14,257 research outputs found
Bounds on the entanglability of thermal states in liquid-state nuclear magnetic resonance
The role of mixed state entanglement in liquid-state nuclear magnetic
resonance (NMR) quantum computation is not yet well-understood. In particular,
despite the success of quantum information processing with NMR, recent work has
shown that quantum states used in most of those experiments were not entangled.
This is because these states, derived by unitary transforms from the thermal
equilibrium state, were too close to the maximally mixed state. We are thus
motivated to determine whether a given NMR state is entanglable - that is, does
there exist a unitary transform that entangles the state? The boundary between
entanglable and nonentanglable thermal states is a function of the spin system
size and its temperature . We provide new bounds on the location of this
boundary using analytical and numerical methods; our tightest bound scales as
, giving a lower bound requiring at least proton
spins to realize an entanglable thermal state at typical laboratory NMR
magnetic fields. These bounds are tighter than known bounds on the
entanglability of effective pure states.Comment: REVTeX4, 15 pages, 4 figures (one large figure: 414 K
An implicit algorithm for validated enclosures of the solutions to variational equations for ODEs
We propose a new algorithm for computing validated bounds for the solutions
to the first order variational equations associated to ODEs. These validated
solutions are the kernel of numerics computer-assisted proofs in dynamical
systems literature. The method uses a high-order Taylor method as a predictor
step and an implicit method based on the Hermite-Obreshkov interpolation as a
corrector step. The proposed algorithm is an improvement of the -Lohner
algorithm proposed by Zgliczy\'nski and it provides sharper bounds.
As an application of the algorithm, we give a computer-assisted proof of the
existence of an attractor set in the R\"ossler system, and we show that the
attractor contains an invariant and uniformly hyperbolic subset on which the
dynamics is chaotic, that is, conjugated to subshift of finite type with
positive topological entropy.Comment: 33 pages, 11 figure
Bounding Run-Times of Local Adiabatic Algorithms
A common trick for designing faster quantum adiabatic algorithms is to apply
the adiabaticity condition locally at every instant. However it is often
difficult to determine the instantaneous gap between the lowest two
eigenvalues, which is an essential ingredient in the adiabaticity condition. In
this paper we present a simple linear algebraic technique for obtaining a lower
bound on the instantaneous gap even in such a situation. As an illustration, we
investigate the adiabatic unordered search of van Dam et al. (How powerful is
adiabatic quantum computation? Proc. IEEE FOCS, pp. 279-287, 2001) and Roland
and Cerf (Physical Review A 65, 042308, 2002) when the non-zero entries of the
diagonal final Hamiltonian are perturbed by a polynomial (in , where
is the length of the unordered list) amount. We use our technique to derive
a bound on the running time of a local adiabatic schedule in terms of the
minimum gap between the lowest two eigenvalues.Comment: 11 page
Optimization of Information Rate Upper and Lower Bounds for Channels with Memory
We consider the problem of minimizing upper bounds and maximizing lower
bounds on information rates of stationary and ergodic discrete-time channels
with memory. The channels we consider can have a finite number of states, such
as partial response channels, or they can have an infinite state-space, such as
time-varying fading channels. We optimize recently-proposed information rate
bounds for such channels, which make use of auxiliary finite-state machine
channels (FSMCs). Our main contribution in this paper is to provide iterative
expectation-maximization (EM) type algorithms to optimize the parameters of the
auxiliary FSMC to tighten these bounds. We provide an explicit, iterative
algorithm that improves the upper bound at each iteration. We also provide an
effective method for iteratively optimizing the lower bound. To demonstrate the
effectiveness of our algorithms, we provide several examples of partial
response and fading channels, where the proposed optimization techniques
significantly tighten the initial upper and lower bounds. Finally, we compare
our results with an improved variation of the \emph{simplex} local optimization
algorithm, called \emph{Soblex}. This comparison shows that our proposed
algorithms are superior to the Soblex method, both in terms of robustness in
finding the tightest bounds and in computational efficiency. Interestingly,
from a channel coding/decoding perspective, optimizing the lower bound is
related to increasing the achievable mismatched information rate, i.e., the
information rate of a communication system where the decoder at the receiver is
matched to the auxiliary channel, and not to the original channel.Comment: Submitted to IEEE Transactions on Information Theory, November 24,
200
Isomorph-Free Branch and Bound Search for Finite State Controllers
The recent proliferation of smart-phones and other wearable devices has lead
to a surge of new mobile applications. Partially observable Markov decision
processes provide a natural framework to design applications that
continuously make decisions based on noisy sensor measurements. However,
given the limited battery life, there is a need to minimize the amount of
online computation. This can be achieved by compiling a policy into a
finite state controller since there is no need for belief monitoring or
online search. In this paper, we propose a new branch and bound technique
to search for a good controller. In contrast to many existing algorithms
for controllers, our search technique is not subject to local optima. We
also show how to reduce the amount of search by avoiding the enumeration of
isomorphic controllers and by taking advantage of suitable upper and lower
bounds. The approach is demonstrated on several benchmark problems as well
as a smart-phone application to assist persons with Alzheimer's to wayfind
On Characterizing the Data Movement Complexity of Computational DAGs for Parallel Execution
Technology trends are making the cost of data movement increasingly dominant,
both in terms of energy and time, over the cost of performing arithmetic
operations in computer systems. The fundamental ratio of aggregate data
movement bandwidth to the total computational power (also referred to the
machine balance parameter) in parallel computer systems is decreasing. It is
there- fore of considerable importance to characterize the inherent data
movement requirements of parallel algorithms, so that the minimal architectural
balance parameters required to support it on future systems can be well
understood. In this paper, we develop an extension of the well-known red-blue
pebble game to develop lower bounds on the data movement complexity for the
parallel execution of computational directed acyclic graphs (CDAGs) on parallel
systems. We model multi-node multi-core parallel systems, with the total
physical memory distributed across the nodes (that are connected through some
interconnection network) and in a multi-level shared cache hierarchy for
processors within a node. We also develop new techniques for lower bound
characterization of non-homogeneous CDAGs. We demonstrate the use of the
methodology by analyzing the CDAGs of several numerical algorithms, to develop
lower bounds on data movement for their parallel execution
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