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At present modern quantum technologies can get a new twist of development, which will certainly give an opportunity to obtain solutions for numerous problems that previously could not be solved in the framework of “traditional” paradigms and computational models. All mankind stands at the threshold of the so-called “second quantum revolution”, and its short-term and long-term consequences will affect virtually all spheres of life of a global society. Such directions and branches of science and technology as materials science, nanotechnology, pharmacology and biochemistry in general, modeling of chaotic dynamic processes (nuclear explosions, turbulent flows, weather and long-term climatic phenomena), etc. will be directly developed, as well as the solution of any problems, which reduce to the multiplication of matrices of large dimensions (in particular, the modeling of quantum systems). However, along with extraordinary opportunities, quantum technologies carry with them certain risks and threats, in particular, the scrapping of all information systems based on modern achievements in cryptography, which will entail almost complete destruction of secrecy, the global financial crisis due to the destruction of the banking sector and compromise of all communication channels. Even in spite of the fact that methods of so-called “post-quantum” cryptography are already being developed today, some risks still need to be realized, since not all long-term consequences can be calculated. At the same time, one should be prepared to all of the above, including by training specialists working in the field of quantum technologies and understanding all their aspects, new opportunities, risks and threats. In this connection, this article briefly describes the current state of quantum technologies, namely, quantum sensorics, information transfer using quantum protocols, a universal quantum computer (hardware), and quantum computations based on quantum algorithms (software). For all of the above, forecasts are given for the development of the impact on various areas of human civilization.

The work belongs to the direction of experimental mathematics, which investigates the properties of mathematical objects by the computing facilities of a computer. The base is an exponential map, its topological properties (Cantor's bouquets) differ from properties of polynomial and rational complex-valued functions. The subject of the study are the character and features of the Fatou and Julia sets, as well as the equilibrium points and orbits of the zero of three iterated complex-valued mappings: $f:z \to (1+ \mu) \exp (iz)$, $g : z \to \big(1+ \mu |z - z^*|\big) \exp (iz)$, $h : z \to \big(1+ \mu (z - z^* )\big) \exp (iz)$, with $z,\mu \in \mathbb{C}$, $z^* : \exp (iz^*) = z^*$. For a quasilinear map g having no analyticity characteristic, two bifurcation transitions were discovered: the creation of a new equilibrium point (for which the critical value of the linear parameter was found and the bifurcation consists of “fork” type and “saddle”-node transition) and the transition to the radical transformation of the Fatou set. A nontrivial character of convergence to a fixed point is revealed, which is associated with the appearance of “valleys” on the graph of convergence rates. For two other maps, the monoperiodicity of regimes is significant, the phenomenon of “period doubling” is noted (in one case along the path $39\to 3$, in the other along the path $17\to 2$), and the coincidence of the period multiplicity and the number of sleeves of the Julia spiral in a neighborhood of a fixed point is found. A rich illustrative material, numerical results of experiments and summary tables reflecting the parametric dependence of maps are given. Some questions are formulated in the paper for further research using traditional mathematics methods.

The study of the physiological and pathophysiological processes in the cardiovascular system is one of the important contemporary issues, which is addressed in many works. In this work, several approaches to the mathematical modelling of the blood flow are considered. They are based on the spatial order reduction and/or use a steady-state approach. Attention is paid to the discussion of the assumptions and suggestions, which are limiting the scope of such models. Some typical mathematical formulations are considered together with the brief review of their numerical implementation. In the first part, we discuss the models, which are based on the full spatial order reduction and/or use a steady-state approach. One of the most popular approaches exploits the analogy between the flow of the viscous fluid in the elastic tubes and the current in the electrical circuit. Such models can be used as an individual tool. They also used for the formulation of the boundary conditions in the models using one dimensional (1D) and three dimensional (3D) spatial coordinates. The use of the dynamical compartment models allows describing haemodynamics over an extended period (by order of tens of cardiac cycles and more). Then, the steady-state models are considered. They may use either total spatial reduction or two dimensional (2D) spatial coordinates. This approach is used for simulation the blood flow in the region of microcirculation. In the second part, we discuss the models, which are based on the spatial order reduction to the 1D coordinate. The models of this type require relatively small computational power relative to the 3D models. Within the scope of this approach, it is also possible to include all large vessels of the organism. The 1D models allow simulation of the haemodynamic parameters in every vessel, which is included in the model network. The structure and the parameters of such a network can be set according to the literature data. It also exists methods of medical data segmentation. The 1D models may be derived from the 3D Navier – Stokes equations either by asymptotic analysis or by integrating them over a volume. The major assumptions are symmetric flow and constant shape of the velocity profile over a cross-section. These assumptions are somewhat restrictive and arguable. Some of the current works paying attention to the 1D model’s validation, to the comparing different 1D models and the comparing 1D models with clinical data. The obtained results reveal acceptable accuracy. It allows concluding, that the 1D approach can be used in medical applications. 1D models allow describing several dynamical processes, such as pulse wave propagation, Korotkov’s tones. Some physiological conditions may be included in the 1D models: gravity force, muscles contraction force, regulation and autoregulation.

