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• W3104289311 abstract "Traditional quantum state tomography requires a number of measurements that grows exponentially with the number of qubits n . But using ideas from computational learning theory, we show that one can do exponentially better in a statistical setting. In particular, to predict the outcomes of most measurements drawn from an arbitrary probability distribution, one needs only a number of sample measurements that grows linearly with n . This theorem has the conceptual implication that quantum states, despite being exponentially long vectors, are nevertheless ‘reasonable’ in a learning theory sense. The theorem also has two applications to quantum computing: first, a new simulation of quantum one-way communication protocols and second, the use of trusted classical advice to verify untrusted quantum advice." @default.
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• W3104289311 date "2007-09-11" @default.
• W3104289311 modified "2023-09-29" @default.
• W3104289311 title "The learnability of quantum states" @default.
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• W3104289311 doi "https://doi.org/10.1098/rspa.2007.0113" @default.
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