98,227 research outputs found
Discriminating quantum states: the multiple Chernoff distance
We consider the problem of testing multiple quantum hypotheses
, where an arbitrary prior
distribution is given and each of the hypotheses is copies of a quantum
state. It is known that the average error probability decays
exponentially to zero, that is, . However, this error
exponent is generally unknown, except for the case that .
In this paper, we solve the long-standing open problem of identifying the
above error exponent, by proving Nussbaum and Szko\l a's conjecture that
. The right-hand side of this equality is
called the multiple quantum Chernoff distance, and
has been previously
identified as the optimal error exponent for testing two hypotheses,
versus .
The main ingredient of our proof is a new upper bound for the average error
probability, for testing an ensemble of finite-dimensional, but otherwise
general, quantum states. This upper bound, up to a states-dependent factor,
matches the multiple-state generalization of Nussbaum and Szko\l a's lower
bound. Specialized to the case , we give an alternative proof to the
achievability of the binary-hypothesis Chernoff distance, which was originally
proved by Audenaert et al.Comment: v2: minor change
Many Hard Examples in Exact Phase Transitions with Application to Generating Hard Satisfiable Instances
This paper first analyzes the resolution complexity of two random CSP models
(i.e. Model RB/RD) for which we can establish the existence of phase
transitions and identify the threshold points exactly. By encoding CSPs into
CNF formulas, it is proved that almost all instances of Model RB/RD have no
tree-like resolution proofs of less than exponential size. Thus, we not only
introduce new families of CNF formulas hard for resolution, which is a central
task of Proof-Complexity theory, but also propose models with both many hard
instances and exact phase transitions. Then, the implications of such models
are addressed. It is shown both theoretically and experimentally that an
application of Model RB/RD might be in the generation of hard satisfiable
instances, which is not only of practical importance but also related to some
open problems in cryptography such as generating one-way functions.
Subsequently, a further theoretical support for the generation method is shown
by establishing exponential lower bounds on the complexity of solving random
satisfiable and forced satisfiable instances of RB/RD near the threshold.
Finally, conclusions are presented, as well as a detailed comparison of Model
RB/RD with the Hamiltonian cycle problem and random 3-SAT, which, respectively,
exhibit three different kinds of phase transition behavior in NP-complete
problems.Comment: 19 pages, corrected mistakes in Theorems 5 and
Estimating decay rate of while assuming them to be molecular states
Discovery of brings up a tremendous interest because it is very
special, i.e. made of four different flavors. The D0 collaboration claimed that
they observed this resonance through portal , but
unfortunately, later the LHCb, CMS, CDF and ATLAS collaborations' reports
indicate that no such state was found. Almost on the Eve of 2017, the D0
collaboration reconfirmed existence of via the semileptonic decay of
. To further reveal the discrepancy, supposing as a molecular
state, we calculate the decay rate of in an
extended light front model. Numerically, the theoretically predicted decay
width of is MeV which is
consistent with the result of the D0 collaboration
( MeV). Since the
resonance is narrow, signals might be drowned in a messy background. In analog,
two open-charm molecular states and named as and , could
be in the same situation. The rates of and
are estimated as about 30 MeV and 20 MeV respectively. We suggest the
experimental collaborations round the world to search for these two modes and
accurate measurements may provide us with valuable information.Comment: 13 pages and 4 figures, accepted by EPJ
Iterative Instance Segmentation
Existing methods for pixel-wise labelling tasks generally disregard the
underlying structure of labellings, often leading to predictions that are
visually implausible. While incorporating structure into the model should
improve prediction quality, doing so is challenging - manually specifying the
form of structural constraints may be impractical and inference often becomes
intractable even if structural constraints are given. We sidestep this problem
by reducing structured prediction to a sequence of unconstrained prediction
problems and demonstrate that this approach is capable of automatically
discovering priors on shape, contiguity of region predictions and smoothness of
region contours from data without any a priori specification. On the instance
segmentation task, this method outperforms the state-of-the-art, achieving a
mean of 63.6% at 50% overlap and 43.3% at 70% overlap.Comment: 13 pages, 10 figures; IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
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