930 research outputs found
The quantum adversary method and classical formula size lower bounds
We introduce two new complexity measures for Boolean functions, or more
generally for functions of the form f:S->T. We call these measures sumPI and
maxPI. The quantity sumPI has been emerging through a line of research on
quantum query complexity lower bounds via the so-called quantum adversary
method [Amb02, Amb03, BSS03, Zha04, LM04], culminating in [SS04] with the
realization that these many different formulations are in fact equivalent.
Given that sumPI turns out to be such a robust invariant of a function, we
begin to investigate this quantity in its own right and see that it also has
applications to classical complexity theory.
As a surprising application we show that sumPI^2(f) is a lower bound on the
formula size, and even, up to a constant multiplicative factor, the
probabilistic formula size of f. We show that several formula size lower bounds
in the literature, specifically Khrapchenko and its extensions [Khr71, Kou93],
including a key lemma of [Has98], are in fact special cases of our method.
The second quantity we introduce, maxPI(f), is always at least as large as
sumPI(f), and is derived from sumPI in such a way that maxPI^2(f) remains a
lower bound on formula size. While sumPI(f) is always a lower bound on the
quantum query complexity of f, this is not the case in general for maxPI(f). A
strong advantage of sumPI(f) is that it has both primal and dual
characterizations, and thus it is relatively easy to give both upper and lower
bounds on the sumPI complexity of functions. To demonstrate this, we look at a
few concrete examples, for three functions: recursive majority of three, a
function defined by Ambainis, and the collision problem.Comment: Appears in Conference on Computational Complexity 200
Lower Bounds on Quantum Query Complexity
Shor's and Grover's famous quantum algorithms for factoring and searching
show that quantum computers can solve certain computational problems
significantly faster than any classical computer. We discuss here what quantum
computers_cannot_ do, and specifically how to prove limits on their
computational power. We cover the main known techniques for proving lower
bounds, and exemplify and compare the methods.Comment: survey, 23 page
Symmetry-assisted adversaries for quantum state generation
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 {\sc Graph
Isomorphism} problem. We show that for the related problem of {\sc Index
Erasure} our method leads to a lower bound of which matches
an upper bound obtained via reduction to quantum search on 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 {\sc 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 \v{S}palek
[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 \v{S}palek to quantum state generation problems.Comment: 35 pages, 5 figure
- …