Impagliazo and A. Julsgaard, A. Kozhekin, and E. Klapenecker and M. Beyond stabilizer codes I: nice error bases. Lalire and Ph. Lidar, I. Chuang, and K. Marcikic, H. Tittel, H. Zbinden, M. Legre, and N. Mitchison and R. Mosca, A. Tapp, and R. Nielsen and I. Quantum information processing. Cambridge University Press, Perdrix and P. Perdrix and Ph.

Popescu and D. Raussendorf and H.

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Quantum computing by measurements only. Lett , 86 , Exponential separation of quantum and classical communication complexity. Feats, features and failures of the PR-box.

Scarani, W. Zbinden, and N. The speed of quantum information and the preference frame: analysis of experimental data. Schumacher and R. Algorithms for quantum computation: discrete log and factoring. Shor Scheme for reducing decoherence in quantum computer memory. Shor Fault-tolerant quantum computation.

### Seeking a quantum solution to really big problems

Short, N. Cisin, and S. Somma, H. Barnum, and G. Mathematics unlimited, and beyond , chapter The Turing machine paradigm in contemporary computing, pages — Springer Verlag, Vartiainen, M. Vatan and C. Optimal realization of a general two-qubit quantum gate. Realization of a general three-qubit quantum gate. Wiedermann and J. Relativistic computers and non-uniform complexity theory.

Personalised recommendations. Cite paper How to cite? Algorithmic approach to quantum theory 2: method of collective behavior and Monte-Carlo method Author s : I. Semenihin; Y. The method of collective behavior is based on the representation of real quantum particle by the swarm of classical particles which have all properties of the initial particle but have classical states like coordinates and impulse.

Simulation with swarms can be more flexible and powerful than analytical methods because it preserves the methodology of classical description of dynamics. The method of collective behavior is illustrated on the diffusion Monte Carlo way of calculating stationary states of electrons. Algorithmic approach to quantum theory 1: features of many particle quantum dynamics Author s : Yuri Ozhigov ; Igor Semenihin Show Abstract. Algorithmic approach to quantum theory is considered. It is based on the supposition that every evolution of many particle system can be simulated by classical algorithms of polynomial complexity.

This hypothesis agrees with all known experiments but it presumes the principle cut-off of quantum formalism because it excludes a scalable quantum computer. Algorithmic approach describes quantum evolution uniformly, without separation of measurements from the unitary dynamics; it is shown how Bohrn rule for quantum probability follows from the basic principles of this approach.

## Some Elements of Quantum Informatics

The radical difference of algorithmic approach from the standard and its perspectives are discussed. Quantum mechanical view of mathematical statistics Author s : Yu. Multiparametric statistical model providing stable reconstruction of parameters by observations is considered. The only general method of this kind is the root model based on the representation of the probability density as a squared absolute value of a certain function, which is referred to as a psi function in analogy with quantum mechanics.

The psi function is represented by an expansion in terms of an orthonormal set of functions. It is shown that the introduction of the psi function allows one to represent the Fisher information matrix as well as statistical properties of the estimator of the state vector state estimator in simple analytical forms.

## Quantum Approach to Informatics

A new statistical characteristic, a confidence cone, is introduced instead of a standard confidence interval. The chi-square test is considered to test the hypotheses that the estimated vector converges to the state vector of a general population and that both samples are homogeneous. The expansion coefficients are estimated by the maximum likelihood method. An iteration algorithm for solving the likelihood equation is presented. The stability and rate of convergence of the solution are studied. A special iteration parameter is introduced: its optimal value is chosen on the basis of the maximin strategy.

Numerical simulation is performed using the set of the Chebyshev-Hermite functions as a basis. Practical error-correction procedures in quantum cryptography Author s : A. Makkaveyev; S. Molotkov; D. Pomozov; A.