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QuTiP, short for the Quantum Toolbox in Python, is an open-source computational physics software library for simulating quantum systems, particularly open quantum systems. [1] [2] QuTiP allows simulation of Hamiltonians with arbitrary time-dependence, allowing simulation of situations of interest in quantum optics, ion trapping, superconducting circuits and quantum nanomechanical resonators.
barn (10^-28 m^2) surface tension: newton per meter (N/m) tau: torque: newton meter (N⋅m) shear stress: pascal time constant: second (s) 6.28318... unitless phi: field strength: unit varies depending on context magnetic flux: weber (Wb) electric potential: volt (V) Higgs field work function
"High school physics textbooks" (PDF). Reports on high school physics. American Institute of Physics; Zitzewitz, Paul W. (2005). Physics: principles and problems. New York: Glencoe/McGraw-Hill. ISBN 978-0078458132
Written mostly in the Python programming language, it enables users to formulate problems in Ising Model and Quadratic Unconstrained Binary Optimization formats (QUBO). Results can be obtained by submitting to an online quantum computer in Leap, D-Wave's real-time Quantum Application Environment, customer-owned machines, or classical samplers.
The Zen of Python is a collection of 19 "guiding principles" for writing computer programs that influence the design of the Python programming language. [1] Python code that aligns with these principles is often referred to as "Pythonic". [2] Software engineer Tim Peters wrote this set of principles and posted it on the Python mailing list in ...
Also simply application or app. Computer software designed to perform a group of coordinated functions, tasks, or activities for the benefit of the user. Common examples of applications include word processors, spreadsheets, accounting applications, web browsers, media players, aeronautical flight simulators, console games, and photo editors. This contrasts with system software, which is ...
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
The following list illustrates the impact of computational complexity on algorithms used in chemical computations. It is important to note that while this list provides key examples, it is not comprehensive and serves as a guide to understanding how computational demands influence the selection of specific computational methods in chemistry.