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|a Lloyd, Seth
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|a Massachusetts Institute of Technology. Department of Mechanical Engineering
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|a Massachusetts Institute of Technology. Research Laboratory of Electronics
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|a Lloyd, Seth
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|a Rebentrost, Frank Patrick
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|a Mohseni, Masoud
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|a Rebentrost, Frank Patrick
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|a Quantum principal component analysis
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|b Nature Publishing Group,
|c 2015-07-01T18:41:03Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/97628
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|a The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyse the results statistically. For non-sparse but low-rank quantum states, revealing eigenvectors and corresponding eigenvalues in classical form scales super-linearly with the system dimension. Here we show that multiple copies of a quantum system with density matrix ρ can be used to construct the unitary transformation e[superscript −iρt]. As a result, one can perform quantum principal component analysis of an unknown low-rank density matrix, revealing in quantum form the eigenvectors corresponding to the large eigenvalues in time exponentially faster than any existing algorithm. We discuss applications to data analysis, process tomography and state discrimination.
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|a United States. Defense Advanced Research Projects Agency
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|a United States. National Aeronautics and Space Administration. Quantum Artificial Intelligence Laboratory
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|a United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative
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|a en_US
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|a Article
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|t Nature Physics
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