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Quantum Algorithm For Linear Systems Of Equations
The Harrow–Hassidim–Lloyd (HHL) algorithm is a quantum algorithm for numerically solving a system of linear equations, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd. The algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations. The algorithm is one of the main fundamental algorithms expected to provide a speedup over their classical counterparts, along with Shor's Algorithm, Shor's factoring algorithm and Grover's algorithm, Grover's search algorithm. Provided the linear system is sparse matrix, sparse and has a low condition number \kappa, and that the user is interested in the result of a scalar measurement on the solution vector, instead of the values of the solution vector itself, then the algorithm has a runtime of O(\log(N)\kappa^2), where N is the number of variables in the linear system. This offers an exponential speedup over the fastest classical algorithm, which runs in O(N\kappa) (or O(N\sqrt) ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Quantum Algorithm
In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical (or non-quantum) algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is generally reserved for algorithms that seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement. Problems that are undecidable using classical computers remain undecidable using quantum computers. What makes quantum algorithms interesting is that they might be able to solve some problems fa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gaussian Elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients. This method can also be used to compute the rank of a matrix, the determinant of a square matrix, and the inverse of an invertible matrix. The method is named after Carl Friedrich Gauss (1777–1855). To perform row reduction on a matrix, one uses a sequence of elementary row operations to modify the matrix until the lower left-hand corner of the matrix is filled with zeros, as much as possible. There are three types of elementary row operations: * Swapping two rows, * Multiplying a row by a nonzero number, * Adding a multiple of one row to another row. Using these operations, a matrix can always be transformed into an upper triangular matrix (possibly bordered by rows or columns of zeros), and in fact one that is in row echelon form. Once all of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Least-squares Fit
The method of least squares is a mathematical optimization technique that aims to determine the best fit function by minimizing the sum of the squares of the differences between the observed values and the predicted values of the model. The method is widely used in areas such as regression analysis, curve fitting and data modeling. The least squares method can be categorized into linear and nonlinear forms, depending on the relationship between the model parameters and the observed data. The method was first proposed by Adrien-Marie Legendre in 1805 and further developed by Carl Friedrich Gauss. History Founding The method of least squares grew out of the fields of astronomy and geodesy, as scientists and mathematicians sought to provide solutions to the challenges of navigating the Earth's oceans during the Age of Discovery. The accurate description of the behavior of celestial bodies was the key to enabling ships to sail in open seas, where sailors could no longer rely on la ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Finance
Computational finance is a branch of applied computer science that deals with problems of practical interest in finance.Rüdiger U. Seydel, ''Tools for Computational Finance'', Springer; 3rd edition (May 11, 2006) 978-3540279235 Some slightly different definitions are the study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial mathematical model, models or systems.Cornelis A. Los, ''Computational Finance'' World Scientific Pub Co Inc (December 2000) Computational finance emphasizes practical numerical analysis, numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economics, economic analyses.Mario J. Miranda and Paul L. Fackler, ''Applied Computational Economics and Finance'', The MIT Press (September 16, 2002) It is an interdisciplinary field between mathematical finance and numerical analysis, numerical methods.Omur Ugur, ''Introduction to Computational Finance'', Imper ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Finite Element Method
Finite element method (FEM) is a popular method for numerically solving differential equations arising in engineering and mathematical modeling. Typical problem areas of interest include the traditional fields of structural analysis, heat transfer, fluid flow, mass transport, and electromagnetic potential. Computers are usually used to perform the calculations required. With high-speed supercomputers, better solutions can be achieved and are often required to solve the largest and most complex problems. FEM is a general numerical method for solving partial differential equations in two- or three-space variables (i.e., some boundary value problems). There are also studies about using FEM to solve high-dimensional problems. To solve a problem, FEM subdivides a large system into smaller, simpler parts called finite elements. This is achieved by a particular space discretization in the space dimensions, which is implemented by the construction of a mesh of the object: the numer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nonlinear Schrödinger Equation
