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Back to qiskit/qiskit-tutorials

Open-source alternatives to Qiskit Tutorials

20 open-source projects similar to qiskit/qiskit-tutorials, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Qiskit Tutorials alternative.

  • qiskit/qiskitQiskit avatar

    Qiskit/qiskit

    7,522View on GitHub↗

    Qiskit is a quantum computing software development kit used for designing, simulating, and executing quantum circuits on physical hardware and simulators. It functions as a quantum algorithm framework, a circuit simulator, and a vendor-agnostic hardware interface for dispatching workloads across diverse providers. The project features a quantum circuit transpiler that optimizes abstract designs to match the specific basis gates and qubit connectivity of target hardware. It employs a pass-based transpilation pipeline and symbolic instruction translation to convert high-level circuits into hard

    Pythonpythonqiskitquantum
    View on GitHub↗7,522
  • quantumlib/cirqquantumlib avatar

    quantumlib/Cirq

    4,990View on GitHub↗

    Cirq is a Python quantum computing framework used for designing, simulating, and executing quantum circuits on Noisy Intermediate-Scale Quantum (NISQ) hardware. It serves as a quantum circuit simulator and noise modeler, as well as a tool for the implementation of quantum algorithms. The framework provides a specialized interface for NISQ hardware, allowing users to map logical quantum circuits to physical device topologies while validating hardware connectivity and gate constraints. It distinguishes itself through integrated noise modeling, applying depolarizing and damping channels to mimic

    Pythonalgorithmsapicirq
    View on GitHub↗4,990
  • microsoft/quantumkatasMicrosoft avatar

    Microsoft/QuantumKatas

    4,879View on GitHub↗

    QuantumKatas is a set of quantum computing courseware and educational assets designed to teach the Q# programming language and quantum computing principles. It combines structured tutorials and coding tasks with interactive notebooks and a dedicated unit testing suite to validate the correctness of exercise implementations. The project provides a dockerized learning environment that packages all necessary tools and dependencies into a virtual image. This allows for the execution of quantum programming exercises without the need for local software installation. The curriculum covers qubit man

    Jupyter Notebook
    View on GitHub↗4,879

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  • ageron/handson-mlageron avatar

    ageron/handson-ml

    25,608View on GitHub↗

    This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

    Jupyter Notebook
    View on GitHub↗25,608
  • leandromoreira/digital_video_introductionleandromoreira avatar

    leandromoreira/digital_video_introduction

    16,232View on GitHub↗

    This project is an educational suite and technical guide designed for mastering video codecs and signal processing. It provides a structured curriculum through an engineering course, interactive labs, and tutorials focused on the fundamental principles of video compression and digital signal processing. The resource includes a technical guide for analyzing specific codecs like AV1, VP9, and H.265. It distinguishes itself by providing a containerized media lab, which ensures a consistent development environment for experimenting with video technology tools and notebooks. The project covers a

    Jupyter Notebookadaptive-streamingarithmetic-codingaudio
    View on GitHub↗16,232
  • atcold/nyu-dlsp20Atcold avatar

    Atcold/NYU-DLSP20

    6,809View on GitHub↗

    NYU-DLSP20 is a self-paced deep learning course repository that provides a complete educational curriculum covering supervised and unsupervised deep learning fundamentals. The course materials include lecture slides, Jupyter notebooks, and YouTube video recordings, all organized around PyTorch-based code exercises and neural network architecture tutorials. The course is structured as a sequential progression from fundamentals to advanced architectures, with each lecture building on previous material. Assignments are distributed as Jupyter notebooks that students complete and submit, ensuring

    Jupyter Notebookdeep-learningjupyter-notebookneural-nets
    View on GitHub↗6,809
  • chipsalliance/chiselchipsalliance avatar

    chipsalliance/chisel

    4,691View on GitHub↗

    Chisel is a hardware construction language and description tool used to define digital circuits. It functions as a generator that converts high-level hardware descriptions into synthesizable Verilog code for use in ASIC and FPGA design. The project enables the creation of parameterizable hardware templates and reusable digital components. It leverages functional and object-oriented programming patterns to transform complex circuit representations into finalized hardware descriptions. The toolset covers the register-transfer level design workflow, allowing users to model digital circuits usin

    Scalachip-generatorchiselchisel3
    View on GitHub↗4,691
  • pierian-data/complete-python-3-bootcampPierian-Data avatar

    Pierian-Data/Complete-Python-3-Bootcamp

    29,604View on GitHub↗

    This project is a beginner coding bootcamp and Python programming curriculum. It provides a structured set of educational materials and exercise files designed to guide students through the Python language from basic to advanced levels. The curriculum is delivered as Jupyter Notebook courseware, combining live code execution with explanatory text for technical demonstrations. It also functions as a project repository, offering a collection of milestone coding exercises and source files for practicing software development and core syntax. The materials are organized into sequential modules an

