量子搜索及量子智能优化研究进展

为了提高智能优化算法的收敛速度及优化性能,目前国内外将量子计算机制和传统智能优化相融合,研究和提出了多种量子进化算法及量子群智能优化算法;为了进一步推动该领域的研究进展,系统地介绍了国内外提出的多种量子搜索及量子智能优化算法,其中包括量子搜索、量子衍生进化、量子神经网络三个方面内容;总结出目前改进量子搜索算法的主要机制和量子计算与传统智能计算的主要融合方式,并展望了量子搜索和量子智能优化有待进一步研究和需要解决的问题。     

Further Results on the Observability of Quantum Systems Under General Measurement

In this paper, we present a collection of results on the observability of quantum mechanical systems, in the case the output is the result of a discrete nonselective measurement. By defining an effective observable, we extend previous results, on the Lie algebraic characterization of observable systems, to general measurements. Further results include the characterization of a ‘best probe’ (i.e. a minimally disturbing probe) in indirect measurement and a study of the relation between disturbance and observability in this case. We also discuss how the observability properties of a quantum system relate to the problem of state reconstruction. Extensions of the formalism to the case of selective measurements are also given.     

A Constructive Graphical Model Approach for Knowledge-Based Systems: A Vehicle Monitoring Case Study

Graphical models have been widely applied to uncertain reasoning in knowledge-based systems. For many of the problems tackled, a single graphical model is constructed before individual cases are presented and the model is used to reason about each new case. In this work, we consider a class of problems whose solution requires inference over a very large number of models that are impractical to construct a priori. We conduct a case study in the domain of vehicle monitoring and then generalize the approach taken. We show that the previously held negative belief on the applicability of graphical models to such problems is unjustified. We propose a set of techniques based on domain decomposition, model separation, model approximation, model compilation, and re-analysis to meet the computational challenges imposed by the combinatorial explosion. Experimental results on vehicle monitoring demonstrated good performance at near-real-time.     

Learning invariant features using inertial priors

We address the technical challenges involved in combining key features from several theories of the visual cortex in a single coherent model. The resulting model is a hierarchical Bayesian network factored into modular component networks embedding variable-order Markov models. Each component network has an associated receptive field corresponding to components residing in the level directly below it in the hierarchy. The variable-order Markov models account for features that are invariant to naturally occurring transformations in their inputs. These invariant features give rise to increasingly stable, persistent representations as we ascend the hierarchy. The receptive fields of proximate components on the same level overlap to restore selectivity that might otherwise be lost to invariance.      

Factorial Hidden Markov Models

Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach's chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot.     

Sequential decision-theoretic models and expert systems

Sequential decision models are an important component of expert systems since, in general, the cost of acquiring information is significant and there is a trade-off between the cost and the value of information. Many expert systems in various domains (business, engineering, medicine etc.), needing costly inputs that are not known until the system operates, have to face this problem. In the last decade the field of sequential decision models based on decision theory (sequential decision-theoretic models) have become more and more important due to both the continuous progress made by research in Bayesian networks and the availability of modern powerful tools for building Bayesian networks and for probability propagation. This paper provides readers (especially knowledge engineers and expert system designers) with a unified and integrated presentation of the disparate literature in the field of sequential decision-making based on decision theory, in order to improve comprehensibility and accessibility. Besides the presentation of the general theory, a view of sequential diagnosis as an instance of the general concept of sequential decision-theoretic models is also shown.     

Construction and Methods of Learning of Bayesian Networks

Methods of learning Bayesian networks from databases, basic concepts of Bayesian networks, basic methods of learning, methods of learning parameters, and the structures of a network and hidden parameters are considered. Basic definitions and key concepts with illustrative examples are presented. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 133–147, July–August 2005.     

Ergodic Quantum Computing

We propose a (theoretical) model for quantum computation where the result can be read out from the time average of the Hamiltonian dynamics of a 2-dimensional crystal on a cylinder.The Hamiltonian is a spatially local interaction among Wigner–Seitz cells containing six qubits. The quantum circuit that is simulated is specified by the initialization of program qubits. As in Margolus Hamiltonian cellular automaton (implementing classical circuits), a propagating wave in a clock register controls asynchronously the application of the gates. However, in our approach all required initializations are basis states. After a while the synchronizing wave is essentially spread around the whole crystal. The circuit is designed such that the result is available with probability about 1/4 despite of the completely undefined computation step. This model reduces quantum computing to preparing basis states for some qubits, waiting, and measuring in the computational basis. Even though it may be unlikely to find our specific Hamiltonian in real solids, it is possible that also more natural interactions allow ergodic quantum computing.PACS:03.67.Lx     

A Computational Model for Quantum Measurement

Quantum Information Processing - Is the dynamical evolution of physical systems objectively a manifestation of information processing by the universe? We find that an affirmative answer has...     

Population Markov Chain Monte Carlo

Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a Metropolis-Hastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physics-inspired local reversibility conditions.     

How Do Two Observers Pool Their Knowledge About a Quantum System?

In the theory of classical statistical inference one can derive a simple rule by which two or more observers may combine independently obtained states of knowledge together to form a new state of knowledge, which is the state which would be possessed by someone having the combined information of both observers. Moreover, this combined state of knowledge can be found without reference to the manner in which the respective observers obtained their information. However, we show that in general this is not possible for quantum states of knowledge; in order to combine two quantum states of knowledge to obtain the state resulting from the combined information of both observers, these observers must also possess information about how their respective states of knowledge were obtained. Nevertheless, we emphasize this does not preclude the possibility that a unique, well motivated rule for combining quantum states of knowledge without reference to a measurement history could be found. We examine both the direct quantum analog of the classical problem, and that of quantum state-estimation, which corresponds to a variant in which the observers share a specific kind of prior information. PACS: 03.67.-a, 02.50.-r, 03.65.Bz     

计算理论研究的核心问题与方向

论述了计算理论的发展历史 ,核心研究工作 (包括计算模型、算法设计和分析 ) ,最近取得的新成果 ,一些尚未解决的问题及其发展趋势。     

On the probability theory on MV algebras

 The paper contains a review of some results concerning probability theory on MV algebras (laws of large numbers, central limit theorem, martingale convergence theorem). Also some algebraic and methodical aspects are discussed.     

Quantum model of computations: Underlying principles and achievements

A quantum Turing machine is considered. A review of basic methodological principles and achievements in the field of quantum computations is given. Some problems of construction of correct quantum computations and their complexity are considered. The result of P. Shor concerning the solution of the problems of taking discrete logarithms in polynomial time relative to the length of numbers is considered in detail. Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 58–76, January–February, 2000.     

An Introduction to Variational Methods for Graphical Models

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.     

基于量子计算的医疗数据敏感度度量

针对传统的医疗数据敏感度度量方法存在的度量开销大、数据查准率和查全率低的问题,提出基于量子计算的医疗数据敏感度度量方法.首先采用分布式样本重构方法重组医疗数据的分布式结构,建立医疗数据敏感度度量的统计分析模型;其次采用量化回归分析方法进行医疗数据的模糊融合和聚类分析,建立其定量递归分析模型;最后采用量子计算方法进行医疗...