In theoretical physics, the (one-dimensional) nonlinear Schrödinger equation (NLSE) is a nonlinear variation of the Schrödinger equation. It is a classical field equation whose principal applications are to the propagation of light in nonlinear optical fibers, planar waveguides and hot rubidium vapors and to Bose–Einstein condensates confined to highly anisotropic, cigar-shaped traps, in the mean-field regime. Additionally, the equation appears in the studies of small-amplitude gravity waves on the surface of deep inviscid (zero-viscosity) water; the Langmuir waves in hot plasmas; the propagation of plane-diffracted wave beams in the focusing regions of the ionosphere; the propagation of Davydov's alpha-helix solitons, which are responsible for energy transport along molecular chains; and many others. More generally, the NLSE appears as one of universal equations that describe the evolution of slowly varying packets of quasi-monochromatic waves in weakly nonlinear me ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Carleman Linearization
In mathematics, Carleman linearization (or Carleman embedding) is a technique to transform a finite-dimensional nonlinear dynamical system into an infinite-dimensional linear system. It was introduced by the Swedish mathematician Torsten Carleman in 1932. Carleman linearization is related to composition operator and has been widely used in the study of dynamical systems. It also been used in many applied fields, such as in control theory and in quantum computing. Procedure Consider the following autonomous nonlinear system: : \dot=f(x)+\sum_^m g_j(x)d_j(t) where x\in R^n denotes the system state vector. Also, f and g_i's are known analytic vector functions, and d_j is the j^ element of an unknown disturbance to the system. At the desired nominal point, the nonlinear functions in the above system can be approximated by Taylor expansion : f(x)\simeq f(x_0)+ \sum _^\eta \frac\partial f_\mid _(x-x_0)^ where \partial f_\mid _ is the k^ partial derivative of f(x) with respect ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Radar Cross-section
Radar cross-section (RCS), denoted σ, also called radar signature, is a measure of how detectable an object is by radar. A larger RCS indicates that an object is more easily detected. An object reflects a limited amount of radar energy back to the source. The factors that influence this include: *the material with which the target is made; *the size of the target relative to the wavelength of the illuminating radar signal; *the absolute size of the target; *the incident angle (angle at which the radar beam hits a particular portion of the target, which depends upon the shape of the target and its orientation to the radar source); *the reflected angle (angle at which the reflected beam leaves the part of the target hit; it depends upon incident angle); *the polarization of the radiation transmitted and received with respect to the orientation of the target. While important in detecting targets, strength of emitter and distance are not factors that affect the calculation o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Preconditioner
In mathematics, preconditioning is the application of a transformation, called the preconditioner, that conditions a given problem into a form that is more suitable for numerical solving methods. Preconditioning is typically related to reducing a condition number of the problem. The preconditioned problem is then usually solved by an iterative method. Preconditioning for linear systems In linear algebra and numerical analysis, a preconditioner P of a matrix A is a matrix such that P^A has a smaller condition number than A. It is also common to call T=P^ the preconditioner, rather than P, since P itself is rarely explicitly available. In modern preconditioning, the application of T = P^, i.e., multiplication of a column vector, or a block of column vectors, by T = P^, is commonly performed in a matrix-free fashion, i.e., where neither P, nor T = P^ (and often not even A) are explicitly available in a matrix form. Preconditioners are useful in iterative methods to solve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stefanie Barz
Stefanie Barz is a German physicist and Professor of Quantum Information and Technology at the University of Stuttgart working in the field of photonic quantum technology. Early life and education Barz studied at the Johannes Gutenberg University Mainz, with a stay at the KTH Royal Institute of Technology within the Erasmus Programme. She earned her PhD from the University of Vienna under the supervision of Anton Zeilinger, working on various aspects of photonic quantum computing, including 'blind' quantum computing using entangled photons, where the sender knows the initial state of entanglement while companies in control of data processing do not, making it impossible for them to decode the information without destroying it. For her dissertation she was awarded the Laudimaxima Prize of the University of Vienna, and her work has been featured in ''New Scientist'' and covered by the BBC and NBC. In 2013 Barz was awarded the Maria Schaumayer Prize and the Loschmidt Prize. Fro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PSPACE
In computational complexity theory, PSPACE is the set of all decision problems that can be solved by a Turing machine using a polynomial amount of space. Formal definition If we denote by SPACE(''f''(''n'')), the set of all problems that can be solved by Turing machines using ''O''(''f''(''n'')) space for some function ''f'' of the input size ''n'', then we can define PSPACE formally asArora & Barak (2009) p.81 :\mathsf = \bigcup_ \mathsf(n^k). It turns out that allowing the Turing machine to be nondeterministic does not add any extra power. Because of Savitch's theorem,Arora & Barak (2009) p.85 NPSPACE is equivalent to PSPACE, essentially because a deterministic Turing machine can simulate a nondeterministic Turing machine without needing much more space (even though it may use much more time).Arora & Barak (2009) p.86 Also, the complements of all problems in PSPACE are also in PSPACE, meaning that co-PSPACE PSPACE. Relation among other classes The following re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
