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IEICE Transactions on Information and Systems

Lower Bounds on Quantum Query Complexity for Read-Once Formulas with XOR and MUX Operators

Hideaki Fukuhara, Eiji Takimoto

Published Jan 1, 2010• 0 citations
Publisher

Abstract

We introduce a complexity measure r for the class $\mathcal{F}$ of read-once formulas over the basis {AND, OR, NOT, XOR, MUX} and show that for any Boolean formula F in the class $\mathcal{F}$, r(F) is a lower bound on the quantum query complexity of the Boolean function that F represents. We also show that for any Boolean function f represented by a formula in $\mathcal{F}$, the deterministic query complexity of f is only quadratically larger than the quantum query complexity of f. Thus, the paper gives further evidence for the conjecture that there is an only quadratic gap for all functions.

DOI
10.1587/transinf.e93.d.280

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We introduce a complexity measure r for the class F\mathcal{F}F of read-once formulas over the basis {AND, OR, NOT, XOR, MUX} and show that for any Boolean formula F in the class F\mathcal{F}F, r(F) is a lower bound on the quantum query complexity of the Boolean function that F represents. We also show that for any Boolean function f represented by a formula in F\mathcal{F}F, the deterministic query complexity of f is only quadratically larger than the quantum query complexity of f. Thus, the paper gives further evidence for the conjecture that there is an only quadratic gap for all functions.
We introduce a complexity measure for decision trees called the soft rank, which measures how well-balanced a given tree is. The soft rank is a somehow relaxed variant of the rank. Among all decision trees of depth d, the complete binary decision tree (the most balanced tree) has maximum soft rank d, the decision list (the most unbalanced tree) has minimum soft rank √d, and any other trees have soft rank between √d and d. We show that, for any decision tree T in some class G of decision trees which includes all read-once decision trees, the soft rank of T is a lower bound on the quantum query complexity of the Boolean function that T represents. This implies that for any Boolean function f that is represented by a decision tree in G, the deterministic query complexity of f is only quadratically larger than the quantum query complexity of f.
Quantum query complexity measures the number of input bits that must be read by a quantum algorithm in order to evaluate a function. Hoyer et al. (2007) have generalized the adversary semidefinite program that lower-bounds quantum query complexity. By giving a matching quantum algorithm, we show that the general adversary lower bound is tight for every boolean function. The proof is based on span programs, a linear-algebraic computational model without inherent dynamics. Span programs correspond to solutions to the dual semi-definite program, and to bipartite graphs. The analysis shows that properties of eigenvalue-zero eigenvectors of the graphs in fact imply an “effective” spectral gap around zero We thus develop a quantum algorithm for evaluating span programs. It follows that span programs, measured by witness size, and quantum algorithms, measured by query complexity, are equivalent computational models, up to a constant factor. The result efficiently characterizes the quantum query complexity of a read-once formula over any finite gate set. It also implies that the quantum query complexity of the composition f ◦ (g, . . . , g) of two boolean functions matches the product of the query complexities of f and g, without a logarithmic factor for error reduction. The algorithm alternates a fixed reflection with input queries. Originally introduced for solving the unstructured search problem, this structure is therefore a universal feature of quantum query algorithms. We give a second algorithm for evaluating span programs that has the further potential to be time-efficient. Subsequent applications have derived nearly time-optimal quantum algorithms for evaluating many read-once formulas. Span programs may have promise for developing more quantum algorithms.
One of the main reasons for query model's prominence in quantum complexity is the presence of concrete lower bounding techniques: polynomial method and adversary method. There have been considerable efforts to not just give lower bounds using these methods but even to compare and relate them. We explore the value of these bounds on quantum query complexity for the class of symmetric functions, arguably one of the most natural and basic set of Boolean functions. We show (using the recently introduced spectral sensitivity) that both these bounds (positive adversary and approximate degree) give the same value for every total symmetric Boolean function. We also look at the quantum query complexity of Gap Majority, a partial symmetric function. It has gained importance recently in regard to understanding the composition of randomized query complexity. We characterize the quantum query complexity of Gap Majority and show a lower bound on noisy randomized query complexity (Ben-David and Blais, FOCS 2020) in terms of quantum query complexity. In addition, we study how large certificate complexity and block sensitivity can be as compared to sensitivity (even up to constant factors) for symmetric functions. We show tight separations, i.e., give upper bound on possible separations and construct functions achieving the same.
It is well known that quantum, randomized and deterministic (sequential) query complexities are polynomially related for total boolean functions. We find that significantly larger separations between the parallel generalizations of these measures are possible. In particular, 1) We employ the cheatsheet framework to obtain an unbounded parallel quantum query advantage over its randomized analogue for a total function, falsifying a conjecture of [https://arxiv.org/abs/1309.6116]. 2) We strengthen 1 by constructing a total function which exhibits an unbounded parallel quantum query advantage despite having no sequential advantage, suggesting that genuine quantum advantage could occur entirely due to parallelism. 3) We construct a total function that exhibits a polynomial separation between 2-round quantum and randomized query complexities, contrasting a result of [https://arxiv.org/abs/1001.0018] that there is at most a constant separation for 1-round (nonadaptive) algorithms. 4) We develop a new technique for deriving parallel quantum lower bounds from sequential upper bounds. We employ this technique to give lower bounds for Boolean symmetric functions and read-once formulas, ruling out large parallel query advantages for them. We also provide separations between randomized and deterministic parallel query complexities analogous to items 1-3.
The degree of a polynomial representing (or approximating) a function f is a lower bound for the quantum query complexity of f. This observation has been a source of many lower bounds on quantum algorithms. It has been an open problem whether this lower bound is tight. This is why Boolean functions are needed with a high number of essential variables and a low polynomial degree. Unfortunately, it is a well-known problem to construct such functions. The best separation between these two complexity measures of a Boolean function was exhibited by Ambai- nis [5]. He constructed functions with polynomial degree M and number of variables Omega(M2). We improve such a separation to become exponential. On the other hand, we use a computerized exhaustive search to prove tightness of this bound.
It is well known that quantum, randomized and deterministic (sequential) query complexities are polynomially related for total boolean functions. We find that significantly larger separations between the parallel generalizations of these measures are possible. In particular, 1) We employ the cheatsheet framework to obtain an unbounded parallel quantum query advantage over its randomized analogue for a total function, falsifying a conjecture of [https://arxiv.org/abs/1309.6116]. 2) We strengthen 1 by constructing a total function which exhibits an unbounded parallel quantum query advantage despite having no sequential advantage, suggesting that genuine quantum advantage could occur entirely due to parallelism. 3) We construct a total function that exhibits a polynomial separation between 2-round quantum and randomized query complexities, contrasting a result of [https://arxiv.org/abs/1001.0018] that there is at most a constant separation for 1-round (nonadaptive) algorithms. 4) We develop a new technique for deriving parallel quantum lower bounds from sequential upper bounds. We employ this technique to give lower bounds for Boolean symmetric functions and read-once formulas, ruling out large parallel query advantages for them. We also provide separations between randomized and deterministic parallel query complexities analogous to items 1-3.