How Much of One-Way Computation Is Just Thermodynamics?

In this paper we argue that one-way quantum computation can be seen as a form of phase transition with the available information about the solution of the computation being the order parameter. We draw a number of striking analogies between standard thermodynamical quantities such as energy, temperature, work, and corresponding computational quantities such as the amount of entanglement, time, potential capacity for computation, respectively. Aside from being intuitively pleasing, this picture allows us to make novel conjectures, such as an estimate of the necessary critical time to finish a computation and a proposal of suitable architectures for universal one-way computation in 1D.     

Accelerated steered response power method for sound source localization using orthogonal linear array

In microphone arrays application, it is a difficult task to accurately and fast localize sound source in a noisy, reverberant environment. In order to solve this problem, many approaches have been presented. Among them, the steered response power-phase transform weighted (SRP–PHAT) source localization algorithm has been proved robust. However, SRP–PHAT requires high computation cost for searching a large location space. To overcome this shortcoming, an improved SRP–PHAT will be presented that reduces a two-dimension searching space into a couple of one-dimension ones by using an orthogonal linear array. In this method, the parameters of direction of arrival (DOA) are separated. The main computation can be carried out independently in two one-dimension spaces, thus the computational load will be greatly cut down. Simulations show that there is no loss in accuracy in the proposed method.     

Computers with Closed Timelike Curves Can Solve Hard Problems Efficiently

A computer which has access to a closed timelike curve, and can thereby send the results of calculations into its own past, can exploit this to solve difficult computational problems efficiently. I give a specific demonstration of this for the problem of factoring large numbers and argue that a similar approach can solve NP-complete and PSPACE-complete problems. I discuss the potential impact of quantum effects on this result.     

Analog computation through high-dimensional physical chaotic neuro-dynamics

Conventional von Neumann computers have difficulty in solving complex and ill-posed real-world problems. However, living organisms often face such problems in real life, and must quickly obtain suitable solutions through physical, dynamical, and collective computations involving vast assemblies of neurons. These highly parallel computations through high-dimensional dynamics (computation through dynamics) are completely different from the numerical computations on von Neumann computers (computation through algorithms). In this paper, we explore a novel computational mechanism with high-dimensional physical chaotic neuro-dynamics. We physically constructed two hardware prototypes using analog chaotic-neuron integrated circuits. These systems combine analog computations with chaotic neuro-dynamics and digital computation through algorithms. We used quadratic assignment problems (QAPs) as benchmarks. The first prototype utilizes an analog chaotic neural network with 800-dimensional dynamics. An external algorithm constructs a solution for a QAP using the internal dynamics of the network. In the second system, 300-dimensional analog chaotic neuro-dynamics drive a tabu-search algorithm. We demonstrate experimentally that both systems efficiently solve QAPs through physical chaotic dynamics. We also qualitatively analyze the underlying mechanism of the highly parallel and collective analog computations by observing global and local dynamics. Furthermore, we introduce spatial and temporal mutual information to quantitatively evaluate the system dynamics. The experimental results confirm the validity and efficiency of the proposed computational paradigm with the physical analog chaotic neuro-dynamics.     

Quality of Service based resource allocation in D2D enabled 5G-CNs with network slicing

With the rapid new advancements in technology, there is an enormous increase in devices and their versatile need for services. Fifth-generation (5G) cellular networks (5G-CNs) with network slicing (NS) have emerged as a necessity for future mobile communication. The available network is partitioned logically into multiple virtual networks to provide an enormous range of users’ specific services. Efficient resource allocation methods are critical to delivering the customers with their required Quality of Service (QoS) priorities. In this work, we have investigated a QoS based resource allocation (RA) scheme considering two types of 5G slices with different service requirements; (1) enhanced Mobile Broadband (eMBB) slice that requires a very high data rate and (2) massive Machine Type Communication (mMTC) slice that requires extremely low latency. We investigated the device-to-device (D2D) enabled 5G-CN model with NS to assign resources to users based on their QoS needs while considering the cellular and D2D user’s data rate requirements. We have proposed a Distributed Algorithm (DA) with edge computation to solve the optimization problem, which is novel as edge routers will solve the problem locally using the augmented Lagrange method. They then send this information to the central server to find the global optimum solution utilizing a consensus algorithm. Simulation analysis proves that this scheme is efficient as it assigns resources based on their QoS requirements. This scheme is excellent in reducing the central load and computational time.     

Can Everett be Interpreted Without Extravaganza?

Everett’s relative states interpretation of quantum mechanics has met with problems related to probability, the preferred basis, and multiplicity. The third theme, I argue, is the most important one. It has led to developments of the original approach into many-worlds, many-minds, and decoherence-based approaches. The latter especially have been advocated in recent years, in an effort to understand multiplicity without resorting to what is often perceived as extravagant constructions. Drawing from and adding to arguments of others, I show that proponents of decoherence-based approaches have not yet succeeded in making their ontology clear.     

