curriculum.json

{
  "metadata": {
    "schema_version": 1,
    "last_updated": "2026-05-18",
    "source_of_truth": "This file is the canonical machine-readable inventory for CUDA-Q Academic lessons, learning track membership, and live visualization-gallery widgets.",
    "lesson_policy": "Use lessons for content discovery and learning track routing. For deeper notebook detail or when updating a lesson, consult the notebook intro cell when it exists.",
    "widget_policy": "Widget metadata is sourced from visualization-gallery.html on the widgets-as-html branch because the main branch does not contain the full live gallery inventory.",
    "new_lesson_policy": "Lessons backed by a notebook first added to this repo in the current calendar year carry \"is_new\": true and an \"added_date\" (YYYY-MM-DD) sourced from the first git commit that introduced the notebook (or the rename-replace that produced the current file). Clear these fields at the start of each calendar year."
  },
  "track_order": [
    "quick-start-to-quantum",
    "qis-foundational-algorithms",
    "qec-101",
    "chemistry-simulations",
    "applications-to-finance",
    "qaoa-for-max-cut",
    "ai-for-quantum",
    "dynamics-101",
    "simulation-101",
    "hybrid-workflows",
    "calibration",
    "quantum-ai-project-template"
  ],
  "tracks": {
    "quick-start-to-quantum": {
      "title": "Quick Start to Quantum Computing",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-quickstart",
      "difficulty_range": [
        "intro"
      ],
      "summary": "Foundational track from single qubits to a first variational algorithm in CUDA-Q.",
      "lesson_ids": [
        "quickstart-01-start-small-with-one-qubit",
        "quickstart-02-multiple-qubits-and-entanglement",
        "quickstart-03-write-your-first-parameterized-kernel",
        "quickstart-04-build-your-first-variational-algorithm"
      ]
    },
    "simulation-101": {
      "title": "Quantum Algorithm Simulation",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html",
      "difficulty_range": [
        "intermediate"
      ],
      "summary": "Introduces the tradeoffs between state vector, tensor network, MPS, Pauli propagation, and stabilizer simulation in CUDA-Q.",
      "lesson_ids": [
        "simulation-101-choosing-the-right-simulator"
      ]
    },
    "qis-foundational-algorithms": {
      "title": "QIS Foundational Algorithms",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-qis",
      "difficulty_range": [
        "intro",
        "intermediate"
      ],
      "summary": "Foundational quantum algorithms spanning teleportation, oracle problems, QFT, phase estimation, Grover, and Shor.",
      "lesson_ids": [
        "teleportation",
        "bernstein-vazirani",
        "deutschs-algorithm",
        "quantum-fourier-transform",
        "quantum-phase-estimation",
        "grovers-algorithm",
        "shors-algorithm"
      ]
    },
    "qec-101": {
      "title": "QEC 101",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-qec",
      "difficulty_range": [
        "intermediate",
        "advanced"
      ],
      "summary": "Quantum error correction from repetition codes and stabilizers through qLDPC codes, detector error models, and real-time decoding.",
      "lesson_ids": [
        "qec-01-introduction-to-qec",
        "qec-02-stabilizers-shor-and-steane",
        "qec-03-noisy-simulation",
        "qec-04-ai-decoders",
        "qec-05-magic-state-distillation",
        "qec-06-toric-and-surface-codes",
        "qec-07-qldpc-codes",
        "qec-08-decoder-metrics-and-parallel-window-decoding",
        "qec-09-detector-error-models-and-real-time-decoding"
      ]
    },
    "chemistry-simulations": {
      "title": "Chemistry Simulations",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-chem",
      "difficulty_range": [
        "intermediate",
        "advanced"
      ],
      "summary": "Quantum chemistry lessons covering VQE, ADAPT-VQE, QPE, Krylov methods, and QM/MM workflows.",
      "lesson_ids": [
        "vqe-and-gqe",
        "adapt-vqe",
        "quantum-phase-estimation",
        "krylov-subspace-diagonalization",
        "qmmm-hybrid-simulation"
      ]
    },
    "applications-to-finance": {
      "title": "Applications to Finance",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-finance",
      "difficulty_range": [
        "intermediate",
        "advanced"
      ],
      "summary": "Finance-focused lessons on quantum walks, portfolio optimization with QAOA and industry relevant quantum algorithm Q-CHOP.",
      "lesson_ids": [
        "quantum-walks-for-finance-part-1",
        "quantum-walks-for-finance-part-2",
        "portfolio-optimization-three-ways-q-chop"
      ]
    },
    "qaoa-for-max-cut": {
      "title": "QAOA for Max Cut (Circuit Cutting)",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-qaoa",
