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Register a new userThe quantum query complexity of certification
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We study the quantum query complexity of finding a certificate for a d-regular, k-level balanced NAND formula. Up to logarithmic factors, we show that the query complexity is Theta(d^{(k+1)/2}) for 0-certificates, and Theta(d^{k/2}) for 1-certificates. In particular, this shows that the zero-error quantum query complexity of evaluating such formulas is O(d^{(k+1)/2}) (again neglecting a logarithmic factor). Our lower bound relies on the fact that the quantum adversary method obeys a direct sum theorem.
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We combine the classical notions and techniques for bounded query classes with those developed in quantum computing. We give strong evidence that quantum queries to an oracle in the class NP does indeed reduce the query complexity of decision problems. Under traditional complexity assumptions, we obtain an exponential speedup between the quantum and the classical query complexity of function classes. For decision problems and function classes we obtain the following results: o P_||^NP[2k] is included in EQP_||^NP[k] o P_||^NP[2^(k+1)-2] is included in EQP^NP[k] o FP_||^NP[2^(k+1)-2] is included in FEQP^NP[2k] o FP_||^NP is included in FEQP^NP[O(log n)] For sets A that are many-one complete for PSPACE or EXP we show that FP^A is included in FEQP^A[1]. Sets A that are many-one complete for PP have the property that FP_||^A is included in FEQP^A[1]. In general we prove that for any set A there is a set X such that FP^A is included in FEQP^X[1], establishing that no set is superterse in the quantum setting.
We present three new quantum algorithms in the quantum query model for textsc{graph-collision} problem: begin{itemize} item an algorithm based on tree decomposition that uses $Oleft(sqrt{n}t^{sfrac{1}{6}}right)$ queries where $t$ is the treewidth of the graph; item an algorithm constructed on a span program that improves a result by Gavinsky and Ito. The algorithm uses $O(sqrt{n}+sqrt{alpha^{**}})$ queries, where $alpha^{**}(G)$ is a graph parameter defined by [alpha^{**}(G):=min_{VCtext{-- vertex cover of}G}{max_{substack{Isubseteq VCItext{-- independent set}}}{sum_{vin I}{deg{v}}}};] item an algorithm for a subclass of circulant graphs that uses $O(sqrt{n})$ queries. end{itemize} We also present an example of a possibly difficult graph $G$ for which all the known graphs fail to solve graph collision in $O(sqrt{n} log^c n)$ queries.
The negative weight adversary method, $mathrm{ADV}^pm(g)$, is known to characterize the bounded-error quantum query complexity of any Boolean function $g$, and also obeys a perfect composition theorem $mathrm{ADV}^pm(f circ g^n) = mathrm{ADV}^pm(f) mathrm{ADV}^pm(g)$. Belovs gave a modified version of the negative weight adversary method, $mathrm{ADV}_{rel}^pm(f)$, that characterizes the bounded-error quantum query complexity of a relation $f subseteq {0,1}^n times [K]$, provided the relation is efficiently verifiable. A relation is efficiently verifiable if $mathrm{ADV}^pm(f_a) = o(mathrm{ADV}_{rel}^pm(f))$ for every $a in [K]$, where $f_a$ is the Boolean function defined as $f_a(x) = 1$ if and only if $(x,a) in f$. In this note we show a perfect composition theorem for the composition of a relation $f$ with a Boolean function $g$ [ mathrm{ADV}_{rel}^pm(f circ g^n) = mathrm{ADV}_{rel}^pm(f) mathrm{ADV}^pm(g) enspace . ] For an efficiently verifiable relation $f$ this means $Q(f circ g^n) = Theta( mathrm{ADV}_{rel}^pm(f) mathrm{ADV}^pm(g) )$.
We study quantum algorithms that learn properties of a matrix using queries that return its action on an input vector. We show that for various problems, including computing the trace, determinant, or rank of a matrix or solving a linear system that it specifies, quantum computers do not provide an asymptotic speedup over classical computation. On the other hand, we show that for some problems, such as computing the parities of rows or columns or deciding if there are two identical rows or columns, quantum computers provide exponential speedup. We demonstrate this by showing equivalence between models that provide matrix-vector products, vector-matrix products, and vector-matrix-vector products, whereas the power of these models can vary significantly for classical computation.
I offer a case that quantum query complexity still has loads of enticing and fundamental open problems -- from relativized QMA versus QCMA and BQP versus IP, to time/space tradeoffs for collision and element distinctness, to polynomial degree versus quantum query complexity for partial functions, to the Unitary Synthesis Problem and more.
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