O que hádentro de um buraco negro? Fasico usa computação qua¢ntica e aprendizado de ma¡quina para descobrir
A dualidade hologra¡fica éuma conjectura matemática que conecta teorias departículas e suas interaçaµes com a teoria da gravidade.
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)
Enrico Rinaldi, pesquisador do Departamento de Fasica da Universidade de Michigan, estãousando dois manãtodos de simulação para resolver modelos de matriz qua¢ntica que podem descrever como éa gravidade de um buraco negro. Nesta imagem, uma representação picta³rica do Espaço-tempo curvo conecta os dois manãtodos de simulação. Na parte inferior, um manãtodo de aprendizado profundo érepresentado por gra¡ficos de pontos (rede neural), enquanto o manãtodo de circuito qua¢ntico na parte superior érepresentado por linhas, quadrados e carculos (qubits e portas). Os manãtodos de simulação se fundem com cada lado do Espaço-tempo curvo para representar o fato de que as propriedades da gravidade saem das simulações. Rinaldi estãosediado em Ta³quio e hospedado pelo Laborata³rio de Fasica Qua¢ntica Tea³rica no Cluster for Pioneering Research em RIKEN, Wako. Crédito: Enrico Rinaldi/UM, RIKEN e A.
Cara, e se tudo ao nosso redor fosse apenas... um holograma?
O problema éque pode ser ose um fasico da Universidade de Michigan estãousando computação qua¢ntica e aprendizado de ma¡quina para entender melhor a ideia, chamada dualidade hologra¡fica.
A dualidade hologra¡fica éuma conjectura matemática que conecta teorias departículas e suas interações com a teoria da gravidade. Essa conjectura sugere que a teoria da gravidade e a teoria daspartículas são matematicamente equivalentes: o que acontece matematicamente na teoria da gravidade acontece na teoria daspartículas e vice-versa.
Ambas as teorias descrevemDimensões diferentes, mas o número deDimensões que descrevem difere em uma. Assim, dentro da forma de um buraco negro, por exemplo, a gravidade existe em trêsDimensões , enquanto uma teoria departículas existe em duasDimensões , em suasuperfÍcie osum disco plano.
Para visualizar isso, pense novamente no buraco negro, que distorce o Espaço-tempo por causa de sua imensa massa. A gravidade do buraco negro, que existe em trêsDimensões , se conecta matematicamente a spartículas que dançam acima dele, em duasDimensões . Portanto, um buraco negro existe em um espaço tridimensional, mas noso vemos como projetado atravanãs de partículas
Alguns cientistas teorizam que todo o nosso universo éuma projeção hologra¡fica departículas, e isso pode levar a uma teoria qua¢ntica consistente da gravidade.
"Na teoria da Relatividade Geral de Einstein, não hápartaculas - hápenas Espaço-tempo. E no Modelo Padra£o da física departículas, não hágravidade, hápenaspartículas", disse Enrico Rinaldi, pesquisador do Departamento de Fasica da UM. "Conectar as duas teorias diferentes éuma questãode longa data na física - algo que as pessoas vão tentando fazer desde o século passado."
Em um estudo publicado na revista PRX Quantum , Rinaldi e seus coautores examinam como investigar a dualidade hologra¡fica usando computação qua¢ntica e aprendizado profundo para encontrar o estado de energia mais baixo de problemas matema¡ticos chamados modelos de matriz qua¢ntica.
Esses modelos de matriz qua¢ntica são representações da teoria das partículas Como a dualidade hologra¡fica sugere que o que acontece matematicamente em um sistema que representa a teoria daspartículas afetara¡ da mesma forma um sistema que representa a gravidade, resolver esse modelo de matriz qua¢ntica pode revelar informações sobre a gravidade.
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Para o estudo, Rinaldi e sua equipe usaram dois modelos de matriz simples o suficiente para serem resolvidos usando manãtodos tradicionais, mas que possuem todas as caracteristicas de modelos de matriz mais complicados usados ​​para descrever buracos negros atravanãs da dualidade hologra¡fica.
"Esperamos que, ao entender as propriedades dessa teoria departículas por meio de experimentos numanãricos, entendamos algo sobre a gravidade", disse Rinaldi, baseado em Ta³quio e hospedado pelo Laborata³rio de Fasica Qua¢ntica Tea³rica do Cluster for Pioneering Research em RIKEN, Wako. . "Infelizmente ainda não éfa¡cil resolver as teorias das partículas E éaa que os computadores podem nos ajudar."
Esses modelos de matriz são blocos de números que representam objetos na teoria das cordas, que éuma estrutura na qual aspartículas na teoria daspartículas são representadas por cordas unidimensionais. Quando os pesquisadores resolvem modelos de matriz como esses, eles estãotentando encontrar a configuração especafica daspartículas no sistema que representam o estado de energia mais baixo do sistema, chamado de estado fundamental. No estado fundamental, nada acontece ao sistema, a menos que vocêadicione algo a ele que o perturbe.