      "difficulty_range": [
        "intermediate"
      ],
      "summary": "QAOA for Max Cut with divide-and-conquer and recursive circuit-cutting strategies.",
      "lesson_ids": [
        "max-cut-with-qaoa",
        "divide-and-conquer-qaoa-one-level",
        "recursive-divide-and-conquer-qaoa",
        "weighted-max-cut-project"
      ]
    },
    "ai-for-quantum": {
      "title": "AI for Quantum",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-ai",
      "difficulty_range": [
        "intermediate"
      ],
      "summary": "Uses AI models to tackle bottlenecks in unitary compilation, error correction, and eigensolver workflows.",
      "lesson_ids": [
        "vqe-and-gqe",
        "compiling-unitaries-with-diffusion-models",
        "qec-04-ai-decoders",
        "qaoa-max-cut-and-gpt-qaoa-workshop"
      ]
    },
    "dynamics-101": {
      "title": "Dynamics 101",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-dynamics",
      "difficulty_range": [
        "intermediate"
      ],
      "summary": "GPU-accelerated lessons on Jaynes-Cummings dynamics and time-dependent Hamiltonians.",
      "lesson_ids": [
        "jaynes-cummings-hamiltonian",
        "time-dependent-hamiltonians"
      ]
    },
    "hybrid-workflows": {
      "title": "Hybrid Workflows",
      "learning_path_url": "https://nvidia.github.io/cuda-q-academic/learningpath.html?track=track-hybrid",
      "difficulty_range": [
        "intermediate"
      ],
      "summary": "Cross-cutting hybrid classical-quantum workflows spanning LABS optimization, QAOA, quantum walks, and VQE/GQE.",
      "lesson_ids": [
        "labs-optimization",
        "max-cut-with-qaoa",
        "quantum-walks-for-finance-part-1",
        "vqe-and-gqe",
        "qaoa-max-cut-and-gpt-qaoa-workshop"
      ]
    },
    "calibration": {
      "title": "Calibration",
      "learning_path_url": null,
      "difficulty_range": [
        "intermediate"
      ],
      "summary": "Instructor resource: classroom guide for NVIDIA's Ising Calibration NIM, an open vision-language model purpose-built for analyzing quantum hardware calibration plots. Browser-based playground with no setup; sample plots from the QCalEval dataset. Hands-on tutorial notebooks are in development.",
      "lesson_ids": [
        "calibration-start-here"
      ]
    },
    "quantum-ai-project-template": {
      "title": "Quantum AI Project Template",
      "learning_path_url": null,
      "difficulty_range": [
        "intermediate",
        "advanced"
      ],
      "summary": "Instructor resource: a role-based group project template for deploying a quantum-GPU computing project in a course. Includes faculty deployment guides, student-facing templates, and AI agent role cards. Not a self-paced module.",
      "lesson_ids": [
        "quantum-ai-project-template-overview"
      ]
    }
  },
  "lessons": {
    "quickstart-01-start-small-with-one-qubit": {
      "title": "Start Small with One Qubit",
      "primary_track_id": "quick-start-to-quantum",
      "track_ids": [
        "quick-start-to-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quick-start-to-quantum/01_quick_start_to_quantum.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quick-start-to-quantum/01_quick_start_to_quantum.ipynb",
      "difficulty": "intro",
      "prerequisites": [
        "Python",
        "Jupyter notebooks",
        "complex numbers",
        "linear algebra basics",
        "basic statistics"
      ],
      "keywords": [
        "single qubit",
        "Bloch sphere",
        "Pauli gates",
        "Hadamard gate",
        "measurement"
      ],
      "summary": "Introduces single-qubit states, Bloch-sphere intuition, and the Pauli and Hadamard gates in CUDA-Q.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": [
        "bloch-sphere-visualizer",
        "borns-rule-and-sampling"
      ]
    },
    "quickstart-02-multiple-qubits-and-entanglement": {
      "title": "Multiple Qubits and Entanglement",
      "primary_track_id": "quick-start-to-quantum",
      "track_ids": [
        "quick-start-to-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quick-start-to-quantum/02_quick_start_to_quantum.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quick-start-to-quantum/02_quick_start_to_quantum.ipynb",
      "difficulty": "intro",
      "prerequisites": [
        "Quick Start Lab 1 or equivalent single-qubit background",
        "Python",
        "Jupyter notebooks",
        "complex numbers",
        "linear algebra basics"
      ],
      "keywords": [
        "multiple qubits",
        "entanglement",
        "CNOT",
        "Bell states",
        "subkernels"
      ],
      "summary": "Builds intuition for multi-qubit registers, CNOT, Bell states, and entanglement in CUDA-Q.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "cnot-gate-visualizer",
