In recent years, several computer scientists and physicists have been exploring the potential of quantum-enhanced machine learning algorithms. As their name suggests, quantum machine learning approaches combine quantum algorithms ...
Determining the quantum mechanical behavior of many interacting particles is essential to solving important problems in a variety of scientific fields, including physics, chemistry and mathematics. For instance, in order ...
In materials science, surfaces that strongly repel low surface tension liquids are classified 'superoleophobic," while high surface tension liquid repellants are 'superhydrophobic' and surfaces that display both characteristics ...
Searching large, unordered databases for a desired item is a time-consuming task for classical computers, but quantum computers are expected to perform these searches much more quickly. Previous research has shown that Grover's ...
(Phys.org)—Physicists have applied the ability of machine learning algorithms to learn from experience to one of the biggest challenges currently facing quantum computing: quantum error correction, which is used to design ...
(Phys.org)—Inspired by natural selection and the concept of "survival of the fittest," genetic algorithms are flexible optimization techniques that can find the best solution to a problem by repeatedly selecting for and ...
(Phys.org) -- While factoring an integer is a simple problem when the integer is small, the complexity of factorization greatly increases as the integer increases. When the integer grows to more than 100,000 or so digits, ...
(PhysOrg.com) -- A primitive quantum computer that uses single particles of light (photons) whizzing through a silicon chip has performed its first mathematical calculation. This is the first time a calculation has been performed ...
Quantum Physics
Sep 3, 2009
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A team of astronomers has found that the total mass of stars in a galaxy is not a good predictor of the galaxy's abundance of heavier elements, a surprising result according to previous studies. Instead, the gravitational ...
Window glass, at the microscopic level, shows a strange mix of properties. Like a liquid, its atoms are disordered, but like a solid, its atom are rigid, so a force applied to one atom causes all of them to move.
Condensed Matter
Sep 6, 2023
0
210
In mathematics, computing, linguistics, and related subjects, an algorithm is a finite sequence of instructions, an explicit, step-by-step procedure for solving a problem, often used for calculation and data processing. It is formally a type of effective method in which a list of well-defined instructions for completing a task, will when given an initial state, proceed through a well-defined series of successive states, eventually terminating in an end-state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as probabilistic algorithms, incorporate randomness.
A partial formalization of the concept began with attempts to solve the Entscheidungsproblem (the "decision problem") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define "effective calculability" (Kleene 1943:274) or "effective method" (Rosser 1939:225); those formalizations included the Gödel-Herbrand-Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's "Formulation 1" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939.
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