Machine learning for quantum physics
An artificial neural network can discover the ground state of a quantum many-body system Machine learning has been used to beat a human competitor in a game of Go ( 1 ), a game that has long been viewed as the most challenging of board games for artificial intelligence. Research is now under way to...
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| Veröffentlicht in: | Science (American Association for the Advancement of Science) 2017-02, Vol.355 (6325), p.580-580 |
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| container_title | Science (American Association for the Advancement of Science) |
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| creator | Hush, Michael R. |
| description | An artificial neural network can discover the ground state of a quantum many-body system
Machine learning has been used to beat a human competitor in a game of Go (
1
), a game that has long been viewed as the most challenging of board games for artificial intelligence. Research is now under way to investigate whether machine learning can be used to solve long outstanding problems in quantum science. On page 602 of this issue, Carleo and Troyer (
2
) use machine learning on one of quantum science's greatest challenges: the simulation of quantum many-body systems. Carleo and Troyer used an artificial neural network to represent the wave function of a quantum many-body system and to make the neural network "learn" what the ground state (or dynamics) of the system is. Their approach is found to perform better than the current state-of-the-art numerical simulation methods. |
| doi_str_mv | 10.1126/science.aam6564 |
| format | Article |
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Machine learning has been used to beat a human competitor in a game of Go (
1
), a game that has long been viewed as the most challenging of board games for artificial intelligence. Research is now under way to investigate whether machine learning can be used to solve long outstanding problems in quantum science. On page 602 of this issue, Carleo and Troyer (
2
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1
), a game that has long been viewed as the most challenging of board games for artificial intelligence. Research is now under way to investigate whether machine learning can be used to solve long outstanding problems in quantum science. On page 602 of this issue, Carleo and Troyer (
2
) use machine learning on one of quantum science's greatest challenges: the simulation of quantum many-body systems. Carleo and Troyer used an artificial neural network to represent the wave function of a quantum many-body system and to make the neural network "learn" what the ground state (or dynamics) of the system is. 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1
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2
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| fulltext | fulltext |
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| ispartof | Science (American Association for the Advancement of Science), 2017-02, Vol.355 (6325), p.580-580 |
| issn | 0036-8075 1095-9203 |
| language | eng |
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| source | Jstor Complete Legacy; MEDLINE; Science Magazine |
| subjects | Artificial intelligence Artificial neural networks Boards Computer simulation Dynamical systems Expert systems Games Learning algorithms Machine Learning Mathematical models Neural networks Numerical methods PERSPECTIVES Physics Quantum Theory Simulation Wave functions |
| title | Machine learning for quantum physics |
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