We give and prove an optimal exact quantum query algorithm with complexity
k+1 for computing the promise problem (i.e., symmetric and partial Boolean function)
DJnk defined as:
DJnk(x)=1 for
∣x∣=n/2,
DJnk(x)=0 for
∣x∣ in the set
{0,1,…,k,n−k,n−k+1,…,n}, and it is undefined for the rest cases, where
n is even,
∣x∣ is the Hamming weight of
x. The case of
k=0 is the well-known Deutsch-Jozsa problem. We outline all symmetric (and partial) Boolean functions with degrees 1 and 2, and prove their exact quantum query complexity. Then we prove that any symmetrical (and partial) Boolean function
f has exact quantum 1-query complexity if and only if
f can be computed by the Deutsch-Jozsa algorithm. We also discover the optimal exact quantum 2-query complexity for distinguishing between inputs of Hamming weight
{⌊n/2⌋,⌈n/2⌉} and Hamming weight in the set
{0,n} for all odd
n. In addition, a method is provided to determine the degree of any symmetrical (and partial) Boolean function.
We present several families of total boolean functions which have exact quantum query com-plexity which is a constant multiple (between 1/2 and 2/3) of their classical query complexity, and show that optimal quantum algorithms for these functions cannot be obtained by simply computing parities of pairs of bits. We also characterise the model of nonadaptive exact quantum query complexity in terms of coding theory and completely characterise the query complexity of symmetric boolean functions in this context. These results were originally inspired by nu-merically solving the semidefinite programs characterising quantum query complexity for small problem sizes. We include numerical results giving the optimal success probabilities achievable by quantum algorithms computing all boolean functions on up to 4 bits, and all symmetric boolean functions on up to 6 bits. 1
Computational models typically assume that operations are applied in a fixed sequential order.In recent years several works have looked at relaxing this assumption, considering computations without any fixed causal structure and showing that such "causally indefinite" computations can provide advantages in various tasks.Recently, the quantum query complexity of Boolean functions has been used as a tool to probe their computational power in a standard complexity theoretic framework, but no separation in exact query complexity has thus-far been found.In this paper, we investigate this problem starting with the simpler and fully classical notion of deterministic query complexity of Boolean functions, and using classical-deterministic processes -which may exhibit causal indefiniteness -as a generalised computational framework.We first show that the standard polynomial and certificate lower bounds of deterministic query complexity also hold in such generalised models.Then, we formulate a Boolean function for which causal indefiniteness permits a reduction in query complexity and show that this advantage can be amplified into a polynomial separation.Finally, with the insights gained in the classical-deterministic setting, we give a Boolean function whose quantum query complexity is reduced by causally indefinite computations.