Polynomial Degree and Lower Bounds in Quantum Complexity: Collision and Element Distinctness with Small Range

We give a general method for proving quantum lower bounds for problems with small range. Namely, we show that, for any symmetric problem defined on functions $f:\{1, ..., N\}\to\{1, ..., M\}$, its polynomial degree is the same for all $M\geq N$. Therefore, if we have a quantum lower bound for some (possibly, quite large) range $M$ which is shown using polynomials method, we immediately get the same lower bound for all ranges $M\geq N$. In particular, we get $\Omega(N^{1/3})$ and $\Omega(N^{2/3})$ quantum lower bounds for collision and element distinctness with small range.Comment: 9 pages, LaTeX, v2 new result on degree lower bound for AND-OR added, v3 many small change

Quantum search with variable times

Since Grover's seminal work, quantum search has been studied in great detail. In the usual search problem, we have a collection of n items and we would like to find a marked item. We consider a new variant of this problem in which evaluating the i-th item may take a different number of time steps for different i. Let t_i be the number of time steps required to evaluate the i-th item. If the numbers t_i are known in advance, we give an algorithm that solves the problem in O(\sqrt{t_1^2+t_2^2+...+t_n^2}) steps. This is optimal, as we also show a matching lower bound. The case, when t_i are not known in advance, can be solved with a polylogarithmic overhead. We also give an application of our new search algorithm to computing read-once functions.Comment: 19 page

Variable time amplitude amplification and a faster quantum algorithm for solving systems of linear equations

We present two new quantum algorithms. Our first algorithm is a generalization of amplitude amplification to the case when parts of the quantum algorithm that is being amplified stop at different times. Our second algorithm uses the first algorithm to improve the running time of Harrow et al. algorithm for solving systems of linear equations from O(kappa^2 log N) to O(kappa log^3 kappa log N) where \kappa is the condition number of the system of equations.Comment: 17 pages, no figures, v2: various small correction

Quantum algorithms for formula evaluation

We survey the recent sequence of algorithms for evaluating Boolean formulas consisting of NAND gates.Comment: 11 pages, survey for NATO ARW "Quantum Cryptography and Computing", Gdansk, September 200

A New Protocol and Lower Bounds for Quantum Coin Flipping

We present a new protocol and two lower bounds for quantum coin flipping. In our protocol, no dishonest party can achieve one outcome with probability more than 0.75. Then, we show that our protocol is optimal for a certain type of quantum protocols. For arbitrary quantum protocols, we show that if a protocol achieves a bias of at most $\epsilon$, it must use at least $\Omega(\log \log \frac{1}{\epsilon})$ rounds of communication. This implies that the parallel repetition fails for quantum coin flipping. (The bias of a protocol cannot be arbitrarily decreased by running several copies of it in parallel.)Comment: 20 pages, submitted to Journal of Computer and System Sciences, earlier version in proceedings of STOC'0

Understanding Quantum Algorithms via Query Complexity

Query complexity is a model of computation in which we have to compute a function $f(x_1, \ldots, x_N)$ of variables $x_i$ which can be accessed via queries. The complexity of an algorithm is measured by the number of queries that it makes. Query complexity is widely used for studying quantum algorithms, for two reasons. First, it includes many of the known quantum algorithms (including Grover's quantum search and a key subroutine of Shor's factoring algorithm). Second, one can prove lower bounds on the query complexity, bounding the possible quantum advantage. In the last few years, there have been major advances on several longstanding problems in the query complexity. In this talk, we survey these results and related work, including: - the biggest quantum-vs-classical gap for partial functions (a problem solvable with 1 query quantumly but requiring $\Omega(\sqrt{N})$ queries classically); - the biggest quantum-vs-determistic and quantum-vs-probabilistic gaps for total functions (for example, a problem solvable with $M$ queries quantumly but requiring $\tilde{\Omega}(M^{2.5})$ queries probabilistically); - the biggest probabilistic-vs-deterministic gap for total functions (a problem solvable with $M$ queries probabilistically but requiring $\tilde{\Omega}(M^{2})$ queries deterministically); - the bounds on the gap that can be achieved for subclasses of functions (for example, symmetric functions); - the connections between query algorithms and approximations by low-degree polynomials.Comment: 20 page survey of recent results, for Proceedings of International Congress of Mathematicians'201

Polynomial degree vs. quantum query complexity

The degree of a polynomial representing (or approximating) a function f is a lower bound for the number of quantum queries needed to compute 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. We exhibit a function with polynomial degree M and quantum query complexity \Omega(M^{1.321...}). This is the first superlinear separation between polynomial degree and quantum query complexity. The lower bound is shown by a new, more general version of quantum adversary method.Comment: 23 pages, 1 figure, v4 "proof by old method" corrected, moderate changes to presentation elsewher

Quantum walk algorithm for element distinctness

We use quantum walks to construct a new quantum algorithm for element distinctness and its generalization. For element distinctness (the problem of finding two equal items among N given items), we get an O(N^{2/3}) query quantum algorithm. This improves the previous O(N^{3/4}) query quantum algorithm of Buhrman et.al. (quant-ph/0007016) and matches the lower bound by Shi (quant-ph/0112086). The algorithm also solves the generalization of element distinctness in which we have to find k equal items among N items. For this problem, we get an O(N^{k/(k+1)}) query quantum algorithm.Comment: 33 pages, 1 figure, v9 typos with signs corrected on pages 11-1

A note on quantum black-box complexity of almost all Boolean functions

We show that, for almost all N-variable Boolean functions f, at least N/4-O(\sqrt{N} log N) queries are required to compute f in quantum black-box model with bounded error.Comment: 4 pages, LaTe

A nearly optimal discrete query quantum algorithm for evaluating NAND formulas

We present an O(\sqrt{N}) discrete query quantum algorithm for evaluating balanced binary NAND formulas and an O(N^{{1/2}+O(\frac{1}{\sqrt{\log N}})}) discrete query quantum algorithm for evaluating arbitrary binary NAND formulas.Comment: 21 pages, 2 figure