"a‰ realmente importante entender como éesse estado fundamental, porque assim vocêpode criar coisas a partir dele", disse Rinaldi. "Então, para um material, conhecer o estado fundamental écomo saber, por exemplo, se éum condutor, ou se éum supercondutor, ou se érealmente forte ou fraco. Mas encontrar esse estado fundamental entre todos os estados possaveis éuma tarefa bastante difacil. a‰ por isso que estamos usando esses manãtodos numanãricos."
Vocaª pode pensar nos números nos modelos de matriz como gra£os de areia, diz Rinaldi. Quando a areia estãonivelada, esse éo estado fundamental do modelo. Mas se houver ondulações na areia, vocêprecisa encontrar uma maneira de nivela¡-las. Para resolver isso, os pesquisadores primeiro olharam para circuitos qua¢nticos. Nesse manãtodo, os circuitos qua¢nticos são representados por fios, e cada qubit, ou bit de informação qua¢ntica, éum fio. No topo dos fios estãoos portaµes, que são operações qua¢nticas que ditam como a informação passara¡ pelos fios.
"Vocaª pode laª-los como música, indo da esquerda para a direita", disse Rinaldi. "Se vocêlaª isso como música, vocêestãobasicamente transformando os qubits desde o inicio em algo novo a cada passo. Mas vocênão sabe quais operações vocêdeve fazer a medida que avana§a, quais notas tocar. O processo de agitação ira¡ ajustar todos esses portaµes para fazaª-los tomar a forma correta, de modo que no final de todo o processo, vocêalcance o estado fundamental. Então vocêtem toda essa música, e se vocêtocar direito, no final, vocêtem o estado fundamental. "
Os pesquisadores queriam comparar o uso desse manãtodo de circuito qua¢ntico com o uso de um manãtodo de aprendizado profundo. O aprendizado profundo éum tipo de aprendizado de ma¡quina que usa uma abordagem de rede neural osuma sanãrie de algoritmos que tenta encontrar relacionamentos nos dados, semelhante ao funcionamento do cérebro humano.
As redes neurais são usadas para projetar softwares de reconhecimento facial, alimentando milhares de imagens de rostos osdas quais desenham pontos de referaªncia específicos do rosto para reconhecer imagens individuais ou gerar novos rostos de pessoas que não existem.
No estudo de Rinaldi, os pesquisadores definem a descrição matemática do estado qua¢ntico de seu modelo de matriz, chamado de função de onda qua¢ntica. Em seguida, eles usam uma rede neural especial para encontrar a função de onda da matriz com a menor energia possível osseu estado fundamental. Os números da rede neural passam por um processo de "otimização" iterativo para encontrar o estado fundamental do modelo de matriz, batendo no balde de areia para que todos os seus gra£os sejam nivelados.
Em ambas as abordagens, os pesquisadores conseguiram encontrar o estado fundamental de ambos os modelos de matriz que examinaram, mas os circuitos qua¢nticos são limitados por um pequeno número de qubits. O hardware qua¢ntico atual são pode lidar com algumas dezenas de qubits: adicionar linhas a sua partitura se torna caro e, quanto mais vocêadiciona, com menos precisão vocêpode tocar a música.
"Outros manãtodos que as pessoas normalmente usam podem encontrar a energia do estado fundamental, mas não toda a estrutura da função de onda", disse Rinaldi. "Mostramos como obter todas as informações sobre o estado fundamental usando essas novas tecnologias emergentes, computadores qua¢nticos e aprendizado profundo .
“Como essas matrizes são uma representação possível para um tipo especial de buraco negro, se soubermos como as matrizes estãoorganizadas e quais são suas propriedades, podemos saber, por exemplo, como éum buraco negro por dentro. o horizonte de eventos para um buraco negro? De onde ele vem? Responder a essas perguntas seria um passo para realizar uma teoria qua¢ntica da gravidade ."
Os resultados, diz Rinaldi, mostram uma referaªncia importante para trabalhos futuros em algoritmos qua¢nticos e de aprendizado de ma¡quina que os pesquisadores podem usar para estudar a gravidade qua¢ntica por meio da ideia de dualidade hologra¡fica.
Os coautores de Rinaldi incluem Xizhi Han na Universidade de Stanford; Mohammad Hassan no City College de Nova York; Yuan Feng no Pasadena City College; Franco Nori na UM e RIKEN; Michael McGuigan no Laborata³rio Nacional de Brookhaven e Masanori Hanada na Universidade de Surrey.
Em seguida, Rinaldi estãotrabalhando com Nori e Hanada para estudar como os resultados desses algoritmos podem ser dimensionados para matrizes maiores, bem como quanto robustos eles são contra a introdução de efeitos "ruidosos" ou interferaªncias que podem introduzir erros.