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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]
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.
Below is a simple example of how the Deutsch–Jozsa algorithm can be implemented in Python using Qiskit, an open-source quantum computing software development framework by IBM. We will walk through each part of the code step by step to show how it translates the theory into a working quantum circuit.
Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment.
Qiskit was founded by IBM Research to allow software development for their cloud quantum computing service, IBM Quantum Experience. [5] [6] Contributions are also made by external supporters, typically from academic institutions. [7] [8] The primary version of Qiskit uses the Python programming language.
For combinatorial optimization, the quantum approximate optimization algorithm (QAOA) [6] briefly had a better approximation ratio than any known polynomial time classical algorithm (for a certain problem), [7] until a more effective classical algorithm was proposed. [8] The relative speed-up of the quantum algorithm is an open research question.
Quantum Trajectory Theory (QTT) is a formulation of quantum mechanics used for simulating open quantum systems, quantum dissipation and single quantum systems. [1] It was developed by Howard Carmichael in the early 1990s around the same time as the similar formulation, known as the quantum jump method or Monte Carlo wave function (MCWF) method, developed by Dalibard, Castin and Mølmer. [2]
An implementation of the quantum algorithm for linear systems of equations was first demonstrated in 2013 by three independent publications. [3] [4] [5] The demonstrations consisted of simple linear equations on specially designed quantum devices. [3] [4] [5] The first demonstration of a general-purpose version of the algorithm appeared in 2018 ...