21,352 research outputs found
Many Worlds, the Cluster-state Quantum Computer, and the Problem of the Preferred Basis
I argue that the many worlds explanation of quantum computation is not
licensed by, and in fact is conceptually inferior to, the many worlds
interpretation of quantum mechanics from which it is derived. I argue that the
many worlds explanation of quantum computation is incompatible with the
recently developed cluster state model of quantum computation. Based on these
considerations I conclude that we should reject the many worlds explanation of
quantum computation.Comment: Added doi, acknowledgements, miscellaneous typo correction
Computer Architectures to Close the Loop in Real-time Optimization
© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
The quantum measurement problem and physical reality: a computation theoretic perspective
Is the universe computable? If yes, is it computationally a polynomial place?
In standard quantum mechanics, which permits infinite parallelism and the
infinitely precise specification of states, a negative answer to both questions
is not ruled out. On the other hand, empirical evidence suggests that
NP-complete problems are intractable in the physical world. Likewise,
computational problems known to be algorithmically uncomputable do not seem to
be computable by any physical means. We suggest that this close correspondence
between the efficiency and power of abstract algorithms on the one hand, and
physical computers on the other, finds a natural explanation if the universe is
assumed to be algorithmic; that is, that physical reality is the product of
discrete sub-physical information processing equivalent to the actions of a
probabilistic Turing machine. This assumption can be reconciled with the
observed exponentiality of quantum systems at microscopic scales, and the
consequent possibility of implementing Shor's quantum polynomial time algorithm
at that scale, provided the degree of superposition is intrinsically, finitely
upper-bounded. If this bound is associated with the quantum-classical divide
(the Heisenberg cut), a natural resolution to the quantum measurement problem
arises. From this viewpoint, macroscopic classicality is an evidence that the
universe is in BPP, and both questions raised above receive affirmative
answers. A recently proposed computational model of quantum measurement, which
relates the Heisenberg cut to the discreteness of Hilbert space, is briefly
discussed. A connection to quantum gravity is noted. Our results are compatible
with the philosophy that mathematical truths are independent of the laws of
physics.Comment: Talk presented at "Quantum Computing: Back Action 2006", IIT Kanpur,
India, March 200
Complexity, parallel computation and statistical physics
The intuition that a long history is required for the emergence of complexity
in natural systems is formalized using the notion of depth. The depth of a
system is defined in terms of the number of parallel computational steps needed
to simulate it. Depth provides an objective, irreducible measure of history
applicable to systems of the kind studied in statistical physics. It is argued
that physical complexity cannot occur in the absence of substantial depth and
that depth is a useful proxy for physical complexity. The ideas are illustrated
for a variety of systems in statistical physics.Comment: 21 pages, 7 figure
Multi-threading a state-of-the-art maximum clique algorithm
We present a threaded parallel adaptation of a state-of-the-art maximum clique
algorithm for dense, computationally challenging graphs. We show that near-linear speedups
are achievable in practice and that superlinear speedups are common. We include results for
several previously unsolved benchmark problems
OpenACC Based GPU Parallelization of Plane Sweep Algorithm for Geometric Intersection
Line segment intersection is one of the elementary operations in computational geometry. Complex problems in Geographic Information Systems (GIS) like finding map overlays or spatial joins using polygonal data require solving segment intersections. Plane sweep paradigm is used for finding geometric intersection in an efficient manner. However, it is difficult to parallelize due to its in-order processing of spatial events. We present a new fine-grained parallel algorithm for geometric intersection and its CPU and GPU implementation using OpenMP and OpenACC. To the best of our knowledge, this is the first work demonstrating an effective parallelization of plane sweep on GPUs.
We chose compiler directive based approach for implementation because of its simplicity to parallelize sequential code. Using Nvidia Tesla P100 GPU, our implementation achieves around 40X speedup for line segment intersection problem on 40K and 80K data sets compared to sequential CGAL library
The Quantum Frontier
The success of the abstract model of computation, in terms of bits, logical
operations, programming language constructs, and the like, makes it easy to
forget that computation is a physical process. Our cherished notions of
computation and information are grounded in classical mechanics, but the
physics underlying our world is quantum. In the early 80s researchers began to
ask how computation would change if we adopted a quantum mechanical, instead of
a classical mechanical, view of computation. Slowly, a new picture of
computation arose, one that gave rise to a variety of faster algorithms, novel
cryptographic mechanisms, and alternative methods of communication. Small
quantum information processing devices have been built, and efforts are
underway to build larger ones. Even apart from the existence of these devices,
the quantum view on information processing has provided significant insight
into the nature of computation and information, and a deeper understanding of
the physics of our universe and its connections with computation.
We start by describing aspects of quantum mechanics that are at the heart of
a quantum view of information processing. We give our own idiosyncratic view of
a number of these topics in the hopes of correcting common misconceptions and
highlighting aspects that are often overlooked. A number of the phenomena
described were initially viewed as oddities of quantum mechanics. It was
quantum information processing, first quantum cryptography and then, more
dramatically, quantum computing, that turned the tables and showed that these
oddities could be put to practical effect. It is these application we describe
next. We conclude with a section describing some of the many questions left for
future work, especially the mysteries surrounding where the power of quantum
information ultimately comes from.Comment: Invited book chapter for Computation for Humanity - Information
Technology to Advance Society to be published by CRC Press. Concepts
clarified and style made more uniform in version 2. Many thanks to the
referees for their suggestions for improvement
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