Hardness of approximation for quantum problems
S. Gharibian, J. Kempe, Quantum Information & Computation 14 (2014) 517–540.
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Author
Gharibian, SevagLibreCat ;
Kempe, Julia
Abstract
The polynomial hierarchy plays a central role in classical complexity theory. Here, we define
a quantum generalization of the polynomial hierarchy, and initiate its study. We show that
not only are there natural complete problems for the second level of this quantum hierarchy, but that these problems are in fact hard to approximate. Using the same techniques, we
also obtain hardness of approximation for the class QCMA. Our approach is based on the
use of dispersers, and is inspired by the classical results of Umans regarding hardness of approximation for the second level of the classical polynomial hierarchy [Umans, FOCS 1999].
The problems for which we prove hardness of approximation for include, among others, a
quantum version of the Succinct Set Cover problem, and a variant of the local Hamiltonian
problem with hybrid classical-quantum ground states.
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Publishing Year
Journal Title
Quantum Information & Computation
Volume
14
Issue
5-6
Page
517-540
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Cite this
Gharibian S, Kempe J. Hardness of approximation for quantum problems. Quantum Information & Computation. 2014;14(5-6):517-540.
Gharibian, S., & Kempe, J. (2014). Hardness of approximation for quantum problems. Quantum Information & Computation, 14(5–6), 517–540.
@article{Gharibian_Kempe_2014, title={Hardness of approximation for quantum problems}, volume={14}, number={5–6}, journal={Quantum Information & Computation}, author={Gharibian, Sevag and Kempe, Julia}, year={2014}, pages={517–540} }
Gharibian, Sevag, and Julia Kempe. “Hardness of Approximation for Quantum Problems.” Quantum Information & Computation 14, no. 5–6 (2014): 517–40.
S. Gharibian and J. Kempe, “Hardness of approximation for quantum problems,” Quantum Information & Computation, vol. 14, no. 5–6, pp. 517–540, 2014.
Gharibian, Sevag, and Julia Kempe. “Hardness of Approximation for Quantum Problems.” Quantum Information & Computation, vol. 14, no. 5–6, 2014, pp. 517–40.
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