77 skills found · Page 1 of 3
mxgmn / WaveFunctionCollapseBitmap & tilemap generation from a single example with the help of ideas from quantum mechanics
amazon-braket / Amazon Braket ExamplesExample notebooks that show how to apply quantum computing with Amazon Braket.
Quantinuum / Pytket DocsUser manual and example notebooks for the pytket quantum computing toolkit
rdisipio / QlstmExample of a Quantum LSTM
mrtkp9993 / QuantumComputingExamplesQuantum computing examples with QISKit.
nguyen-group / QE SSPThis repository contains example codes for the book: Quantum ESPRESSO Course for Solid‑State Physics, Jenny Stanford Publishing, New York, 372 Pages (2022) by N. T. Hung, A. R. T. Nugraha and R. Saito.
tsotchke / Quantum RngSemi-Classical Quantum Random Number Generator library written in C for cryptographic, simulation and generative AI applications with examples
Perfecto-Quantum / Quantum Starter KitGet started with Quantum! Clone or download this repository to start, contains examples of tests and step definitions.
caidish / Quantum Optics With PythonThis is a repository for notes on Quantum Optics. The examples are implemented by QuTip using Python.
mathworks / Quantum Computing MATLABMATLAB examples, functions and otherwise helpful material using the MATLAB Support Package for Quantum Computing
givgramacho / CERN Quantum Computing CourseQuantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.
qojulia / QuantumOptics.jl ExamplesExamples for QuantumOptics.jl
JordanovSJ / VQEA python module and example scripts to perform molecular simulationts with the variational quantum eigensolver (VQE)
Slimane33 / QuantumClassifierAn example of a variational quantum classifier implemented with qiskit using only elementary gates.
NuclearPhysicsWorkshops / FRIB TASummerSchoolQuantumComputingRecent developments in quantum information systems and technologies offer the possibility to address some of the most challenging large-scale problems in science, whether they are represented by complicated interacting quantum mechanical systems or classical systems. The last years have seen a rapid and exciting development in algorithms and quantum hardware. The emphasis of this summer school is to highlight, through a series of lectures and hands-on exercises and practice sessions, how quantum computing algorithms can be used to study nuclear few- and many-body problems of relevance for low-energy nuclear physics. And how quantum computing algorithms can aid in studying systems with increasingly many more degrees of freedom compared with more classical few- and many-body methods. Several quantum algorithms for solving quantum-mechanical few- and many-particle problems with be discussed. The lectures will start with the basic ideas of quantum computing. Thereafter, through examples from nuclear physics, we will elucidate how different quantum algorithms can be used to study these systems. The results from various quantum computing algorithms will be compared to standard methods like full configuration interaction theory, field theories on the lattice, in-medium similarity renormalization group and coupled cluster theories.
gt-tinker / QwertyCompiler, runtime, and examples for the Qwerty quantum programming language
kaelynj / Qiskit HubbardModelCode containing notes for the derivation of mapping a Hubbard system to a quantum computing environment and an example of time evolution of a 1d chain containing 3 sites.
jochym / Qe DocDocs and examples for Quantum-Espresso
mortele / Variational Monte Carlo Fys4411Example class structure for use in FYS4411: Quantum mechanical systems at UiO.
YilingQiao / DiffquantumExample code for NeurIPS 2022 paper "Differentiable Analog Quantum Computing for Learning and Control"
