115,468 research outputs found
Finding Optimal Flows Efficiently
Among the models of quantum computation, the One-way Quantum Computer is one
of the most promising proposals of physical realization, and opens new
perspectives for parallelization by taking advantage of quantum entanglement.
Since a one-way quantum computation is based on quantum measurement, which is a
fundamentally nondeterministic evolution, a sufficient condition of global
determinism has been introduced as the existence of a causal flow in a graph
that underlies the computation. A O(n^3)-algorithm has been introduced for
finding such a causal flow when the numbers of output and input vertices in the
graph are equal, otherwise no polynomial time algorithm was known for deciding
whether a graph has a causal flow or not. Our main contribution is to introduce
a O(n^2)-algorithm for finding a causal flow, if any, whatever the numbers of
input and output vertices are. This answers the open question stated by Danos
and Kashefi and by de Beaudrap. Moreover, we prove that our algorithm produces
an optimal flow (flow of minimal depth.)
Whereas the existence of a causal flow is a sufficient condition for
determinism, it is not a necessary condition. A weaker version of the causal
flow, called gflow (generalized flow) has been introduced and has been proved
to be a necessary and sufficient condition for a family of deterministic
computations. Moreover the depth of the quantum computation is upper bounded by
the depth of the gflow. However, the existence of a polynomial time algorithm
that finds a gflow has been stated as an open question. In this paper we answer
this positively with a polynomial time algorithm that outputs an optimal gflow
of a given graph and thus finds an optimal correction strategy to the
nondeterministic evolution due to measurements.Comment: 10 pages, 3 figure
Streamlines for Motion Planning in Underwater Currents
Motion planning for underwater vehicles must consider the effect of ocean
currents. We present an efficient method to compute reachability and cost
between sample points in sampling-based motion planning that supports
long-range planning over hundreds of kilometres in complicated flows. The idea
is to search a reduced space of control inputs that consists of stream
functions whose level sets, or streamlines, optimally connect two given points.
Such stream functions are generated by superimposing a control input onto the
underlying current flow. A streamline represents the resulting path that a
vehicle would follow as it is carried along by the current given that control
input. We provide rigorous analysis that shows how our method avoids exhaustive
search of the control space, and demonstrate simulated examples in complicated
flows including a traversal along the east coast of Australia, using actual
current predictions, between Sydney and Brisbane.Comment: 7 pages, 4 figures, accepted to IEEE ICRA 2019. Copyright 2019 IEE
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
Physical portrayal of computational complexity
Computational complexity is examined using the principle of increasing
entropy. To consider computation as a physical process from an initial instance
to the final acceptance is motivated because many natural processes have been
recognized to complete in non-polynomial time (NP). The irreversible process
with three or more degrees of freedom is found intractable because, in terms of
physics, flows of energy are inseparable from their driving forces. In
computational terms, when solving problems in the class NP, decisions will
affect subsequently available sets of decisions. The state space of a
non-deterministic finite automaton is evolving due to the computation itself
hence it cannot be efficiently contracted using a deterministic finite
automaton that will arrive at a solution in super-polynomial time. The solution
of the NP problem itself is verifiable in polynomial time (P) because the
corresponding state is stationary. Likewise the class P set of states does not
depend on computational history hence it can be efficiently contracted to the
accepting state by a deterministic sequence of dissipative transformations.
Thus it is concluded that the class P set of states is inherently smaller than
the set of class NP. Since the computational time to contract a given set is
proportional to dissipation, the computational complexity class P is a subset
of NP.Comment: 16, pages, 7 figure
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