{"id":17055,"date":"2026-09-02T11:10:11","date_gmt":"2026-09-02T09:10:11","guid":{"rendered":"https:\/\/qilimanjaro.tech\/?p=17055"},"modified":"2026-09-03T08:13:39","modified_gmt":"2026-09-03T06:13:39","slug":"quantum-reservoir-computing","status":"publish","type":"post","link":"https:\/\/qilimanjaro.tech\/ca\/quantum-reservoir-computing\/","title":{"rendered":"Computaci\u00f3 de Reservori Qu\u00e0ntica: Aprenentatge Autom\u00e0tic Sense Entrenar el Sistema Qu\u00e0ntic"},"content":{"rendered":"
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COMPARTIR ARTICLE<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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PER<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Carles Ramon Escandell<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Enginyer Qu\u00e0ntic J\u00fanior<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Tota la ind\u00fastria genera ara m\u00e9s d \u017eeny de la que pot processar f\u00e0cilment, i els models d'aprenentatge autom\u00e0tic que l'analitzen no paren de m\u00e9s car d'entrenar i executar<\/strong>. L'aprenentatge autom\u00e0tic qu\u00e0ntic explora si els sistemes qu\u00e0ntics poden assumir algunes d'aquestes tasques d'aprenentatge utilitzant menys recursos.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Una de les tasques m\u00e9s habituals d'aquestes \u00e9s previsi\u00f3<\/strong>donat un flux de dades que es desenvolupa al llarg del temps, com ara lectures de sensors, demanda energ\u00e8tica o senyals financers, l'objectiu \u00e9s predir qu\u00e8 ve a continuaci\u00f3 a partir dels patrons del passat recent. Per a aix\u00f2 es va crear el \u00abQuantum Reservoir Computing\u00bb (QRC). A difer\u00e8ncia de la majoria d'enfocaments d'aprenentatge autom\u00e0tic qu\u00e0ntic, el QRC no entren el sistema qu\u00e0ntic: la seva din\u00e0mica es mant\u00e9 fixa i nom\u00e9s s'entrena una lectura lineal, la qual cosa facilita la seva implementaci\u00f3 en el maquinari qu\u00e0ntic actual.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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El problema de la formaci\u00f3<\/b><\/h1>

La majoria dels algorismes d'aprenentatge autom\u00e0tic qu\u00e0ntic segueixen la mateixa estrat\u00e8gia. Dissenyeu un circuit qu\u00e0ntic amb portes d'un i dos qubits ajustables, executeu-lo, mesureu-ne la sortida, compareu-la amb la resposta que vol\u00edeu, ajusteu els par\u00e0metres i torneu-ho a comen\u00e7ar. Milers de vegades. Cada ajust significa tornar a executar el maquinari qu\u00e0ntic, aix\u00ed que l'entrenament \u00e9s lent i cost\u00f3s<\/strong>.<\/span><\/p>

Aquests algorismes tamb\u00e9 s'enfronten a un repte m\u00e9s gran conegut com a replans est\u00e8rils. L'entrenament avan\u00e7a seguint petites millores pas a pas, com caminar avall a la recerca del punt m\u00e9s baix d'una vall. A mesura que aquests circuits creixen, el paisatge es torna pla gaireb\u00e9 a tot arreu. No hi ha cap pendent a seguir, res indica com ajustar els par\u00e0metres<\/strong> i l'entrenament es queda encallat abans de trobar res \u00fatil.<\/span><\/p>

Altres models qu\u00e0ntics mantenen la part entrenable dins del sistema qu\u00e0ntic, no pas en les portes, sin\u00f3 en les interaccions entre els qubits. Una m\u00e0quina de Boltzmann qu\u00e0ntica, l'exemple est\u00e0ndard, apr\u00e8n exactament d'aquesta manera. La dificultat rau a entrenar aquestes interaccions. Els m\u00e8todes per fer-ho existeixen sobre el paper, per\u00f2 necessiten maquinari qu\u00e0ntic m\u00e9s enll\u00e0 del que est\u00e0 disponible avui dia<\/strong>, de manera que entrenar aquests models a una escala \u00fatil continua sent impossible. A m\u00e9s a m\u00e9s, les interaccions que el model necessita potser no s\u00f3n les que el dispositiu pot oferir.<\/span><\/p>

El patr\u00f3 darrere de tots dos problemes \u00e9s el mateix. Mentre all\u00f2 que s'est\u00e0 entrenant visqui dins del sistema qu\u00e0ntic, cada pas d'entrenament ha de passar pel maquinari qu\u00e0ntic<\/strong>, amb tot el seu cost i les complicacions de manipular i ajustar un dispositiu qu\u00e0ntic. El Computatge de Reservori Qu\u00e0ntic adopta el punt de vista contrari. L'entrenament es mant\u00e9 completament en el costat cl\u00e0ssic<\/strong>, mentre que els par\u00e0metres del sistema qu\u00e0ntic romanen fixos.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t

