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 this http URL. (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.
In the usual query model (where one query gives one value of @math ), it is easy to see that @math queries are also necessary. Classical lower bounds have also been shown for more general models (e.g. @cite_36 ).
@cite_36
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Proving lower bounds for range queries has been an active topic of research since the late 70s, but so far nearly all results have been limited to the (rather restrictive) semigroup model. We consider one of the most basic range problem, orthogonal range counting in two dimensions, and show almost optimal bounds in the group model and the (holy grail) cell-probe model. Specifically, we show the following bounds, which were known in the semigroup model, but are major improvements in the more general models:* In the group and cell-probe models, a static data structure of size n lgO(1) n requires Omega(lg n lglg n) time per query. This is an exponential improvement over previous bounds, and matches known upper bounds.* In the group model, a dynamic data structure takes time Omega((lg n lglg n)2) per operation. This is close to the O(lg2 n) upper bound, where as the previous lower bound was Omega(lg n). Proving such (static and dynamic) bounds in the group model has been regarded as an important challenge at least since [Fredman, JACM 1982] and [Chazelle, FOCS 1986]. We describe new lower bounds for randomized communication complexity and query complexity which we call the partition bounds. They are expressed as the optimum value of linear programs. For communication complexity we show that the partition bound is stronger than both the rectangle corruption bound and the generalized discrepancy bounds. In the model of query complexity we show that the partition bound is stronger than the approximate polynomial degree and classical adversary bounds. We also exhibit an example where the partition bound is quadratically larger than polynomial degree and classical adversary bounds. We consider the problem of estimating the model count (number of solutions) of Boolean formulas, and present two techniques that compute estimates of these counts, as well as either lower or upper bounds with different trade-offs between efficiency, bound quality, and correctness guarantee. For lower bounds, we use a recent framework for probabilistic correctness guarantees, and exploit message passing techniques for marginal probability estimation, namely, variations of the Belief Propagation (BP) algorithm. Our results suggest that BP provides useful information even on structured, loopy formulas. For upper bounds, we perform multiple runs of the MiniSat SAT solver with a minor modification, and obtain statistical bounds on the model count based on the observation that the distribution of a certain quantity of interest is often very close to the normal distribution. Our experiments demonstrate that our model counters based on these two ideas, BPCount and MiniCount, can provide very good bounds in time significantly less than alternative approaches. Copyright Springer Science+Business Media, LLC 2011 To model modern large-scale datasets, we need efficient algorithms to infer a set of @math unknown model parameters from @math noisy measurements. What are fundamental limits on the accuracy of parameter inference, given finite signal-to-noise ratios, limited measurements, prior information, and computational tractability requirements? How can we combine prior information with measurements to achieve these limits? Classical statistics gives incisive answers to these questions as the measurement density @math . However, these classical results are not relevant to modern high-dimensional inference problems, which instead occur at finite @math . We formulate and analyze high-dimensional inference as a problem in the statistical physics of quenched disorder. Our analysis uncovers fundamental limits on the accuracy of inference in high dimensions, and reveals that widely cherished inference algorithms like maximum likelihood (ML) and maximum-a posteriori (MAP) inference cannot achieve these limits. We further find optimal, computationally tractable algorithms that can achieve these limits. Intriguingly, in high dimensions, these optimal algorithms become computationally simpler than MAP and ML, while still outperforming them. For example, such optimal algorithms can lead to as much as a 20 reduction in the amount of data to achieve the same performance relative to MAP. Moreover, our analysis reveals simple relations between optimal high dimensional inference and low dimensional scalar Bayesian inference, insights into the nature of generalization and predictive power in high dimensions, information theoretic limits on compressed sensing, phase transitions in quadratic inference, and connections to central mathematical objects in convex optimization theory and random matrix theory.