        "quantum-gate-visualization"
      ]
    },
    "quickstart-03-write-your-first-parameterized-kernel": {
      "title": "Write Your First Parameterized Kernel",
      "primary_track_id": "quick-start-to-quantum",
      "track_ids": [
        "quick-start-to-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quick-start-to-quantum/03_quick_start_to_quantum.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quick-start-to-quantum/03_quick_start_to_quantum.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Quick Start Labs 1-2 or equivalent",
        "single- and multi-qubit gates",
        "basic CUDA-Q kernel syntax"
      ],
      "keywords": [
        "parameterized kernels",
        "rotations",
        "observables",
        "variational circuits",
        "CUDA-Q"
      ],
      "summary": "Introduces parameterized CUDA-Q kernels, rotation gates, and observable evaluation.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": []
    },
    "quickstart-04-build-your-first-variational-algorithm": {
      "title": "Build Your First Variational Algorithm",
      "primary_track_id": "quick-start-to-quantum",
      "track_ids": [
        "quick-start-to-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quick-start-to-quantum/04_quick_start_to_quantum.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quick-start-to-quantum/04_quick_start_to_quantum.ipynb",
      "difficulty": "intro",
      "prerequisites": [
        "Quick Start Labs 1-3 or equivalent",
        "parameterized kernels",
        "expectation values",
        "basic optimization intuition"
      ],
      "keywords": [
        "VQE",
        "variational algorithm",
        "Hamiltonian",
        "optimization",
        "hybrid quantum-classical"
      ],
      "summary": "Builds an end-to-end variational workflow for a simple Hamiltonian using CUDA-Q.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": []
    },
    "simulation-101-choosing-the-right-simulator": {
      "title": "Choosing the Right Simulator",
      "primary_track_id": "simulation-101",
      "track_ids": [
        "simulation-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "simulation/01_simulation101.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/simulation/01_simulation101.ipynb",
      "is_new": true,
      "added_date": "2026-04-23",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter notebook familiarity",
        "basic quantum-computing knowledge (qubits, gates, circuits, bra-ket notation, measurement)",
        "expectation values and Pauli operators are helpful"
      ],
      "keywords": [
        "state vector",
        "tensor network",
        "matrix product state",
        "Pauli propagation",
        "stabilizer simulation"
      ],
      "summary": "Compares state vector, tensor network, MPS, Pauli propagation, and stabilizer methods so learners can choose the right CUDA-Q simulator.",
      "metadata_source": "notebook intro cell + module README",
      "widget_ids": [
        "pauli-propagation-visualizer"
      ]
    },
    "teleportation": {
      "title": "Teleportation",
      "primary_track_id": "qis-foundational-algorithms",
      "track_ids": [
        "qis-foundational-algorithms"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qis-examples/quantum_teleportation.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qis-examples/quantum_teleportation.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/qis-examples/quantum_teleportation.ipynb",
      "difficulty": "intro",
      "prerequisites": [
        "single- and two-qubit states",
        "Bell states and entanglement",
        "measurement and basis changes",
        "classical communication basics"
      ],
      "keywords": [
        "quantum teleportation",
        "Bell pair",
        "entanglement",
        "measurement",
        "classical communication"
      ],
      "summary": "Explains the Bell-pair teleportation protocol for transferring a quantum state with classical communication.",
      "metadata_source": "llms.txt"
    },
    "bernstein-vazirani": {
      "title": "Bernstein-Vazirani",
      "primary_track_id": "qis-foundational-algorithms",
      "track_ids": [
        "qis-foundational-algorithms"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qis-examples/bernstein_vazirani.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qis-examples/bernstein_vazirani.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/qis-examples/bernstein_vazirani.ipynb",
      "difficulty": "intro",
      "prerequisites": [
        "superposition and Hadamard gates",
        "oracle circuits",
        "bit strings"
      ],
      "keywords": [
        "Bernstein-Vazirani",
        "oracle",
        "query complexity",
        "Hadamard transform",
        "bit strings"