The problem of online convex optimization naturally occurs in cases when there is an update of statistical information. The mirror descent method is well known for non-smooth optimization problems. Mirror descent is an extension of the subgradient method for solving non-smooth convex optimization problems in the case of a non-Euclidean distance. This paper is devoted to a stochastic variant of recently proposed Mirror Descent methods for convex online optimization problems with convex Lipschitz (generally, non-smooth) functional constraints. This means that we can still use the value of the functional constraint, but instead of (sub)gradient of the objective functional and the functional constraint, we use their stochastic (sub)gradients. More precisely, assume that on a closed subset of $n$-dimensional vector space, $N$ convex Lipschitz non-smooth functionals are given. The problem is to minimize the arithmetic mean of these functionals with a convex Lipschitz constraint. Two methods are proposed, for solving this problem, using stochastic (sub)gradients: adaptive method (does not require knowledge of Lipschitz constant neither for the objective functional, nor for the functional of constraint) and non-adaptivemethod (requires knowledge of Lipschitz constant for the objective functional and the functional of constraint). Note that it is allowed to calculate the stochastic (sub)gradient of each functional only once. In the case of non-negative regret, we find that the number of non-productive steps is $O$($N$), which indicates the optimality of the proposed methods. We consider an arbitrary proximal structure, which is essential for decisionmaking problems. The results of numerical experiments are presented, allowing to compare the work of adaptive and non-adaptive methods for some examples. It is shown that the adaptive method can significantly improve the number of the found solutions.

In various applications arise problems modeled by nonlinear partial differential equations on graphs (networks, trees). In order to study such problems and various extreme situations arose in the problems of designing and optimizing networks developed the computational model based on solving the corresponding boundary problems for partial differential equations of hyperbolic type on graphs (networks, trees). As applications, three different problems were chosen solved in the framework of the general approach of network computational models. The first was modeling of traffic flow. In solving this problem, a macroscopic approach was used in which the transport flow is described by a nonlinear system of second-order hyperbolic equations. The results of numerical simulations showed that the model developed as part of the proposed approach well reproduces the real situation various sections of the Moscow transport network on significant time intervals and can also be used to select the most optimal traffic management strategy in the city. The second was modeling of data flows in computer networks. In this problem data flows of various connections in packet data network were simulated as some continuous medium flows. Conceptual and mathematical network models are proposed. The numerical simulation was carried out in comparison with the NS-2 network simulation system. The results showed that in comparison with the NS-2 packet model the developed streaming model demonstrates significant savings in computing resources while ensuring a good level of similarity and allows us to simulate the behavior of complex globally distributed IP networks. The third was simulation of the distribution of gas impurities in ventilation networks. It was developed the computational mathematical model for the propagation of finely dispersed or gas impurities in ventilation networks using the gas dynamics equations by numerical linking of regions of different sizes. The calculations shown that the model with good accuracy allows to determine the distribution of gas-dynamic parameters in the pipeline network and solve the problems of dynamic ventilation management.

Padé approximation is a useful tool for extracting singularity information from a power series. A linear Padé approximant is a rational function and can provide estimates of pole and zero locations in the complex plane. A quadratic Padé approximant has square root singularities and can, therefore, provide additional information such as estimates of branch point locations. In this paper, we discuss numerical aspects of computing quadratic Padé approximants as well as some applications. Two algorithms for computing the coefficients in the approximant are discussed: a direct method involving the solution of a linear system (well-known in the mathematics community) and a recursive method (well-known in the physics community). We compare the accuracy of these two methods when implemented in floating-point arithmetic and discuss their pros and cons. In addition, we extend Luke’s perturbation analysis of linear Padé approximation to the quadratic case and identify the problem of spurious branch points in the quadratic approximant, which can cause a significant loss of accuracy. A possible remedy for this problem is suggested by noting that these troublesome points can be identified by the recursive method mentioned above. Another complication with the quadratic approximant arises in choosing the appropriate branch. One possibility, which is to base this choice on the linear approximant, is discussed in connection with an example due to Stahl. It is also known that the quadratic method is capable of providing reasonable approximations on secondary sheets of the Riemann surface, a fact we illustrate here by means of an example. Two concluding applications show the superiority of the quadratic approximant over its linear counterpart: one involving a special function (the Lambert $W$-function) and the other a nonlinear PDE (the continuation of a solution of the inviscid Burgers equation into the complex plane).

