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 ...
The second law of thermodynamics delineates an asymmetry in how physical systems evolve over time, known as the arrow of time. In macroscopic systems, this asymmetry has a clear direction (e.g., one can easily notice if ...
Many research groups worldwide are currently trying to develop instruments to collect high-precision measurements, such as atomic clocks or gravimeters. Some of these researchers have tried to achieve this using entangled ...
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)—Physicists have experimentally demonstrated a purely quantum method for solving systems of linear equations that has the potential to work exponentially faster than the best classical methods. The results show ...
(Phys.org)—Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. ...
(Phys.org)—Every time you take a photograph, your camera detects more than a billion photons. For a basic one-megapixel camera, that's more than 1,000 photons per pixel. Now in a new study, researchers have developed an ...
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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