Package: paddle-quantum
English |简体中文Paddle Quantum (量桨)FeaturesInstallInstall PaddlePaddleInstall Paddle QuantumEnvironment setup for Quantum Chemistry moduleRun ExampleIntroduction and developmentsQuick startTutorialsAPI documentationFeedbacksResearch with Paddle QuantumFrequently Asked QuestionsCopyright and LicenseReferencesPaddle Quantum (量桨)is the world's first cloud-integrated quantum machine learning platform based on Baidu PaddlePaddle. It supports the building and training of quantum neural networks, making PaddlePaddle the first deep learning framework in China. Paddle Quantum is feature-rich and easy to use. It provides comprehensive API documentation and tutorials help users get started right away.Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing (QC). It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts.FeaturesEasy-to-useMany online learning resources (Nearly 50 tutorials)High efficiency in building QNN with various QNN templatesAutomatic differentiationVersatileMultiple optimization tools and GPU modeSimulation with 25+ qubitsFlexible noise modelsFeatured ToolkitsToolboxes for Chemistry & OptimizationLOCCNet for distributed quantum information processingSelf-developed QML algorithmsInstallInstall PaddlePaddleThis dependency will be automatically satisfied when users install Paddle Quantum. Please refer toPaddlePaddle's official installation and configuration page. This project requires PaddlePaddle 2.2.0 to 2.3.0.Install Paddle QuantumWe recommend the following way of installing Paddle Quantum withpip,pipinstallpaddle-quantumor download all the files and finish the installation locally,gitclonehttps://github.com/PaddlePaddle/quantumcdquantum
pipinstall-e.Environment setup for Quantum Chemistry moduleCurrently, ourqchemmodule usesPySCFas its backend to compute molecular integrals, so before executing quantum chemistry, we have to install this Python package.It is recommended thatPySCFis installed in a Python environment whose Python version >=3.6.We highly recommend you to installPySCFvia conda.MacOS/Linuxuser can use the command:condainstall-cpyscfpyscfNOTE: ForWindowsuser, if your operating system is Windows10, you can installPySCFin Ubuntu subsystem provided by Windows 10's App Store.PySCFcan't run directly in Windows, so we are working hard to develop more quantum chemistry backends. Our support for Windows will be improved in the coming release of Paddle Quantum.Note:Please refer toPySCFfor more download options.Run exampleNow, you can try to run a program to verify whether Paddle Quantum has been installed successfully. Here we take quantum approximate optimization algorithm (QAOA) as an example.cdpaddle_quantum/QAOA/example
pythonmain.pyFor the introduction of QAOA, please refer to ourQAOA tutorial.Breaking ChangeIn version 2.2.0 of Paddle Quantum, we have made an incompatible upgrade to the code architecture, and the new version's structure and usage can be found in ourtutorials,API documentation, andthe source code. Also, we support connecting to a real quantum computer viaQuLeaf, usingpaddle_quantum.set_backend('quleaf')to select QuLeaf as the backend.Introduction and developmentsQuick startPaddle Quantum Quick Start Manualis probably the best place to get started with Paddle Quantum. Currently, we support online reading and running the Jupyter Notebook locally. The manual includes the following contents:Detailed installation tutorials for Paddle QuantumIntroduction to quantum computing and quantum neural networks (QNNs)Introduction to Variational Quantum Algorithms (VQAs)Introduction to Paddle QuantumPaddlePaddle optimizer tutorialIntroduction to the quantum chemistry module in Paddle QuantumHow to train QNN with GPUTutorialsWe provide tutorials covering quantum simulation, machine learning, combinatorial optimization, local operations and classical communication (LOCC), and other popular QML research topics. Each tutorial currently supports reading on our website and running Jupyter Notebooks locally. For interested developers, we recommend them to download Jupyter Notebooks and play around with