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The design of new quantum algorithms has proven to be an extremely difficult task. This paper considers a different approach to this task by studying the problem of quantum state generation. We motivate this problem by showing that the entire class of statistical zero knowledge, which contains natural candidates for efficient quantum algorithms such as graph isomorphism and lattice problems, can be reduced to the problem of quantum state generation. To study quantum state generation, we define a paradigm which we call adiabatic state generation (ASG) and which is based on adiabatic quantum computation. The ASG paradigm is not meant to replace the standard quantum circuit model or to improve on it in terms of computational complexity. Rather, our goal is to provide a natural theoretical framework, in which quantum state generation algorithms could be designed. The new paradigm seems interesting due to its intriguing links to a variety of different areas: the analysis of spectral gaps and ground-states of Hamiltonians in physics, rapidly mixing Markov chains, adiabatic computation, and approximate counting. To initiate the study of ASG, we prove several general lemmas that can serve as tools when using this paradigm. We demonstrate the application of the paradigm by using it to turn a variety of (classical) approximate counting algorithms into efficient quantum state generators of nontrivial quantum states, including, for example, the uniform superposition over all perfect matchings in a bipartite graph. [ABSTRACT FROM AUTHOR]

Let S be a set of n points in ℝ². Given an integer 1 ≤ k ≤ n, we wish to find a maximally separated subset I ⊆ S of size k; this is a subset for which the minimum among the (2k) pairwise distances between its points is as large as possible. The decision problem associated with this problem is to determine whether there exists I ⊆ S, ∣I∣ = k, so that all (2k) pairwise distances in I are at least 2. This problem can also be formulated in terms of disk-intersection graphs: Let D be the set of unit disks centered at the points of S. The disk-intersection graph G of D has as edges all pairs of disks with nonempty intersection. Any set I with the above properties is then the set of centers of disks that form an independent set in the graph G. This problem is known to be NP-complete if k is part of the input. In this paper we first present a linear-time e-approximation algorithm for any constant k. Next we give exact algorithms for the cases k = 3 and k = 4 that run in time O(n4/3polylog(n)). We also present a simpler nO(√k)-time exact algorithm (as compared with the recent algorithm in [J. Alber and J. Fiala, J. Algorithms, 52 (2004), pp. 134-151]) for arbitrary values of k. [ABSTRACT FROM AUTHOR]

Almost all of the most successful quantum algorithms discovered to date exploit the ability of the Fourier transform to recover subgroup structures of functions, especially periodicity. The fact that Fourier transforms can also be used to capture shift structure has received far less attention in the context of quantum computation. In this paper, we present three examples of ‘unknown shift’ problems that can be solved efficiently on a quantum computer using the quantum Fourier transform. For one of these problems, the shifted Legendre symbol problem, we give evidence that the problem is hard to solve classically, by showing a reduction from breaking algebraically homomorphic cryptosystems. We also define the hidden coset problem, which generalizes the hidden shift problem and the hidden subgroup problem. This framework provides a unified way of viewing the ability of the Fourier transform to capture subgroup and shift structure. [ABSTRACT FROM AUTHOR]

We present an algorithm to enumerate the pointed pseudotriangulations of a given point set, based on the greedy flip algorithm of Pocchiola and Vegter [Discrete Comput. Geom. 16 (1996), pp. 419-453]. Our two independent implementations agree and allow us to experimentally verify or disprove conjectures on the numbers of pointed pseudotriangulations and triangulations of a given point set. (For example, we establish that the number of triangulations is bounded by the number of pointed pseudotriangulations for all sets of up to 10 points.) [ABSTRACT FROM AUTHOR]

In many optimization problems, one seeks to allocate a limited set of resources to a set of individuals with demands. Thus, such allocations can naturally be viewed as vectors, with one coordinate representing each individual. Motivated by work in network routing and bandwidth assignment, we consider the problem of producing solutions that simultaneously approximate all feasible allocations in a coordinate-wise sense. This is a very strong type of ‘global’ approximation guarantee, and we explore its consequences in a wide range of discrete optimization problems, including facility location, scheduling, and bandwidth assignment in networks. A fundamental issue—one not encountered in the traditional design of approximation algorithms—is that good approximations in this global sense need not exist for every problem instance; there is no a priori reason why there should be an allocation that simultaneously approximates all others. As a result, the existential questions concerning such good allocations lead to a new perspective on a number of fundamental problems in resource allocation, and on the structure of their feasible solutions. [ABSTRACT FROM AUTHOR]

Jackson and Wormald conjecture that if G is a 3-connected n-vertex graph with maximum degree d ≥ 4, then G has a cycle of length Ω(nlogd-1 ²). We show that this conjecture holds when d - 1 is replaced by max{64, 4d + 1}. Our proof implies a cubic algorithm for finding such a cycle. [ABSTRACT FROM AUTHOR]