{"id":17919301,"url":"https://github.com/lynnlangit/learning-quantum","last_synced_at":"2026-02-15T03:32:56.040Z","repository":{"id":42457188,"uuid":"337429918","full_name":"lynnlangit/learning-quantum","owner":"lynnlangit","description":"Study resources for learning quantum computing","archived":false,"fork":false,"pushed_at":"2025-10-21T18:07:36.000Z","size":56449,"stargazers_count":181,"open_issues_count":0,"forks_count":57,"subscribers_count":11,"default_branch":"main","last_synced_at":"2026-01-26T12:57:50.427Z","etag":null,"topics":["amazon-braket","cirq","openqasm","qiskit","quantum-computing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lynnlangit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-02-09T14:24:34.000Z","updated_at":"2026-01-25T14:58:33.000Z","dependencies_parsed_at":"2025-03-18T16:40:49.977Z","dependency_job_id":null,"html_url":"https://github.com/lynnlangit/learning-quantum","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/lynnlangit/learning-quantum","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lynnlangit%2Flearning-quantum","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lynnlangit%2Flearning-quantum/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lynnlangit%2Flearning-quantum/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lynnlangit%2Flearning-quantum/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lynnlangit","download_url":"https://codeload.github.com/lynnlangit/learning-quantum/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lynnlangit%2Flearning-quantum/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29466929,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-15T01:01:38.065Z","status":"online","status_checked_at":"2026-02-15T02:00:07.449Z","response_time":118,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["amazon-braket","cirq","openqasm","qiskit","quantum-computing"],"created_at":"2024-10-28T20:15:56.346Z","updated_at":"2026-02-15T03:32:56.034Z","avatar_url":"https://github.com/lynnlangit.png","language":"Jupyter Notebook","readme":"# Learning Cloud Quantum Programming\n\n\u003cimg src=\"https://github.com/lynnlangit/learning-quantum/blob/main/images/bit-vs-qubit.png\" width=375 align=left\u003e\n\nThis repo contains my study resources for learning **cloud quantum programming**.    \n\nShown to the left is a conceptual rendering of a bit vs a qubit, which is a fundamental concept of work in quantum computing.  The Repo is a companion to my LI_L course [\"Cloud Quantum Computing Essentials\"](https://www.linkedin.com/learning/cloud-quantum-computing-essentials)\n\nA **qubit** is a two-state (or two-level) quantum-mechanical system, one of the simplest quantum systems displaying the peculiarity of quantum mechanics. A quantum computer performs quantum computations using the principles of quantum mechanics.   \n\nA **QPU** (quantum processing units) manipulates the quantum states of available qubits in a controlled way to perform computations, such as algorithms. A qubit is a quantum bit of information.  \n\nA **quantum computer** contains QPU processors, some number of qubits and the support mechanisms which allow these items to interact based on quantum instructions or programs.\n\n----\n\n## Repository Structure\n\nThis Repo is organized by folder as follows:\n\n### 📚 concepts\n[Conceptual information](https://github.com/lynnlangit/learning-quantum/tree/main/1_concepts) about quantum languages, libraries, operations, reference programs (Shor's, Grover's, etc...) and notation\n\n### ☁️ cloud-vendors\nInfo about quantum runtime environments (and simulators) organized by [cloud vendor](https://github.com/lynnlangit/learning-quantum/tree/main/2_cloud-vendors):\n- **AWS Braket** - Multi-vendor platform with Ocelot chip, Quantum Embark program\n- **Microsoft Azure Quantum** - Logical qubits, Majorana 1, integrated AI+HPC platform\n- **IBM Quantum** - Utility-scale computing, 156-qubit Heron, path to 2029 fault tolerance\n- **Google Quantum AI** - Willow chip with error correction breakthrough\n- **IonQ** - Trapped-ion systems, 99.9% fidelity, path to 2M qubits\n- **Rigetti** - Superconducting multi-chip architecture\n\n### 📄 whitepapers\n[Academic research papers](https://github.com/lynnlangit/learning-quantum/tree/main/3_whitepapers) of interest including quantum programming algorithms and examples\n\n### 📖 o-reilly-book\nCode examples, slides and link from a [15-week-long bookclub](https://github.com/lynnlangit/learning-quantum/tree/main/4_oreilly-book) covering the referenced book on quantum programming\n\n---\n\n\n## Quantum Computer Example\n\n\u003cimg src=\"https://github.com/lynnlangit/learning-quantum/blob/main/images/d-wave-hardware.png\" width=600 align=right\u003e\n\nThere are a number of quantum computer vendors.  These vendors produce hardware (quantum computers) which contains a particular number of qubits and QPUs.   \n\nOne example is the [D-Wave](https://www.dwavesys.com/) company.  Shown to the right are photos from one of D-Wave's quantum computers.  