Thermal chaos returns quantum system to its unknown past

Building on last year's breakthrough 'time reversal' experiment, two researchers from the Moscow Institute of Physics and Technology and Argonne National Laboratory have published a new theoretical study in Communications ...

Quantum machines learn 'quantum data'

Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a "fertile ground to ...

Learning more about particle collisions with machine learning

The Large Hadron Collider (LHC) near Geneva, Switzerland became famous around the world in 2012 with the detection of the Higgs boson. The observation marked a crucial confirmation of the Standard Model of particle physics, ...

Whales used to identify Arabian horses

A computer program that mimics in software the social interactions of the humpback whale has been used by researchers in Egypt to build a system for the identification of Arabian horses.

Computer vision helps scientists study lithium ion batteries

Lithium-ion batteries lose their juice over time, causing scientists and engineer to work hard to understand that process in detail. Now, scientists at the Department of Energy's SLAC National Accelerator Laboratory have ...

Speak math, not code

Have you ever followed a recipe to bake some bread? If you have, congratulations; you have executed an algorithm. The algorithms that follow us around the internet to suggest items we might like, and those that control what ...

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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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