7,566 research outputs found
Simulating quantum field theory with a quantum computer
Forthcoming exascale digital computers will further advance our knowledge of
quantum chromodynamics, but formidable challenges will remain. In particular,
Euclidean Monte Carlo methods are not well suited for studying real-time
evolution in hadronic collisions, or the properties of hadronic matter at
nonzero temperature and chemical potential. Digital computers may never be able
to achieve accurate simulations of such phenomena in QCD and other
strongly-coupled field theories; quantum computers will do so eventually,
though I'm not sure when. Progress toward quantum simulation of quantum field
theory will require the collaborative efforts of quantumists and field
theorists, and though the physics payoff may still be far away, it's worthwhile
to get started now. Today's research can hasten the arrival of a new era in
which quantum simulation fuels rapid progress in fundamental physics.Comment: 22 pages, The 36th Annual International Symposium on Lattice Field
Theory - LATTICE201
Synthesis and Optimization of Reversible Circuits - A Survey
Reversible logic circuits have been historically motivated by theoretical
research in low-power electronics as well as practical improvement of
bit-manipulation transforms in cryptography and computer graphics. Recently,
reversible circuits have attracted interest as components of quantum
algorithms, as well as in photonic and nano-computing technologies where some
switching devices offer no signal gain. Research in generating reversible logic
distinguishes between circuit synthesis, post-synthesis optimization, and
technology mapping. In this survey, we review algorithmic paradigms ---
search-based, cycle-based, transformation-based, and BDD-based --- as well as
specific algorithms for reversible synthesis, both exact and heuristic. We
conclude the survey by outlining key open challenges in synthesis of reversible
and quantum logic, as well as most common misconceptions.Comment: 34 pages, 15 figures, 2 table
A general guide to applying machine learning to computer architecture
The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results.
The purpose of this paper is to serve as a foundational base and guide to future computer
architecture research seeking to make use of machine learning models for improving system efficiency.
We describe a method that highlights when, why, and how to utilize machine learning
models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data
generation every execution quantum and parameter engineering. This is followed by a survey of a
set of popular machine learning models. We discuss their strengths and weaknesses and provide
an evaluation of implementations for the purpose of creating a workload performance predictor
for different core types in an x86 processor. The predictions can then be exploited by a scheduler
for heterogeneous processors to improve the system throughput. The algorithms of focus are
stochastic gradient descent based linear regression, decision trees, random forests, artificial neural
networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version
Quantum Computing
Quantum mechanics---the theory describing the fundamental workings of
nature---is famously counterintuitive: it predicts that a particle can be in
two places at the same time, and that two remote particles can be inextricably
and instantaneously linked. These predictions have been the topic of intense
metaphysical debate ever since the theory's inception early last century.
However, supreme predictive power combined with direct experimental observation
of some of these unusual phenomena leave little doubt as to its fundamental
correctness. In fact, without quantum mechanics we could not explain the
workings of a laser, nor indeed how a fridge magnet operates. Over the last
several decades quantum information science has emerged to seek answers to the
question: can we gain some advantage by storing, transmitting and processing
information encoded in systems that exhibit these unique quantum properties?
Today it is understood that the answer is yes. Many research groups around the
world are working towards one of the most ambitious goals humankind has ever
embarked upon: a quantum computer that promises to exponentially improve
computational power for particular tasks. A number of physical systems,
spanning much of modern physics, are being developed for this task---ranging
from single particles of light to superconducting circuits---and it is not yet
clear which, if any, will ultimately prove successful. Here we describe the
latest developments for each of the leading approaches and explain what the
major challenges are for the future.Comment: 26 pages, 7 figures, 291 references. Early draft of Nature 464, 45-53
(4 March 2010). Published version is more up-to-date and has several
corrections, but is half the length with far fewer reference
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