This computer contains QPU units, which is hardware with qubits (image taken from D-Wave whitepaper). To run quantum programs on quantum hardware, use quantum languages or libraries.    \n\nNOTE: Generally quantum programs are run on quantum simulators prior to being run on quantum hardware due to the cost and time run on live QPUs.\n\n\n---\n\n## Quantum Programs and IDEs\n\nShown below are screenshots from a couple of quantum programming development environments.  This is just a small subset of the available options.  Generally these IDEs are either cloud-based (IBM Composer) or downloadable via a SDK (D-Wave).  \n\n- The first example (shown below) shows running a quantum program in the IBM Quantum Composer IDE.  This example runs the `Grover-example` quantum program. The visual environment includes the composer, which shows quantum operations written in the OPENQASM quantum programming language and a number of other visualization tools. \n\u003cimg src=\"https://github.com/lynnlangit/learning-quantum/blob/main/images/grover.png\" width=800\u003e\n\n- The second example (shown below) is from from D-Wave Systems cloud at https://cloud.dwavesys.com/ and is being run using VSCode as an IDE.  The sample shows a path optimization solver and is called `path` in the D-Wave examples. The program is written using the D-Wave Python-like quantum programming library. This IDE is a more traditional environment and doesn't include as many visualization tools for the state of the qubits used in computation.\n\n\u003cimg src=\"https://github.com/lynnlangit/learning-quantum/blob/main/images/dwave-ide.png\" width=800\u003e\n\n## Resources for Learning\n\n- Yet another example of a quantum program visualization tools is the browser-based `Quantum Playground` - http://www.quantumplayground.net/.  Shown below is an example of animated output using the H gate example code.  This is a particularly good tool for gaining an intuition into key quantum operations and program examples.\n\n\u003cimg src=\"https://github.com/lynnlangit/learning-quantum/blob/main/images/quantum-playground.png\" width=800\u003e\n\n- The QuanTime website (partnership with National Q-12 Education Partnership group) aggregates resources and links to materials which are designed to be used by educators - https://q12education.org/quantime\n\n---\n\n## 2024-2025 Industry Breakthroughs\n\nThe quantum computing industry achieved major milestones in 2024-2025, marking the transition from research to practical engineering:\n\n### Error Correction Threshold Achieved\n- **Google Willow**: First demonstration of exponential error reduction as qubits scale\n- **Microsoft**: 800x error rate improvement, created 24 entangled logical qubits (world record)\n- **IBM**: Achieved 5,000-gate circuit execution on 156-qubit system\n\n### Proprietary Hardware Innovation\n- **AWS Ocelot**: First AWS quantum chip using cat qubits (90% error correction cost reduction)\n- **Microsoft Majorana 1**: World's first topological quantum processor, designed to scale to 1 million qubits on single chip\n\n### Enterprise Readiness\n- All major cloud providers launched quantum-ready programs (AWS Quantum Embark, Microsoft Quantum Ready, IBM advisory services)\n- Focus on hybrid quantum-classical computing integrating AI and HPC\n- Industry consensus: Fault-tolerant quantum computing by 2028-2030\n\n### Current State (2025)\nModern cloud quantum systems range from 50-156 physical qubits with several platforms now demonstrating logical qubits with error correction. Key systems include:\n- **Google Willow**: 105 qubits with error correction breakthrough\n- **IBM Heron**: 156 qubits running 5,000-gate circuits\n- **Microsoft + Atom Computing**: 24 logical qubits\n- **AWS Ocelot**: 9 cat qubits (proprietary)\n- **IonQ Forte Enterprise**: 36 algorithmic qubits with 99.9% fidelity\n\n\n## Quantum Computer Vendors\n\nThere are a number of quantum computer vendors. These vendors produce hardware (quantum computers) which contains a particular number of qubits and QPUs.\n\nOne example is the [D-Wave](https://www.dwavesys.com/) company. Shown to the right are photos from one of D-Wave's quantum computers. This computer contains QPU units, which is hardware with qubits (image taken from D-Wave whitepaper). \n\nTo run quantum programs on quantum hardware, use quantum languages or libraries.\n\n**NOTE:** Generally quantum programs are run on quantum simulators prior to being run on quantum hardware due to the cost and time run on live QPUs.\n\n## Development Environments\n\nShown below are screenshots from a couple of quantum programming development environments. This is just a small subset of the available options. Generally these IDEs are either cloud-based (IBM Composer) or downloadable via a SDK (D-Wave).\n\n### IBM Quantum Composer\nThe first example (shown below) shows running a quantum program in the IBM Quantum Composer IDE. This example runs the Grover-example quantum program. The visual environment includes the composer, which shows quantum operations written in the OPENQASM quantum programming language and a number of other visualization tools.