Quantum Algorithms for the Triangle Problem

We present two new quantum algorithms that either find a triangle (a copy of $K_{3}$) in an undirected graph $G$ on $n$ nodes, or reject if $G$ is triangle free. The first algorithm uses combinatorial ideas with Grover Search and makes $\tilde{O}(n^{10/7})$ queries. The second algorithm uses $\tilde{O}(n^{13/10})$ queries, and it is based on a design concept of Ambainis~\cite{amb04} that incorporates the benefits of quantum walks into Grover search~\cite{gro96}. The first algorithm uses only $O(\log n)$ qubits in its quantum subroutines, whereas the second one uses O(n) qubits. The Triangle Problem was first treated in~\cite{bdhhmsw01}, where an algorithm with $O(n+\sqrt{nm})$ query complexity was presented, where $m$ is the number of edges of $G$.Comment: Several typos are fixed, and full proofs are included. Full version of the paper accepted to SODA'0

The Quantum Query Complexity of Algebraic Properties

We present quantum query complexity bounds for testing algebraic properties. For a set S and a binary operation on S, we consider the decision problem whether $S$ is a semigroup or has an identity element. If S is a monoid, we want to decide whether S is a group. We present quantum algorithms for these problems that improve the best known classical complexity bounds. In particular, we give the first application of the new quantum random walk technique by Magniez, Nayak, Roland, and Santha that improves the previous bounds by Ambainis and Szegedy. We also present several lower bounds for testing algebraic properties.Comment: 13 pages, 0 figure

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

Quantum and Classical Tradeoffs

We propose an approach for quantifying a quantum circuit's quantumness as a means to understand the nature of quantum algorithmic speedups. Since quantum gates that do not preserve the computational basis are necessary for achieving quantum speedups, it appears natural to define the quantumness of a quantum circuit using the number of such gates. Intuitively, a reduction in the quantumness requires an increase in the amount of classical computation, hence giving a ``quantum and classical tradeoff''. In this paper we present two results on this direction. The first gives an asymptotic answer to the question: ``what is the minimum number of non-basis-preserving gates required to generate a good approximation to a given state''. This question is the quantum analogy of the following classical question, ``how many fair coins are needed to generate a given probability distribution'', which was studied and resolved by Knuth and Yao in 1976. Our second result shows that any quantum algorithm that solves Grover's Problem of size n using k queries and l levels of non-basis-preserving gates must have k*l=\Omega(n)