14,257 research outputs found

    Bounds on the entanglability of thermal states in liquid-state nuclear magnetic resonance

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    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 NN and its temperature TT. We provide new bounds on the location of this boundary using analytical and numerical methods; our tightest bound scales as NTN \sim T, giving a lower bound requiring at least N22,000N \sim 22,000 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

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    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 C1C^1-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

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    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 logN\log N, where NN 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

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    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

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    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

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    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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