\n\n### D-Wave Cloud\nThe second example (shown below) is from from D-Wave Systems cloud at [https://cloud.dwavesys.com/](https://cloud.dwavesys.com/) and is being run using VSCode as an IDE. The sample shows a path optimization solver and is called `path` in the D-Wave examples. The program is written using the D-Wave Python-like quantum programming library. This IDE is a more traditional environment and doesn't include as many visualization tools for the state of the qubits used in computation.\n\n### Quantum Playground\nYet another example of a quantum program visualization tools is the browser-based Quantum Playground - [http://www.quantumplayground.net/](http://www.quantumplayground.net/). Shown below is an example of animated output using the H gate example code. This is a particularly good tool for gaining an intuition into key quantum operations and program examples.\n\n### Educational Resources\nThe QuanTime website (partnership with National Q-12 Education Partnership group) aggregates resources and links to materials which are designed to be used by educators - [https://q12education.org/quantime](https://q12education.org/quantime)\n\n## Getting Started with Cloud Quantum Computing\n\n### Choose Your Platform\n\n**For Hardware Diversity**: AWS Braket (access to IonQ, Rigetti, IQM, D-Wave, and more)\u003cbr\u003e\n**For Logical Qubits**: Microsoft Azure Quantum (24 entangled logical qubits, topological qubits)\u003cbr\u003e\n**For Utility-Scale Computing**: IBM Quantum (5,000-gate circuits, 156 qubits)\u003cbr\u003e\n**For Open Source**: IBM Quantum (Qiskit framework)\u003cbr\u003e\n**For Highest Fidelity**: IonQ via AWS/Azure (99.9% two-qubit gate fidelity)\n\n### Learning Paths\n\n1. **Courses**:\n   - [LinkedIn Learning: Cloud Quantum Computing Essentials](https://www.linkedin.com/learning/cloud-quantum-computing-essentials)\n   - IBM Qiskit Textbook\n   - AWS Braket Digital Learning Plan (free credentials)\n   - Microsoft Learn: Quantum Computing Fundamentals\n\n2. **Hands-On Practice**:\n   - IBM Quantum (free access to quantum computers)\n   - AWS Braket (free simulator time)\n   - Azure Quantum ($500 free credits)\n   - Annual quantum coding challenges\n\n3. **Community**:\n   - Quantum Computing Stack Exchange\n   - IBM Quantum Network\n   - Cloud provider quantum communities\n\n## Industry Timeline\n\nBased on 2024-2025 announcements, the industry is converging on this timeline:\n\n| Period | Expected Progress |\n|--------|------------------|\n| **2025** | 100-500 physical qubits, maturing logical qubit technology |\n| **2026-2027** | 500-5,000 physical qubits, 10-100 logical qubits |\n| **2028-2029** | 1,000-20,000 physical qubits, 100-1,000 logical qubits |\n| **2030+** | Fault-tolerant quantum computers, quantum advantage at scale |\n\n## Key Technologies (2025)\n\n### Qubit Technologies\n- **Superconducting** (IBM, Google, AWS, Rigetti): Fast gates, cryogenic cooling required\n- **Trapped Ion** (IonQ, Quantinuum): High fidelity, all-to-all connectivity\n- **Neutral Atom** (Atom Computing, Pasqal): Scalability, reconfigurable\n- **Topological** (Microsoft Majorana 1): Hardware-protected error resistance\n- **Photonic** (Xanadu): Room temperature operation\n- **Quantum Annealing** (D-Wave): Optimization problems\n\n### Software Frameworks\n- **Qiskit** (IBM): Most popular, open-source\n- **Q#** (Microsoft): Enterprise-focused, integrated with .NET\n- **Cirq** (Google): Research-oriented\n- **Amazon Braket SDK**: Multi-platform access\n- **PennyLane**: Quantum machine learning\n\n## Resources\n\n### Official Documentation\n- [IBM Quantum Documentation](https://docs.quantum.ibm.com/)\n- [AWS Braket Documentation](https://docs.aws.amazon.com/braket/)\n- [Azure Quantum Documentation](https://docs.microsoft.com/azure/quantum/)\n- [Qiskit Documentation](https://qiskit.org/documentation/)\n\n### Recent Major Announcements (2024-2025)\n- [Google Willow Quantum Chip](https://blog.google/technology/research/google-willow-quantum-chip/)\n- [AWS Ocelot Chip](https://www.aboutamazon.com/news/aws/quantum-computing-aws-ocelot-chip)\n- [Microsoft Majorana 1](https://azure.microsoft.com/en-us/blog/quantum/2025/02/19/microsoft-unveils-majorana-1-the-worlds-first-quantum-processor-powered-by-topological-qubits/)\n- [IBM Quantum Roadmap](https://www.ibm.com/quantum/blog/ibm-quantum-roadmap-2025)\n- [Microsoft 24 Logical Qubits](https://azure.microsoft.com/en-us/blog/quantum/2024/11/19/microsoft-and-atom-computing-offer-a-commercial-quantum-machine-with-the-largest-number-of-entangled-logical-qubits-on-record/)\n\n### This Repository\nExplore the `cloud-vendors` directory for detailed information about each platform, including:\n- Getting started guides\n- Code examples\n- Hardware specifications\n- Pricing information\n- Recent updates and announcements\n\n---\n\n\n\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flynnlangit%2Flearning-quantum","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flynnlangit%2Flearning-quantum","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flynnlangit%2Flearning-quantum/lists"}