      ],
      "summary": "Shows how an oracle-based algorithm can recover a hidden bit string in a single quantum query.",
      "metadata_source": "llms.txt"
    },
    "deutschs-algorithm": {
      "title": "Deutsch's Algorithm",
      "primary_track_id": "qis-foundational-algorithms",
      "track_ids": [
        "qis-foundational-algorithms"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qis-examples/deutsch_algorithm.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qis-examples/deutsch_algorithm.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/qis-examples/deutsch_algorithm.ipynb",
      "difficulty": "intro",
      "prerequisites": [
        "Hadamard gates",
        "oracle circuits",
        "boolean functions",
        "superposition"
      ],
      "keywords": [
        "Deutsch's algorithm",
        "constant vs balanced",
        "oracle",
        "interference",
        "query complexity"
      ],
      "summary": "Uses interference to distinguish constant and balanced Boolean functions with fewer queries than a classical strategy.",
      "metadata_source": "llms.txt"
    },
    "quantum-fourier-transform": {
      "title": "Quantum Fourier Transform",
      "primary_track_id": "qis-foundational-algorithms",
      "track_ids": [
        "qis-foundational-algorithms"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qis-examples/quantum_fourier_transform.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qis-examples/quantum_fourier_transform.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/qis-examples/quantum_fourier_transform.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "complex numbers and phases",
        "controlled rotations",
        "linear algebra basics",
        "quantum circuit notation"
      ],
      "keywords": [
        "Quantum Fourier Transform",
        "phase kickback",
        "controlled rotations",
        "frequency basis",
        "QFT"
      ],
      "summary": "Builds the QFT circuit and develops intuition for phase kickback and Fourier-basis transformations.",
      "metadata_source": "llms.txt"
    },
    "quantum-phase-estimation": {
      "title": "QPE: Canonical, Iterative, and Bayesian",
      "primary_track_id": "chemistry-simulations",
      "track_ids": [
        "qis-foundational-algorithms",
        "chemistry-simulations"
      ],
      "source_kind": "local_notebook",
      "repo_path": "chemistry-simulations/qpe.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/qpe.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/qpe.ipynb",
      "is_new": true,
      "added_date": "2026-03-03",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "complex numbers and matrix exponentiation",
        "eigenvalues and eigenstates of Hermitian operators"
      ],
      "keywords": [
        "quantum phase estimation",
        "QFT",
        "phase kickback",
        "IQPE",
        "BQPE",
        "eigenvalues"
      ],
      "summary": "Shows how canonical, iterative, and Bayesian QPE estimate eigenphases, with chemistry-motivated examples.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "interactive-qft-visualizer",
        "binary-fraction-explorer"
      ]
    },
    "grovers-algorithm": {
      "title": "Grover's Algorithm",
      "primary_track_id": "qis-foundational-algorithms",
      "track_ids": [
        "qis-foundational-algorithms"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qis-examples/grovers.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qis-examples/grovers.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/qis-examples/grovers.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "superposition and amplitudes",
        "oracle circuits",
        "reflection or phase-flip intuition",
        "basic quantum algorithm notation"
      ],
      "keywords": [
        "Grover's algorithm",
        "amplitude amplification",
        "oracle",
        "search",
        "diffusion operator"
      ],
      "summary": "Covers oracle design and amplitude amplification for quadratic-speedup search.",
      "metadata_source": "llms.txt + notebook title",
      "widget_ids": []
    },
    "shors-algorithm": {
      "title": "Shor's Algorithm",
      "primary_track_id": "qis-foundational-algorithms",
      "track_ids": [
        "qis-foundational-algorithms"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qis-examples/shors.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qis-examples/shors.ipynb",
      "colab_url": "https://colab.research.google.com/github/NVIDIA/cuda-q-academic/blob/main/qis-examples/shors.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Quantum Fourier Transform",
        "modular arithmetic",
        "phase estimation basics",
        "classical factoring context"
      ],
      "keywords": [
        "Shor's algorithm",
        "period finding",
