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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. [ 1 ] [ 2 ] 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 ...
With quantum processor based systems, quantum programming languages help express quantum algorithms using high-level constructs. [1] The field is deeply rooted in the open-source philosophy and as a result most of the quantum software discussed in this article is freely available as open-source software .
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.
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In the context of quantum computing, the quantum walk search is a quantum algorithm for finding a marked node in a graph. [ 1 ] The concept of a quantum walk is inspired by classical random walks , in which a walker moves randomly through a graph or lattice .
Quil is being developed for the superconducting quantum processors developed by Rigetti Computing through the Forest quantum programming API. [5] [6] A Python library called pyQuil was introduced to develop Quil programs with higher level constructs. A Quil backend is also supported by other quantum programming environments. [7] [8]
In quantum computing, Grover's algorithm, also known as the quantum search algorithm, is a quantum algorithm for unstructured search that finds with high probability the unique input to a black box function that produces a particular output value, using just () evaluations of the function, where is the size of the function's domain.
Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. [1] Mathematical optimization deals with finding the best solution to a problem (according to some criteria) from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize ...