24,044 research outputs found
Arithmetic on a Distributed-Memory Quantum Multicomputer
We evaluate the performance of quantum arithmetic algorithms run on a
distributed quantum computer (a quantum multicomputer). We vary the node
capacity and I/O capabilities, and the network topology. The tradeoff of
choosing between gates executed remotely, through ``teleported gates'' on
entangled pairs of qubits (telegate), versus exchanging the relevant qubits via
quantum teleportation, then executing the algorithm using local gates
(teledata), is examined. We show that the teledata approach performs better,
and that carry-ripple adders perform well when the teleportation block is
decomposed so that the key quantum operations can be parallelized. A node size
of only a few logical qubits performs adequately provided that the nodes have
two transceiver qubits. A linear network topology performs acceptably for a
broad range of system sizes and performance parameters. We therefore recommend
pursuing small, high-I/O bandwidth nodes and a simple network. Such a machine
will run Shor's algorithm for factoring large numbers efficiently.Comment: 24 pages, 10 figures, ACM transactions format. Extended version of
Int. Symp. on Comp. Architecture (ISCA) paper; v2, correct one circuit error,
numerous small changes for clarity, add reference
Quantum Computing: Pro and Con
I assess the potential of quantum computation. Broad and important
applications must be found to justify construction of a quantum computer; I
review some of the known quantum algorithms and consider the prospects for
finding new ones. Quantum computers are notoriously susceptible to making
errors; I discuss recently developed fault-tolerant procedures that enable a
quantum computer with noisy gates to perform reliably. Quantum computing
hardware is still in its infancy; I comment on the specifications that should
be met by future hardware. Over the past few years, work on quantum computation
has erected a new classification of computational complexity, has generated
profound insights into the nature of decoherence, and has stimulated the
formulation of new techniques in high-precision experimental physics. A broad
interdisciplinary effort will be needed if quantum computers are to fulfill
their destiny as the world's fastest computing devices. (This paper is an
expanded version of remarks that were prepared for a panel discussion at the
ITP Conference on Quantum Coherence and Decoherence, 17 December 1996.)Comment: 17 pages, LaTeX, submitted to Proc. Roy. Soc. Lond. A, minor
correction
Neural Networks for Predicting Algorithm Runtime Distributions
Many state-of-the-art algorithms for solving hard combinatorial problems in
artificial intelligence (AI) include elements of stochasticity that lead to
high variations in runtime, even for a fixed problem instance. Knowledge about
the resulting runtime distributions (RTDs) of algorithms on given problem
instances can be exploited in various meta-algorithmic procedures, such as
algorithm selection, portfolios, and randomized restarts. Previous work has
shown that machine learning can be used to individually predict mean, median
and variance of RTDs. To establish a new state-of-the-art in predicting RTDs,
we demonstrate that the parameters of an RTD should be learned jointly and that
neural networks can do this well by directly optimizing the likelihood of an
RTD given runtime observations. In an empirical study involving five algorithms
for SAT solving and AI planning, we show that neural networks predict the true
RTDs of unseen instances better than previous methods, and can even do so when
only few runtime observations are available per training instance
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