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2011 26th Annual IEEE Conference on Computational Complexity
Symmetry-Assisted Adversaries for Quantum State Generation
San Jose, California USA
June 08-June 11
ISBN: 978-0-7695-4411-3
We introduce a new quantum adversary method to prove lower bounds on the query complexity of the quantum state generation problem. This problem encompasses both, the computation of partial or total functions and the preparation of target quantum states. There has been hope for quite some time that quantum state generation might be a route to tackle the GRAPH-ISOMORPHISM problem. We show that for the related problem of INDEX-ERASURE our method leads to a lower bound of square root of N which matches an upper bound obtained via reduction to quantum search on N elements. This closes an open problem first raised by Shi [FOCS'02]. Our approach is based on two ideas: (i) on the one hand we generalize the known additive and multiplicative adversary methods to the case of quantum state generation, (ii) on the other hand we show how the symmetries of the underlying problem can be leveraged for the design of optimal adversary matrices and dramatically simplify the computation of adversary bounds. Taken together, these two ideas give the new result for INDEX-ERASURE by using the representation theory of the symmetric group. Also, the method can lead to lower bounds even for small success probability, contrary to the standard adversary method. Furthermore, we answer an open question due to Spalek [CCC'08] by showing that the multiplicative version of the adversary method is stronger than the additive one for any problem. Finally, we prove that the multiplicative bound satisfies a strong direct product theorem, extending a result by Spalek to quantum state generation problems.
Index Terms:
quantum query complexity, adversary method, strong direct product theorem, index-erasure
Citation:
Andris Ambainis, Loïck Magnin, Martin Roetteler, Jérémie Roland, "Symmetry-Assisted Adversaries for Quantum State Generation," ccc, pp.167-177, 2011 26th Annual IEEE Conference on Computational Complexity, 2011
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