一种求解锂离子电池单粒子模型液相扩散方程的新方法

电解液中的锂离子浓度表达是锂离子电池电化学模型求解的基本任务之一.为了平衡单粒子模型的液相动态性能和计算效率,假设反应仅发生在集电极和电解质界面上,为此,提出一种基于液相扩散方程无穷级数解析解的界面浓度求解新方法.在恒流工况下,利用数列单调收敛准则将解析解转化为一个收敛和函数.在动态工况下,将该解析解简化为输入与和函数的无限离散卷积.利用和函数随时间单调衰减并收敛至零的特性对其进行截断,从而得到有限离散卷积求解算法.对比专业有限元分析软件,该方法在恒流工况和动态工况下均能以较少的计算时间获得相当好的精度.而且,该方法仅有一个配置参数.因此,所提方法将有效减小应用于实时电池管理系统上的电化学模型计算负担.     

一种求解锂离子电池单粒子模型液相扩散方程的新方法

电解液中的锂离子浓度表达是锂离子电池电化学模型求解的基本任务之一.为了平衡单粒子模型的液相动态性能和计算效率,假设反应仅发生在集电极和电解质界面上,为此,提出一种基于液相扩散方程无穷级数解析解的界面浓度求解新方法.在恒流工况下,利用数列单调收敛准则将解析解转化为一个收敛和函数.在动态工况下,将该解析解简化为输入与和函数的无限离散卷积.利用和函数随时间单调衰减并收敛至零的特性对其进行截断,从而得到有限离散卷积求解算法.对比专业有限元分析软件,该方法在恒流工况和动态工况下均能以较少的计算时间获得相当好的精度.而且,该方法仅有一个配置参数.因此,所提方法将有效减小应用于实时电池管理系统上的电化学模型计算负担.     

On Solving Three-Dimensional Electrostatic Field Distribution by Multigrid Method

A highly efficient numerical algorithm using the multigrid method (MGM) is introduced to solve a three-dimensional (3-D)field distribution. Taking advantage of the restriction and prolongation in MGM computation, a more accurate field distribution can be acquired rapidly. According to the MGM algorithm, a 3-D program is accomplished, which can solve the field distributions in electron optical systems for various electrostatic lenses. The 3-D field distribution in an electrostatic concentric spherical model is tested with the MGM algorithm and with an algorithm based on the finite difference method (FDM). Comparing these two results in terms of computational efficiency and computational accuracy, it appears that MGM is superior to FDM, which is now used the most in field computations. This paper shows that the 3-D field computation using MGM greatly improves the computational efficiency of field distributions in electron optical systems and shortens the computational time.     

The Analysis of Mammalian Hearing Systems Supports the Hypothesis That Criticality Favors Neuronal Information Representation but Not Computation

In the neighborhood of critical states, distinct materials exhibit the same physical behavior, expressed by common simple laws among measurable observables, hence rendering a more detailed analysis of the individual systems obsolete. It is a widespread view that critical states are fundamental to neuroscience and directly favor computation. We argue here that from an evolutionary point of view, critical points seem indeed to be a natural phenomenon. Using mammalian hearing as our example, we show, however, explicitly that criticality does not describe the proper computational process and thus is only indirectly related to the computation in neural systems.     

Hadronic structure from the lattice

In recent years the investigation of hadron structure using lattice techniques has attracted growing attention. The computation of several important quantities has become feasible. Furthermore, theoretical developments as well as progress in algorithms and an increase in computing resources have contributed to a significantly improved control of systematic errors. In this article we give an overview on the work that has been carried out in the framework of the Hadron Physics I3 (I3HP) network “Computational (lattice) hadron physics”. Here we will not restrict ourselves to spin physics but focus on results for nucleon spectrum and structure from the QCDSF collaboration. For a broader overview of developments in this field see, e.g., [1].     

Novel schemes for measurement-based quantum computation

We establish a framework which allows one to construct novel schemes for measurement-based quantum computation. The technique develops tools from many-body physics-based on finitely correlated or projected entangled pair states-to go beyond the cluster-state based one-way computer. We identify resource states radically different from the cluster state, in that they exhibit nonvanishing correlations, can be prepared using nonmaximally entangling gates, or have very different local entanglement properties. In the computational models, randomness is compensated in a different manner. It is shown that there exist resource states which are locally arbitrarily close to a pure state. We comment on the possibility of tailoring computational models to specific physical systems.     