        "modular exponentiation",
        "QFT",
        "factoring"
      ],
      "summary": "Combines modular arithmetic, period finding, and the QFT to factor integers.",
      "metadata_source": "llms.txt"
    },
    "qec-01-introduction-to-qec": {
      "title": "Introduction to QEC",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/01_QEC_Intro.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/01_QEC_Intro.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "braket notation",
        "Quick Start to Quantum Computing or equivalent"
      ],
      "keywords": [
        "repetition code",
        "Hamming code",
        "syndrome",
        "logical error rate",
        "error correction basics"
      ],
      "summary": "Introduces repetition codes, Hamming codes, and the core ideas that separate quantum error correction from classical error correction.",
      "metadata_source": "notebook intro cell",
      "widget_ids": [
        "hamming-7-4-3-visualizer",
        "error-digitization-explorer"
      ]
    },
    "qec-02-stabilizers-shor-and-steane": {
      "title": "Stabilizers, Shor Code, Steane Code",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/02_QEC_Stabilizers.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/02_QEC_Stabilizers.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "QEC 101 Lab 1",
        "Pauli matrices and tensor products"
      ],
      "keywords": [
        "stabilizers",
        "Steane code",
        "Shor code",
        "CSS codes",
        "code capacity"
      ],
      "summary": "Introduces the stabilizer formalism and works through Shor and Steane code implementations in CUDA-Q.",
      "metadata_source": "notebook intro cell",
      "widget_ids": []
    },
    "qec-03-noisy-simulation": {
      "title": "Noisy Simulation",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/03_QEC_Noisy_Simulation.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/03_QEC_Noisy_Simulation.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "CUDA-Q kernel syntax and cudaq.sample",
        "QEC 101 Labs 1-2"
      ],
      "keywords": [
        "noise channels",
        "density matrices",
        "trajectory simulation",
        "zero-noise extrapolation",
        "logical error rates"
      ],
      "summary": "Explores depolarizing and Pauli-style noise, noisy circuit simulation, and mitigation workflows in CUDA-Q.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "trajectory-noise-demo"
      ]
    },
    "qec-04-ai-decoders": {
      "title": "AI Decoders",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101",
        "ai-for-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/04_QEC_Decoders.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/04_QEC_Decoders.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "QEC 101 Lab 1",
        "QEC 101 Lab 2"
      ],
      "keywords": [
        "decoders",
        "Pauli frame tracking",
        "maximum-likelihood decoding",
        "AI decoder",
        "belief propagation"
      ],
      "summary": "Introduces syndrome decoding through brute-force, neural, and BP+OSD decoders for Steane-code and qLDPC examples.",
      "metadata_source": "notebook intro cell + llms.txt (notebook contains merge-conflict markers later in the file)",
      "widget_ids": []
    },
    "qec-05-magic-state-distillation": {
      "title": "Magic State Distillation",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/05_QEC_MSD.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/05_QEC_MSD.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "QEC basics",
        "Steane-code background"
      ],
      "keywords": [
        "magic state distillation",
        "T gate",
        "fault tolerance",
        "Clifford+T",
        "resource overhead"
      ],
      "summary": "Explains why magic states are needed for universal fault-tolerant quantum computing and implements a distillation protocol in CUDA-Q.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": []
    },
    "qec-06-toric-and-surface-codes": {
      "title": "Toric and Surface Codes",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/06_QEC_Topological_Codes.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/06_QEC_Topological_Codes.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "stabilizer formalism and syndrome measurement",
        "CUDA-Q kernels and sampling"
      ],
      "keywords": [
        "toric code",
        "surface code",
        "topological codes",
        "MWPM",
        "logical operators"
      ],
      "summary": "Covers toric and surface-code structure, logical operators on lattices, and minimum-weight matching decoding.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": []
    },
    "qec-07-qldpc-codes": {
