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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.
Qiskit is made of elements that work together to enable quantum computing. The central goal of Qiskit is to build a software stack that makes it easier for anyone to use quantum computers, regardless of their skill level or area of interest; Qiskit allows users to design experiments and applications and run them on real quantum computers and/or classical simulators.
Quantum chemistry computer programs are used in computational chemistry to implement the methods of quantum chemistry. Most include the Hartree–Fock (HF) and some post-Hartree–Fock methods. They may also include density functional theory (DFT), molecular mechanics or semi-empirical quantum chemistry methods.
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.
Hamiltonian simulation is a problem that demands algorithms which implement the evolution of a quantum state efficiently. The Hamiltonian simulation problem was proposed by Richard Feynman in 1982, where he proposed a quantum computer as a possible solution since the simulation of general Hamiltonians seem to grow exponentially with respect to ...
An open-source Python library developed by Xanadu Quantum Technologies for designing, simulating, and optimizing continuous variable (CV) quantum optical circuits. [ 39 ] [ 40 ] Three simulators are provided - one in the Fock basis , one using the Gaussian formulation of quantum optics, [ 41 ] and one using the TensorFlow machine learning library.
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]
Quantum Monte Carlo is a way to directly study the many-body problem and the many-body wave function beyond these approximations. The most advanced quantum Monte Carlo approaches provide an exact solution to the many-body problem for non-frustrated interacting boson systems, while providing an approximate description of interacting fermion systems.