Parallelized Characteristic Basis Finite Element Method (CBFEM-MPI)—A non-iterative domain decomposition algorithm for electromagnetic scattering problems

In this paper, we introduce a parallelized version of a novel, non-iterative domain decomposition algorithm, called Characteristic Basis Finite Element Method (CBFEM-MPI), for efficient solution of large-scale electromagnetic scattering problems, by utilizing a set of specially defined characteristic basis functions (CBFs). This approach is based on the decomposition of the computational domain into a number of non-overlapping subdomains wherein the CBFs are generated by employing a novel procedure, which differs from all those that have been used in the past. Clearly, the CBFs are obtained by calculating the fields radiated by a finite number of dipole-type sources, which are placed hypothetically along the boundary of the conducting object. The major advantages of the proposed technique are twofold: (i) it provides a substantial reduction in the matrix size, and thus, makes use of direct solvers efficiently and (ii) it enables the utilization of parallel processing techniques that considerably decrease the overall computation time. We illustrate the application of the proposed approach via several 3D electromagnetic scattering problems.     

Using small molecules to probe protein cavities: The myoglobin–XO (X = C,N) family of systems

Myoglobin has been studied in considerable detail using different experimental and computational techniques over the past decades. Recent developments in time resolved spectroscopy have provided experimental data amenable to detailed atomistic simulations. Here, results from computational methods including molecular dynamics and free energy simulations are discussed which provide insight into the dynamics of ligands in confined spaces for MbCO. Application of these methods to calculate and understand experimental observations for myoglobin interacting with CO are presented and discussed.     

Computational models of music perception and cognition II: Domain-specific music processing

In Part I [Purwins H, Herrera P, Grachten M, Hazan A, Marxer R, Serra X. Computational models of music perception and cognition I: The perceptual and cognitive processing chain. Physics of Life Reviews 2008, in press, doi:10.1016/j.plrev.2008.03.004], we addressed the study of cognitive processes that underlie auditory perception of music, and their neural correlates. The aim of the present paper is to summarize empirical findings from music cognition research that are relevant to three prominent music theoretic domains: rhythm, melody, and tonality. Attention is paid to how cognitive processes like category formation, stimulus grouping, and expectation can account for the music theoretic key concepts in these domains, such as beat, meter, voice, consonance. We give an overview of computational models that have been proposed in the literature for a variety of music processing tasks related to rhythm, melody, and tonality. Although the present state-of-the-art in computational modeling of music cognition definitely provides valuable resources for testing specific hypotheses and theories, we observe the need for models that integrate the various aspects of music perception and cognition into a single framework. Such models should be able to account for aspects that until now have only rarely been addressed in computational models of music cognition, like the active nature of perception and the development of cognitive capacities from infancy to adulthood.     

Why Should We Interpret Quantum Mechanics?

The development of quantum information theory has renewed interest in the idea that the state vector does not represent the state of a quantum system, but rather the knowledge or information that we may have on the system. I argue that this epistemic view of states appears to solve foundational problems of quantum mechanics only at the price of being essentially incomplete.     

Virtual parallel computing and a search algorithm using matrix product States

We propose a form of parallel computing on classical computers that is based on matrix product states. The virtual parallelization is accomplished by representing bits with matrices and by evolving these matrices from an initial product state that encodes multiple inputs. Matrix evolution follows from the sequential application of gates, as in a logical circuit. The action by classical probabilistic one-bit and deterministic two-bit gates such as NAND are implemented in terms of matrix operations and, as opposed to quantum computing, it is possible to copy bits. We present a way to explore this method of computation to solve search problems and count the number of solutions. We argue that if the classical computational cost of testing solutions (witnesses) requires less than O(n^{2}) local two-bit gates acting on n bits, the search problem can be fully solved in subexponential time. Therefore, for this restricted type of search problem, the virtual parallelization scheme is faster than Grover's quantum algorithm.     

Typicality versus thermality: an analytic distinction

In systems with a large degeneracy of states such as black holes, one expects that the average value of probe correlation functions will be well approximated by the thermal ensemble. To understand how correlation functions in individual microstates differ from the canonical ensemble average and from each other, we study the variances in correlators. Using general statistical considerations, we show that the variance between microstates will be exponentially suppressed in the entropy. However, by exploiting the analytic properties of correlation functions we argue that these variances are amplified in imaginary time, thereby distinguishing pure states from the thermal density matrix. We demonstrate our general results in specific examples and argue that our results apply to the microstates of black holes.     

Direct search coding algorithm with reduction in computing time by simultaneous selection rule

An optimized encoding algorithm is required to produce high-quality computer-generated holograms (CGHs). For such a purpose, I have proposed that the use of the direct search algorithm (DSA) is effective for encoding the amplitude and phase in the Lohmann-type CGH. However, it takes much computation time to obtain an optimum solution by the DSA. To solve this problem, I have newly found that the simultaneous direct search algorithm (SDSA) is greatly effective for shortening the computation time for encoding the Lohmann-type CGH. As a result, the evaluation value of the reconstructed image for the SDSA is the same as that of 0.992 for the DSA. The computation time for the SDSA is drastically shortened from 3575 to 55 s for the DSA.