6,144 research outputs found
Occam's Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel
A stochastic process's statistical complexity stands out as a fundamental
property: the minimum information required to synchronize one process generator
to another. How much information is required, though, when synchronizing over a
quantum channel? Recent work demonstrated that representing causal similarity
as quantum state-indistinguishability provides a quantum advantage. We
generalize this to synchronization and offer a sequence of constructions that
exploit extended causal structures, finding substantial increase of the quantum
advantage. We demonstrate that maximum compression is determined by the
process's cryptic order---a classical, topological property closely allied to
Markov order, itself a measure of historical dependence. We introduce an
efficient algorithm that computes the quantum advantage and close noting that
the advantage comes at a cost---one trades off prediction for generation
complexity.Comment: 10 pages, 6 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/oqs.ht
Extreme Quantum Advantage for Rare-Event Sampling
We introduce a quantum algorithm for efficient biased sampling of the rare
events generated by classical memoryful stochastic processes. We show that this
quantum algorithm gives an extreme advantage over known classical biased
sampling algorithms in terms of the memory resources required. The quantum
memory advantage ranges from polynomial to exponential and when sampling the
rare equilibrium configurations of spin systems the quantum advantage diverges.Comment: 11 pages, 9 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/eqafbs.ht
Optimizing Quantum Models of Classical Channels: The reverse Holevo problem
Given a classical channel---a stochastic map from inputs to outputs---the
input can often be transformed to an intermediate variable that is
informationally smaller than the input. The new channel accurately simulates
the original but at a smaller transmission rate. Here, we examine this
procedure when the intermediate variable is a quantum state. We determine when
and how well quantum simulations of classical channels may improve upon the
minimal rates of classical simulation. This inverts Holevo's original question
of quantifying the capacity of quantum channels with classical resources. We
also show that this problem is equivalent to another, involving the local
generation of a distribution from common entanglement.Comment: 13 pages, 6 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/qfact.htm; substantially updated
from v
Prediction, Retrodiction, and The Amount of Information Stored in the Present
We introduce an ambidextrous view of stochastic dynamical systems, comparing
their forward-time and reverse-time representations and then integrating them
into a single time-symmetric representation. The perspective is useful
theoretically, computationally, and conceptually. Mathematically, we prove that
the excess entropy--a familiar measure of organization in complex systems--is
the mutual information not only between the past and future, but also between
the predictive and retrodictive causal states. Practically, we exploit the
connection between prediction and retrodiction to directly calculate the excess
entropy. Conceptually, these lead one to discover new system invariants for
stochastic dynamical systems: crypticity (information accessibility) and causal
irreversibility. Ultimately, we introduce a time-symmetric representation that
unifies all these quantities, compressing the two directional representations
into one. The resulting compression offers a new conception of the amount of
information stored in the present.Comment: 17 pages, 7 figures, 1 table;
http://users.cse.ucdavis.edu/~cmg/compmech/pubs/pratisp.ht
Information Accessibility and Cryptic Processes: Linear Combinations of Causal States
We show in detail how to determine the time-reversed representation of a
stationary hidden stochastic process from linear combinations of its
forward-time -machine causal states. This also gives a check for the
-cryptic expansion recently introduced to explore the temporal range over
which internal state information is spread.Comment: 6 pages, 9 figures, 2 tables;
http://users.cse.ucdavis.edu/~cmg/compmech/pubs/iacplcocs.ht
Inversion of stellar statistics equation for the Galactic Bulge
A method based on Lucy (1974, AJ 79, 745) iterative algorithm is developed to
invert the equation of stellar statistics for the Galactic bulge and is then
applied to the K-band star counts from the Two-Micron Galactic Survey in a
number of off-plane regions (10 deg.>|b|>2 deg., |l|<15 deg.).
The top end of the K-band luminosity function is derived and the morphology
of the stellar density function is fitted to triaxial ellipsoids, assuming a
non-variable luminosity function within the bulge. The results, which have
already been outlined by Lopez-Corredoira et al.(1997, MNRAS 292, L15), are
shown in this paper with a full explanation of the steps of the inversion: the
luminosity function shows a sharp decrease brighter than M_K=-8.0 mag when
compared with the disc population; the bulge fits triaxial ellipsoids with the
major axis in the Galactic plane at an angle with the line of sight to the
Galactic centre of 12 deg. in the first quadrant; the axial ratios are
1:0.54:0.33, and the distance of the Sun from the centre of the triaxial
ellipsoid is 7860 pc. The major-minor axial ratio of the ellipsoids is found
not to be constant. However, the interpretation of this is controversial. An
eccentricity of the true density-ellipsoid gradient and a population gradient
are two possible explanations.
The best fit for the stellar density, for 1300 pc<t<3000 pc, are calculated
for both cases, assuming an ellipsoidal distribution with constant axial
ratios, and when K_z is allowed to vary. From these, the total number of bulge
stars is ~ 3 10^{10} or ~ 4 10^{10}, respectively.Comment: 19 pages, 23 figures, accepted in MNRA
Block CUR: Decomposing Matrices using Groups of Columns
A common problem in large-scale data analysis is to approximate a matrix
using a combination of specifically sampled rows and columns, known as CUR
decomposition. Unfortunately, in many real-world environments, the ability to
sample specific individual rows or columns of the matrix is limited by either
system constraints or cost. In this paper, we consider matrix approximation by
sampling predefined \emph{blocks} of columns (or rows) from the matrix. We
present an algorithm for sampling useful column blocks and provide novel
guarantees for the quality of the approximation. This algorithm has application
in problems as diverse as biometric data analysis to distributed computing. We
demonstrate the effectiveness of the proposed algorithms for computing the
Block CUR decomposition of large matrices in a distributed setting with
multiple nodes in a compute cluster, where such blocks correspond to columns
(or rows) of the matrix stored on the same node, which can be retrieved with
much less overhead than retrieving individual columns stored across different
nodes. In the biometric setting, the rows correspond to different users and
columns correspond to users' biometric reaction to external stimuli, {\em
e.g.,}~watching video content, at a particular time instant. There is
significant cost in acquiring each user's reaction to lengthy content so we
sample a few important scenes to approximate the biometric response. An
individual time sample in this use case cannot be queried in isolation due to
the lack of context that caused that biometric reaction. Instead, collections
of time segments ({\em i.e.,} blocks) must be presented to the user. The
practical application of these algorithms is shown via experimental results
using real-world user biometric data from a content testing environment.Comment: shorter version to appear in ECML-PKDD 201
Surveying structural complexity in quantum many-body systems
Quantum many-body systems exhibit a rich and diverse range of exotic
behaviours, owing to their underlying non-classical structure. These systems
present a deep structure beyond those that can be captured by measures of
correlation and entanglement alone. Using tools from complexity science, we
characterise such structure. We investigate the structural complexities that
can be found within the patterns that manifest from the observational data of
these systems. In particular, using two prototypical quantum many-body systems
as test cases - the one-dimensional quantum Ising and Bose-Hubbard models - we
explore how different information-theoretic measures of complexity are able to
identify different features of such patterns. This work furthers the
understanding of fully-quantum notions of structure and complexity in quantum
systems and dynamics.Comment: 9 pages, 5 figure
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