Query Complexity in Expectation

We study the query complexity of computing a function

$$f:\{0,1\}^n\rightarrow \mathbb {R}_+$$

f

:

{

0

,

1

}

n

→

R

+

in expectation

. This requires the algorithm on input

$$x$$

x

to output a nonnegative random variable whose expectation equals

$$f(x)$$

f

(

x

)

, using as few queries to the input

$$x$$

x

as possible. We exactly characterize both the randomized and the quantum query complexity by two polynomial degrees, the nonnegative literal degree and the sum-of-squares degree, respectively. We observe that the quantum complexity can be unboundedly smaller than the classical complexity for some functions, but can be at most polynomially smaller for Boolean functions. These query complexities relate to (and are motivated by) the extension complexity of polytopes. The

linear

extension complexity of a polytope is characterized by the randomized

communication

complexity of computing its slack matrix in expectation, and the

semidefinite

(psd) extension complexity is characterized by the analogous quantum model. Since query complexity can be used to upper bound communication complexity of related functions, we can derive some upper bounds on psd extension complexity by constructing efficient quantum query algorithms. As an example we give an exponentially-close entrywise approximation of the slack matrix of the perfect matching polytope with psd-rank only

$$2^{n^{1/2+\varepsilon }}$$

2

n

1

/

2

+

ε

. Finally, we show randomized and quantum query complexity in expectation corresponds to the Sherali-Adams and Lasserre hierarchies, respectively.