it. Here is the tutorial list,Quantum SimulationBuilding Molecular HamiltonianVariational Quantum Eigensolver (VQE)Subspace Search-Quantum Variational Quantum Eigensolver (SSVQE)Variational Quantum State Diagonalization (VQSD)Gibbs State PreparationThe Classical Shadow of Unknown Quantum StatesEstimation of Quantum State Properties Based on the Classical ShadowHamiltonian Simulation with Product FormulaSimulate the Spin Dynamics on a Heisenberg ChainDistributed Variational Quantum Eigensolver Based on Schmidt DecompositionQuantum Signal Processing and Quantum Singular Value TransformationHamiltonian Simulation with qDRIFTQuantum Phase ProcessingVariational Quantum MetrologyMachine LearningEncoding Classical Data into Quantum StatesQuantum ClassifierVariational Shadow Quantum Learning (VSQL)Quantum Kernel MethodsQuantum AutoencoderQuantum GANVariational Quantum Singular Value Decomposition (VQSVD)Data Encoding AnalysisQuantum Neural Network Approximating FunctionsVariational quantum amplitude estimationCombinatorial OptimizationQuantum Approximation Optimization Algorithm (QAOA)Solving Max-Cut Problem with QAOALarge-scale QAOA via Divide-and-ConquerTravelling Salesman ProblemQuantum Finance Application on Arbitrage Opportunity OptimizationQuantum Finance Application on Portfolio OptimizationQuantum Finance Application on Portfolio DiversificationLOCC with QNN (LOCCNet)Local Operations and Classical Communication in QNN (LOCCNet)Entanglement Distillation -- the BBPSSW protocolEntanglement Distillation -- the DEJMPS protocolEntanglement Distillation -- Protocol design with LOCCNetQuantum TeleportationQuantum State DiscriminationQNN ResearchThe Barren Plateaus Phenomenon on Quantum Neural Networks (Barren Plateaus)Noise Model and Quantum ChannelExpressibility of Quantum Neural NetworkVariational Quantum Circuit CompilingQuantum Fisher InformationVAns-Variable AnsatzWith the latest LOCCNet module, Paddle Quantum can efficiently simulate distributed quantum information processing tasks. Interested readers can start with thistutorial on LOCCNet. In addition, Paddle Quantum supports QNN training on GPU. For users who want to get into more details, please check out the tutorialUse Paddle Quantum on GPU. Moreover, Paddle Quantum could design robust quantum algorithms under noise. For more information, please seeNoise tutorial.In a recent update, the measurement-based quantum computation (MBQC) module has been added to Paddle Quantum. Unlike the conventional quantum circuit model, MBQC has its unique way of computing. Interested readers are welcomed to read ourtutorialson how to use the MBQC module and its use cases.API documentationFor those who are looking for explanation on the python class and functions provided in Paddle Quantum, we refer to ourAPI documentation page.We, in particular, denote that the current docstring specified in source codeis written in simplified Chinese, this will be updated in later versions.FeedbacksUsers are encouraged to contact us throughGitHub Issuesor emailquantum@baidu.comwith general questions, unfixed bugs, and potential improvements. We hope to make Paddle Quantum better together with the community!Research based on Paddle QuantumWe also highly encourage developers to use Paddle Quantum as a research tool to develop novel QML algorithms. If your work uses Paddle Quantum, feel free to send us a notice viaqml@baidu.com. We are always excited to hear that! Cite us with the following BibTeX:@misc{Paddlequantum,
title = {{Paddle Quantum}},
year = {2020},
