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开源软件名称(OpenSource Name):vishwesh5/Quantum-Machine-Learning开源软件地址(OpenSource Url):https://github.com/vishwesh5/Quantum-Machine-Learning开源编程语言(OpenSource Language):Jupyter Notebook 99.4%开源软件介绍(OpenSource Introduction):Quantum Machine LearningThe pace of development in quantum computing mirrors the rapid advances made in machine learning and artificial intelligence. It is natural to ask whether quantum technologies could boost learning algorithms: this field of enquiry is called quantum machine learning. This massively open online online course (MOOC) on edX is offered by the University of Toronto on edX with an emphasis on what benefits current and near-future quantum technologies may bring to machine learning. These notebooks contain the lecture notes and the code for the course. The content is organized in four modules, with an additional introductory module to the course itself. Since the course is hands-on, we found it important that you can try the code on actual quantum computers if you want to. There isn't a single, unified programming framework that would allow to address all available quantum hardware. For this reason, the notebooks are available in two versions: one in Qiskit targeting the IBM Q hardware and the Forest SDK targetting the Rigetti quantum computer. The notebooks also cover quantum annealing -- for that, the D-Wave Ocean Suite is used. For more details on setting up your computational environment locally, refer to the notebooks in Module 0. The code snippets in the notebooks are licensed under the MIT License. The text and figures are licensed under the Creative Commons Attribution 4.0 International Public License (CC-BY-4.0). PrerequisitesPython and a good command of linear algebra are necessary. Experience with machine learning helps. StructureModule 0: Introduction 00_Course_Introduction.ipynb 00_Introduction_to_Qiskit.ipynb 00_Introduction_to_the_Forest_SDK.ipynb Module 1: Quantum Systems 02_Measurements_and_Mixed_States.ipynb 03_Evolution_in_Closed_and_Open_Systems.ipynb 04_Classical_and_Quantum_Many-Body_Physics.ipynb Module 2: Quantum Computation 05_Gate-Model_Quantum_Computing.ipynb 06_Adiabatic_Quantum_Computing.ipynb 07_Variational_Circuits.ipynb 08_Sampling_a_Thermal_State.ipynb Module 3: Classical-quantum hybrid learning algorithms 09_Discrete_Optimization_and_Ensemble_Learning.ipynb 10_Discrete_Optimization_and_Unsupervised_Learning.ipynb 11_Kernel_Methods.ipynb 12_Training_Probabilistic_Graphical_Models.ipynb Module 4: Coherent Learning Protocols 13_Quantum_Phase_Estimation.ipynb 14_Quantum_Matrix_Inversion.ipynb ContributingWe welcome contributions - simply fork the repository, and then make a pull request containing your contribution. We would especially love to see the course extended to other open source quantum computing frameworks. We also encourage bug reports and suggestions for enhancements. Sourcehttps://gitlab.com/qosf/qml-mooc |
2023-10-27
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