The paper concerns the study of the Rice statistical distribution’s peculiarities which cause the possibility of its efficient application in solving the tasks of high precision phase measuring in optics. The strict mathematical proof of the Rician distribution’s stable character is provided in the example of the differential signal consideration, namely: it has been proved that the sum or the difference of two Rician signals also obey the Rice distribution. Besides, the formulas have been obtained for the parameters of the resulting summand or differential signal’s Rice distribution. Based upon the proved stable character of the Rice distribution a new original technique of the high precision measuring of the two quasi-harmonic signals’ phase shift has been elaborated in the paper. This technique is grounded in the statistical analysis of the measured sampled data for the amplitudes of the both signals and for the amplitude of the third signal which is equal to the difference of the two signals to be compared in phase. The sought-for phase shift of two quasi-harmonic signals is being calculated from the geometrical considerations as an angle of a triangle which sides are equal to the three indicated signals’ amplitude values having been reconstructed against the noise background. Thereby, the proposed technique of measuring the phase shift using the differential signal analysis, is based upon the amplitude measurements only, what significantly decreases the demands to the equipment and simplifies the technique implementation in practice. The paper provides both the strict mathematical substantiation of a new phase shift measuring technique and the results of its numerical testing. The elaborated method of high precision phase measurements may be efficiently applied for solving a wide circle of tasks in various areas of science and technology, in particular — at distance measuring, in communication systems, in navigation, etc.

Roads are a shared resource which can be used either by drivers and autonomous vehicles. Since the total number of vehicles increases annually, each considered vehicle spends more time in traffic jams, and thus the total travel time prolongs. The main purpose while planning the road system is to reduce the time spent on traveling. The optimization of transportation networks is a current goal, thus the formation of traffic flows by creating certain ligaments of the roads is of high importance. The Braess paradox states the existence of a network where the construction of a new edge leads to the increase of traveling time. The objective of this paper is to propose various solutions to the Braess paradox in the presence of autonomous vehicles. One of the methods of solving transportation topology problems is to introduce artificial restrictions on traffic. As an example of such restrictions, this article considers designated lanes which are available only for a certain type of vehicles. Designated lanes have their own location in the network and operating conditions. This article observes the most common two-roads traffic situations, analyzes them using analytical and numerical methods and presents the model of optimal traffic flow distribution, which considers different ways of lanes designation on isolated transportation networks. It was found that the modeling of designated lanes eliminates Braess’ paradox and optimizes the total traveling time. The solutions were shown on artificial networks and on the real-life example. A modeling algorithm for Braess network was proposed and its correctness was verified using the real-life example.

In this paper, we consider the conjugate gradient method for solving the problem of minimizing a quadratic function with additive noise in the gradient. Three concepts of noise were considered: antagonistic noise in the linear term, stochastic noise in the linear term and noise in the quadratic term, as well as combinations of the first and second with the last. It was experimentally obtained that error accumulation is absent for any of the considered concepts, which differs from the folklore opinion that, as in accelerated methods, error accumulation must take place. The paper gives motivation for why the error may not accumulate. The dependence of the solution error both on the magnitude (scale) of the noise and on the size of the solution using the conjugate gradient method was also experimentally investigated. Hypotheses about the dependence of the error in the solution on the noise scale and the size (2-norm) of the solution are proposed and tested for all the concepts considered. It turned out that the error in the solution (by function) linearly depends on the noise scale. The work contains graphs illustrating each individual study, as well as a detailed description of numerical experiments, which includes an account of the methods of noise of both the vector and the matrix.

The paper presents a numerical solution to the heat wave motion problem for a degenerate second-order nonlinear parabolic equation with a source term. The nonlinearity is conditioned by the power dependence of the heat conduction coefficient on temperature. The problem for the case of two spatial variables is considered with the boundary condition specifying the heat wave motion law. A new solution algorithm based on an expansion in radial basis functions and the boundary element method is proposed. The solution is constructed stepwise in time with finite difference time approximation. At each time step, a boundary value problem for the Poisson equation corresponding to the original equation at a fixed time is solved. The solution to this problem is constructed iteratively as the sum of a particular solution to the nonhomogeneous equation and a solution to the corresponding homogeneous equation satisfying the boundary conditions. The homogeneous equation is solved by the boundary element method. The particular solution is sought by the collocation method using inhomogeneity expansion in radial basis functions. The calculation algorithm is optimized by parallelizing the computations. The algorithm is implemented as a program written in the C++ language. The parallel computations are organized by using the OpenCL standard, and this allows one to run the same parallel code either on multi-core CPUs or on graphic CPUs. Test cases are solved to evaluate the effectiveness of the proposed solution method and the correctness of the developed computational technique. The calculation results are compared with known exact solutions, as well as with the results we obtained earlier. The accuracy of the solutions and the calculation time are estimated. The effectiveness of using various systems of radial basis functions to solve the problems under study is analyzed. The most suitable system of functions is selected. The implemented complex computational experiment shows higher calculation accuracy of the proposed new algorithm than that of the previously developed one.