      "title": "qLDPC Codes",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/07_QEC_qLDPC.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/07_QEC_qLDPC.ipynb",
      "difficulty": "advanced",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "QEC 101 Labs 1-4",
        "parity-check matrices and Tanner graphs",
        "basic linear algebra"
      ],
      "keywords": [
        "qLDPC",
        "hypergraph product codes",
        "lifted product codes",
        "Tanner graphs",
        "BP+OSD"
      ],
      "summary": "Introduces quantum LDPC constructions and compares decoder performance on higher-rate code families.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": []
    },
    "qec-08-decoder-metrics-and-parallel-window-decoding": {
      "title": "Decoder Metrics and Parallel Window Decoding",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/08_QEC_Decoder_metrics_and_parallel_window_decoding.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/08_QEC_Decoder_metrics_and_parallel_window_decoding.ipynb",
      "is_new": true,
      "added_date": "2026-01-29",
      "difficulty": "advanced",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "QEC 101 Labs 1-4",
        "stabilizers and decoders",
        "magic state distillation is helpful"
      ],
      "keywords": [
        "decoder metrics",
        "accuracy",
        "throughput",
        "reaction time",
        "parallel window decoding"
      ],
      "summary": "Examines decoder benchmarking metrics and explores scalable temporal window-decoding strategies for large QEC systems.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": []
    },
    "qec-09-detector-error-models-and-real-time-decoding": {
      "title": "Detector Error Models and Real-Time Decoding",
      "primary_track_id": "qec-101",
      "track_ids": [
        "qec-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qec101/09_QEC_Detector_Error_Models.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qec101/09_QEC_Detector_Error_Models.ipynb",
      "is_new": true,
      "added_date": "2026-03-05",
      "difficulty": "advanced",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "QEC 101 Labs 1-4",
        "decoder metrics and reaction-time concepts",
        "stabilizers and syndrome decoding"
      ],
      "keywords": [
        "detector error models",
        "real-time decoding",
        "Tanner graphs",
        "observables",
        "syndrome processing"
      ],
      "summary": "Introduces detector error models and the API patterns needed for low-latency, real-time decoding workflows.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "steane-code-noise-model-explorer"
      ]
    },
    "vqe-and-gqe": {
      "title": "VQE and GQE",
      "primary_track_id": "chemistry-simulations",
      "track_ids": [
        "chemistry-simulations",
        "ai-for-quantum",
        "hybrid-workflows"
      ],
      "source_kind": "local_notebook",
      "repo_path": "chemistry-simulations/vqe_and_gqe.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/vqe_and_gqe.ipynb",
      "is_new": true,
      "added_date": "2026-01-29",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "variational-algorithm intuition",
        "basic quantum chemistry terminology is helpful"
      ],
      "keywords": [
        "VQE",
        "GQE",
        "ground states",
        "ansatz",
        "barren plateaus"
      ],
      "summary": "Introduces variational and generative quantum eigensolvers for molecular ground-state preparation.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": []
    },
    "adapt-vqe": {
      "title": "ADAPT-VQE",
      "primary_track_id": "chemistry-simulations",
      "track_ids": [
        "chemistry-simulations"
      ],
      "source_kind": "local_notebook",
      "repo_path": "chemistry-simulations/adapt_vqe.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/adapt_vqe.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "VQE workflow familiarity",
        "some experience writing CUDA-Q kernels"
      ],
      "keywords": [
        "ADAPT-VQE",
        "operator pool",
        "ansatz construction",
        "gradients",
        "multi-QPU parallelism"
      ],
      "summary": "Demonstrates adaptive ansatz construction through operator-pool gradients and parallel CUDA-Q workflows.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "adapt-vqe-widget"
      ]
    },
    "krylov-subspace-diagonalization": {
      "title": "Krylov Subspace Diagonalization",
      "primary_track_id": "chemistry-simulations",
      "track_ids": [
        "chemistry-simulations"
      ],
      "source_kind": "local_notebook",
      "repo_path": "chemistry-simulations/krylov_subspace_diagonalization.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/krylov_subspace_diagonalization.ipynb",
      "difficulty": "advanced",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "quantum chemistry concepts",
