2,873 research outputs found
A lower bound on the quantum query complexity of read-once functions
We establish a lower bound of on the bounded-error
quantum query complexity of read-once Boolean functions, providing evidence for
the conjecture that is a lower bound for all Boolean
functions. Our technique extends a result of Ambainis, based on the idea that
successful computation of a function requires ``decoherence'' of initially
coherently superposed inputs in the query register, having different values of
the function. The number of queries is bounded by comparing the required total
amount of decoherence of a judiciously selected set of input-output pairs to an
upper bound on the amount achievable in a single query step. We use an
extension of this result to general weights on input pairs, and general
superpositions of inputs.Comment: 12 pages, LaTe
New Developments in Quantum Algorithms
In this survey, we describe two recent developments in quantum algorithms.
The first new development is a quantum algorithm for evaluating a Boolean
formula consisting of AND and OR gates of size N in time O(\sqrt{N}). This
provides quantum speedups for any problem that can be expressed via Boolean
formulas. This result can be also extended to span problems, a generalization
of Boolean formulas. This provides an optimal quantum algorithm for any Boolean
function in the black-box query model.
The second new development is a quantum algorithm for solving systems of
linear equations. In contrast with traditional algorithms that run in time
O(N^{2.37...}) where N is the size of the system, the quantum algorithm runs in
time O(\log^c N). It outputs a quantum state describing the solution of the
system.Comment: 11 pages, 1 figure, to appear as an invited survey talk at MFCS'201
Why Philosophers Should Care About Computational Complexity
One might think that, once we know something is computable, how efficiently
it can be computed is a practical question with little further philosophical
importance. In this essay, I offer a detailed case that one would be wrong. In
particular, I argue that computational complexity theory---the field that
studies the resources (such as time, space, and randomness) needed to solve
computational problems---leads to new perspectives on the nature of
mathematical knowledge, the strong AI debate, computationalism, the problem of
logical omniscience, Hume's problem of induction, Goodman's grue riddle, the
foundations of quantum mechanics, economic rationality, closed timelike curves,
and several other topics of philosophical interest. I end by discussing aspects
of complexity theory itself that could benefit from philosophical analysis.Comment: 58 pages, to appear in "Computability: G\"odel, Turing, Church, and
beyond," MIT Press, 2012. Some minor clarifications and corrections; new
references adde
Highly parallel sparse Cholesky factorization
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms
A Study of Optimal 4-bit Reversible Toffoli Circuits and Their Synthesis
Optimal synthesis of reversible functions is a non-trivial problem. One of
the major limiting factors in computing such circuits is the sheer number of
reversible functions. Even restricting synthesis to 4-bit reversible functions
results in a huge search space (16! {\approx} 2^{44} functions). The output of
such a search alone, counting only the space required to list Toffoli gates for
every function, would require over 100 terabytes of storage. In this paper, we
present two algorithms: one, that synthesizes an optimal circuit for any 4-bit
reversible specification, and another that synthesizes all optimal
implementations. We employ several techniques to make the problem tractable. We
report results from several experiments, including synthesis of all optimal
4-bit permutations, synthesis of random 4-bit permutations, optimal synthesis
of all 4-bit linear reversible circuits, synthesis of existing benchmark
functions; we compose a list of the hardest permutations to synthesize, and
show distribution of optimal circuits. We further illustrate that our proposed
approach may be extended to accommodate physical constraints via reporting
LNN-optimal reversible circuits. Our results have important implications in the
design and optimization of reversible and quantum circuits, testing circuit
synthesis heuristics, and performing experiments in the area of quantum
information processing.Comment: arXiv admin note: substantial text overlap with arXiv:1003.191
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