url = {https://github.com/PaddlePaddle/Quantum}, }So far, we have done several projects with the help of Paddle Quantum as a powerful QML development platform.[1] Wang, Youle, Guangxi Li, and Xin Wang. "Variational quantum Gibbs state preparation with a truncated Taylor series." Physical Review Applied 16.5 (2021): 054035. [pdf][2] Wang, Xin, Zhixin Song, and Youle Wang. "Variational quantum singular value decomposition." Quantum 5 (2021): 483. [pdf][3] Li, Guangxi, Zhixin Song, and Xin Wang. "VSQL: Variational Shadow Quantum Learning for Classification." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 9. 2021.[pdf][4] Chen, Ranyiliu, et al. "Variational quantum algorithms for trace distance and fidelity estimation." Quantum Science and Technology (2021).[pdf][5] Wang, Kun, et al. "Detecting and quantifying entanglement on near-term quantum devices." arXiv preprint arXiv:2012.14311 (2020).[pdf][6] Zhao, Xuanqiang, et al. "Practical distributed quantum information processing with LOCCNet." npj Quantum Information 7.1 (2021): 1-7.[pdf][7] Cao, Chenfeng, and Xin Wang. "Noise-Assisted Quantum Autoencoder." Physical Review Applied 15.5 (2021): 054012.[pdf]Frequently Asked QuestionsQuestion:What is quantum machine learning? What are the applications?Answer:Quantum machine learning (QML) is an interdisciplinary subject that combines quantum computing (QC) and machine learning (ML). On the one hand, QML utilizes existing artificial intelligence technology to break through the bottleneck of quantum computing research. On the other hand, QML uses the information processing advantages of quantum computing to promote the development of traditional artificial intelligence. QML is not only suitable for quantum chemical simulations (with Variational Quantum Eigensolver) and other quantum problems. It also help researchers to solve classical optimization problems including knapsack problem, traveling salesman problem, and Max-Cut problem through the Quantum Approximate Optimization Algorithm.Question:I want to study QML, but I don't know much about quantum computing. Where should I start?Answer:Quantum Computation and Quantum Informationby Nielsen & Chuang is the classic introductory textbook to QC. We recommend readers to study Chapter 1, 2, and 4 of this book first. These chapters introduce the basic concepts, provide solid mathematical and physical foundations, and discuss the quantum circuit model widely used in QC. Readers can also go through Paddle Quantum's quick start guide, which contains a brief introduction to QC and interactive examples. After building a general understanding of QC, readers can try some cutting-edge QML applications provided as tutorials in Paddle Quantum.Question:Currently, there is no fault-tolerant large-scale quantum hardware. How can we develop quantum applications?Answer:The development of useful algorithms does not necessarily require a perfect hardware. The latter is more of an engineering problem. With Paddle Quantum, one can develop, simulate, and verify the validity of self-innovated quantum algorithms. Then, researchers can choose to implement these tested quantum algorithms in a small scale hardware and see the actual performance of it. Following this line of reasoning, we can prepare ourselves with many candidates of useful quantum algorithms before the age of matured quantum hardware.Question:What are the advantages of Paddle Quantum?Answer:Paddle Quantum is an open-source QML toolkit based on Baidu PaddlePaddle. As the first open-source and industrial level deep learning platform in China, PaddlePaddle has the leading ML technology and rich functionality. With the support of PaddlePaddle, especially its dynamic computational graph mechanism, Paddle Quantum could easily train a QNN and with GPU acceleration. In addition, based on the high-performance quantum simulator developed by Institute for Quantum Computing (IQC) at Baidu, Paddle Quantum can simulate more than 20 qubits on personal laptops. Finally, Paddle Quantum provides many open-source QML tutorials for readers from different backgrounds.Copyright and LicensePaddle Quantum usesApache-2.0 license.References[1]Quantum Computing - Wikipedia[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (2010).[3] Phillip Kaye, Laflamme, R. & Mosca, M. An Introduction to Quantum Computing. (2007).[4]Biamonte, J. et al. Quantum machine learning. Nature 549, 195–202 (2017).[5]Schuld, M., Sinayskiy, I. & Petruccione, F. An introduction to quantum machine learning. Contemp. Phys. 56, 172–185 (2015).[6]Benedetti, M., Lloyd, E., Sack, S. & Fiorentini, M. Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4, 043001 (2019).