Inapproximability of Positive Semidefinite Permanents and Quantum State Tomography

2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)(2023)

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摘要
Matrix permanents are hard to compute or even estimate in general. It had been previously suggested that the permanents of Positive Semidefinite (PSD) matrices may have efficient approximations. By relating PSD permanents to a task in quantum state tomography, we show that PSD permanents are NP-hard to approximate within a constant factor, and so admit no polynomial-time approximation scheme (unless P = NP). We also establish that several natural tasks in quantum state tomography, even approximately, are NP-hard in the dimension of the Hilbert space. These state tomography tasks therefore remain hard even with only logarithmically few qubits.
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关键词
Matrix permanent,Hermitian matrix,NP-hard,Quantum state tomography,Positive semidefinite
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