    Jupyter Notebook
    View on GitHub↗29,604
  • probml/pyprobmlprobml avatar

    probml/pyprobml

    7,096View on GitHub↗

    pyprobml is a collection of notebook-based implementations of probabilistic machine learning models and algorithms. It uses scientific computing and data analysis libraries to execute mathematical concepts and theories for practical application and research. The project focuses on the programmatic generation of scientific figures and visualizations to recreate results from a technical text. It employs a system of branch-based asset storage to isolate these generated images from the source code. The repository covers a wide range of probabilistic modeling and machine learning tasks, including

    Jupyter Notebookblackjaxcolabflax
    View on GitHub↗7,096
  • quipnetwork/xq-pyQuipNetwork avatar

    QuipNetwork/xq-py

    5,546View on GitHub↗

    xq-py is a numerical quantum computing library and software emulator used to execute quantum algorithms. It functions as a quantum virtual machine that simulates quantum circuits and state vectors through the use of linear algebra and complex number arrays. The project provides a virtual environment for developing and verifying quantum logic. It models multi-qubit systems by utilizing tensor-product expansion and unitary gate applications to simulate quantum state vectors and calculate probabilistic state collapse. The simulation is supported by a numerical backend that handles the matrix-ba

    View on GitHub↗5,546
  • selfteaching/the-craft-of-selfteachingselfteaching avatar

    selfteaching/the-craft-of-selfteaching

    15,923View on GitHub↗

    This project is a framework and curriculum for self-directed learning, providing a structured methodology for mastering complex technical skills without formal instruction. It combines educational content with a technical study methodology centered on deliberate practice and the psychological habits required for independent mastery. The project is distinguished by its use of interactive notebooks and markdown documentation to deliver a sequenced learning path. It integrates test-driven development patterns into the educational process to provide automated feedback and resolve cognitive barrie

    Jupyter Notebook
    View on GitHub↗15,923
  • facebook/redexfacebook avatar

    facebook/redex

    6,292View on GitHub↗

    A bytecode optimizer for Android apps

    C++
    View on GitHub↗6,292
  • aishwaryanr/awesome-generative-ai-guideaishwaryanr avatar

    aishwaryanr/awesome-generative-ai-guide

    24,755View on GitHub↗

    This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications. The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retri

    HTMLawesomeawesome-listgenerative-ai
    View on GitHub↗24,755
  • developer-y/cs-video-coursesDeveloper-Y avatar

    Developer-Y/cs-video-courses

    81,816View on GitHub↗

    This project is a community-driven educational repository that serves as a comprehensive directory of university-level computer science video lectures. It provides a structured learning path for students and professionals, aggregating high-quality academic resources to facilitate self-paced study across a wide range of technical disciplines. The repository distinguishes itself through a collaborative maintenance model, utilizing version control workflows to allow contributors to expand and update the collection. Content is organized within a single, version-controlled document that leverages

    algorithmsbioinformaticscomputational-biology
    View on GitHub↗81,816
  • yosyshq/yosysYosysHQ avatar

    YosysHQ/yosys

    4,543View on GitHub↗

    Yosys is a digital logic synthesizer and RTL synthesis tool that transforms hardware description language code into optimized gate-level representations. It functions as a hardware logic optimizer and formal verification tool, providing a framework to convert generic circuit representations into specific technology library cells for targeted hardware platforms. The project serves as a hardware design automator through a scriptable environment used to execute sequences of synthesis commands. This automation allows for the management of complex digital hardware workflows, integrating RTL design

    C++
    View on GitHub↗4,543
  • atopile/atopileatopile avatar

    atopile/atopile

    3,070View on GitHub↗

    atopile is an electronic design automation software and hardware description language that enables the definition of electronic circuits and components as code. It functions as a modular electronics framework and a compiler that transforms programmatic circuit definitions into a normalized model and KiCad project files for physical layout. The system distinguishes itself through automated hardware design automation, using a constraint-based value solver to calculate required component values and validate electrical specifications during the build process. It employs a modular approach to hard

    Pythoncadedaelectronics
    View on GitHub↗3,070
  • boto/boto3boto avatar

    boto/boto3

    9,834View on GitHub↗

    Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and contain

    Pythonawsaws-sdkcloud
    View on GitHub↗9,834
  • vinta/awesome-pythonvinta avatar

    vinta/awesome-python

    303,207View on GitHub↗

    This project is a comprehensive, community-curated directory that organizes a vast landscape of Python software libraries, frameworks, and tools. It serves as a centralized knowledge base designed to facilitate ecosystem navigation and accelerate developer discovery across the entire software development lifecycle. The directory distinguishes itself by providing a structured index of resources categorized by technical domain, ranging from foundational development utilities to specialized engineering fields. It covers high-level capabilities including artificial intelligence, data science, web

    Pythonawesomecollectionspython
    View on GitHub↗303,207
  • aws/aws-cdkaws avatar

    aws/aws-cdk

    12,817View on GitHub↗

    The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

    TypeScriptawscloud-infrastructurehacktoberfest
    View on GitHub↗12,817
  • dusty-nv/jetson-inferencedusty-nv avatar

    dusty-nv/jetson-inference

    8,734View on GitHub↗

    jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

    C++caffecomputer-visiondeep-learning
    View on GitHub↗8,734