u00a0\u00a0 These quantum devices necessitate careful verification\u00a0as a result of\u00a0inherent uncertainties. […]<\/p>\n","protected":false},"author":13,"featured_media":16685,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[24615],"tags":[570,24128,24208,793],"acf":[],"yoast_head":"\nMachine learning technique models quantum devices\u00a0<\/title>\n<meta name=\"description\" content=\"A new algorithm developed by\u00a0researchers at the University of Tokyo\u00a0enables efficient and accurate verification of quantum devices.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.innovationnewsnetwork.com\/machine-learning-technique-models-quantum-devices\/16684\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" 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