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Com funciona<\/b><\/h1>

Un dip\u00f2sit qu\u00e0ntic processa una s\u00e8rie temporal pas a pas<\/strong>. En cada pas, el seg\u00fcent valor del senyal s'escriu al sistema qu\u00e0ntic, el el sistema llavors evoluciona<\/strong> sota la seva din\u00e0mica fixa, barrejant aquesta entrada en el seu estat intern, i despr\u00e9s es pren un conjunt de mesures. Aquestes mesures no s\u00f3n m\u00e9s que n\u00fameros, una instant\u00e0nia del sistema en aquell moment, i es converteixen en els ingredients a partir dels quals es construeix la predicci\u00f3. Aquests n\u00fameros s'introdueixen a l'\u00fanica part de tot l'esquema que s'arriba a entrenar, una regressi\u00f3 lineal que apr\u00e8n a combinar-les en la predicci\u00f3<\/strong>. <\/span>Aquesta formaci\u00f3 \u00e9s econ\u00f2mica, troba sempre la millor combinaci\u00f3 possible i ho fa en un sol c\u00e0lcul.<\/span>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t

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El sistema qu\u00e0ntic contribueix all\u00f2 que una regressi\u00f3 lineal no podria fer mai per si sola<\/strong>. La seva din\u00e0mica barreja cada nova entrada amb les que van arribar anteriorment, i aix\u00f2 \u00e9s el que proporciona la mem\u00f2ria de reserva<\/strong>. L'estat del sistema no dep\u00e8n nom\u00e9s de l'\u00faltima entrada sin\u00f3 tamb\u00e9 de les anteriors, de manera que les mesures preses a cada pas porten informaci\u00f3 sobre el passat recent. \u00c9s per aix\u00f2 que el m\u00e8tode \u00e9s molt adequat per a la previsi\u00f3<\/strong>. Una corba de demanda energ\u00e8tica, els cicles de c\u00e0rrega i desc\u00e0rrega d'una cel\u00b7la de bateria, les lectures de sensors d'una l\u00ednia de producci\u00f3, totes aquestes senyals porten el seu futur en les correlacions del seu passat recent, i exactament aqu\u00ed \u00e9s on un reservori qu\u00e0ntic \u00e9s \u00fatil.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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On encaixa el treball de Qilimanjaro<\/strong><\/h1>

La computaci\u00f3 de dip\u00f2sit qu\u00e0ntic \u00e9s especialment adequada per a hardware qu\u00e0ntic anal\u00f2gic.<\/strong> Els processadors anal\u00f2gics evolucionen cont\u00ednuament sota un hamiltoni\u00e0 en lloc d'executar seq\u00fc\u00e8ncies de portes discretes, coincidint amb la din\u00e0mica fixa de la qual dep\u00e8n un reservori qu\u00e0ntic. El dispositiu evoluciona sota la seva din\u00e0mica natural i es mesura, sense que s'optimitzi ni s'ajusti res a l'interior del sistema qu\u00e0ntic.<\/p><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

L'equip d'investigaci\u00f3 de Qilimanjaro est\u00e0 desenvolupant m\u00e8todes QRC en maquinari anal\u00f2gic com a part d'un programa m\u00e9s ampli sobre aprenentatge autom\u00e0tic qu\u00e0ntic per a aplicacions industrials. Aplicacions com previsi\u00f3 energ\u00e8tica, optimitzaci\u00f3 de la xarxa<\/strong> i fabricaci\u00f3 de bateries<\/strong> proporcionen casos de prova ideals perqu\u00e8 generen fluxos continus de dades de s\u00e8ries temporals, exactament el tipus de problemes per als quals el reservoir computing est\u00e0 dissenyat per resoldre.<\/span><\/p>

Aquest article ha presentat els principis del Computing de Reservori Qu\u00e0ntic. El seg\u00fcent mostrar\u00e0 com construir i simular un dip\u00f2sit qu\u00e0ntic utilitzant QiliSDK<\/strong>, el marc de codi obert de Qilimanjaro, amb simulacions que podeu executar vosaltres mateixos.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Publicacions cient\u00edfiques relacionades<\/strong><\/h1>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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El paper de la coher\u00e8ncia en la computaci\u00f3 de dip\u00f2sit qu\u00e0ntica de molts cossos<\/a><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Nature | Communication Physics<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Computaci\u00f3 de Reservori Qu\u00e0ntica<\/a><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

SHARE ARTICLE BY Carlos Ramon Escandell Jr Quantum Engineer Every industry now generates more data than it can easily process, and the machine learning models that analyse it keep getting more expensive to train and run. Quantum machine learning explores whether quantum systems can take on some of these learning tasks using fewer resources. One […]<\/p>\n","protected":false},"author":4,"featured_media":17201,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-17055","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science"],"yoast_head":"\nQuantum Reservoir Computing: Machine Learning Without Training the Quantum System - Qilimanjaro<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/qilimanjaro.tech\/ca\/quantum-reservoir-computing\/\" \/>\n<meta property=\"og:locale\" content=\"ca_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quantum Reservoir Computing: Machine Learning Without Training the Quantum System - Qilimanjaro\" \/>\n<meta property=\"og:description\" content=\"SHARE ARTICLE BY Carlos Ramon Escandell Jr Quantum Engineer Every industry now generates more data than it can easily process, and the machine learning models that analyse it keep getting more expensive to train and run. Quantum machine learning explores whether quantum systems can take on some of these learning tasks using fewer resources. 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Qilimanjaro","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/qilimanjaro.tech\/ca\/quantum-reservoir-computing\/","og_locale":"ca_ES","og_type":"article","og_title":"Quantum Reservoir Computing: Machine Learning Without Training the Quantum System - Qilimanjaro","og_description":"SHARE ARTICLE BY Carlos Ramon Escandell Jr Quantum Engineer Every industry now generates more data than it can easily process, and the machine learning models that analyse it keep getting more expensive to train and run. 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