        "CUDA-Q kernels, observe, and get_state"
      ],
      "keywords": [
        "Krylov subspace",
        "quantum subspace diagonalization",
        "Hadamard test",
        "Trotter-Suzuki",
        "MQPU"
      ],
      "summary": "Applies quantum Krylov-subspace diagonalization to approximate molecular eigenvalues with CUDA-Q.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "krylov-subspace-widget"
      ]
    },
    "qmmm-hybrid-simulation": {
      "title": "QM/MM Hybrid Simulation",
      "primary_track_id": "chemistry-simulations",
      "track_ids": [
        "chemistry-simulations"
      ],
      "source_kind": "local_notebook",
      "repo_path": "chemistry-simulations/qmmm.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/chemistry-simulations/qmmm.ipynb",
      "difficulty": "advanced",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "variational algorithm intuition",
        "Jordan-Wigner and qubit-Hamiltonian familiarity",
        "basic chemistry terminology"
      ],
      "keywords": [
        "QM/MM",
        "polarizable embedding",
        "1-RDM",
        "active space",
        "self-consistent workflow"
      ],
      "summary": "Combines VQE and classical polarizable embedding to build a hybrid QM/MM chemistry workflow.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "hybrid-system-qmmm",
        "active-space-selection"
      ]
    },
    "quantum-walks-for-finance-part-1": {
      "title": "Quantum Walks for Finance, Part 1",
      "primary_track_id": "applications-to-finance",
      "track_ids": [
        "applications-to-finance",
        "hybrid-workflows"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quantum-applications-to-finance/01_quantum_walks.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quantum-applications-to-finance/01_quantum_walks.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic CUDA-Q and quantum-computing familiarity",
        "braket notation",
        "measurement and circuit-sampling basics"
      ],
      "keywords": [
        "quantum walks",
        "finance",
        "stochastic modeling",
        "quantum coin",
        "discrete-time walk"
      ],
      "summary": "Introduces discrete-time quantum walks as a tool for financial and stochastic modeling.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": [
        "classic-and-split-step-random-walk"
      ]
    },
    "quantum-walks-for-finance-part-2": {
      "title": "Quantum Walks for Finance, Part 2",
      "primary_track_id": "applications-to-finance",
      "track_ids": [
        "applications-to-finance"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quantum-applications-to-finance/02_quantum_walks.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quantum-applications-to-finance/02_quantum_walks.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Quantum Walks for Finance Part 1",
        "Python and Jupyter familiarity",
        "basic CUDA-Q and quantum-computing familiarity"
      ],
      "keywords": [
        "quantum walks",
        "option pricing",
        "multi-step evolution",
        "financial simulation",
        "learnable walks"
      ],
      "summary": "Extends the quantum-walk toolkit to multi-step dynamics and option-pricing-style applications.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": []
    },
    "portfolio-optimization-three-ways-q-chop": {
      "title": "Portfolio Optimization Three Ways (Q-CHOP)",
      "primary_track_id": "applications-to-finance",
      "track_ids": [
        "applications-to-finance"
      ],
      "source_kind": "local_notebook",
      "repo_path": "quantum-applications-to-finance/03_qchop.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quantum-applications-to-finance/03_qchop.ipynb",
      "difficulty": "advanced",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic CUDA-Q and quantum-computing familiarity",
        "Hamiltonians and circuit sampling",
        "optimization basics"
      ],
      "keywords": [
        "portfolio optimization",
        "QUBO",
        "QAOA",
        "adiabatic evolution",
        "Q-CHOP"
      ],
      "summary": "Formulates portfolio optimization as QUBO and compares QAOA, adiabatic, and Q-CHOP-style solution strategies.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "qubo-portfolio-optimization",
        "q-chop-vs-adiabatic"
      ]
    },
    "max-cut-with-qaoa": {
      "title": "Max Cut with QAOA",
      "primary_track_id": "qaoa-for-max-cut",
      "track_ids": [
        "qaoa-for-max-cut",
        "hybrid-workflows"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qaoa-for-max-cut/01_Max-Cut-with-QAOA.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qaoa-for-max-cut/01_Max-Cut-with-QAOA.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python familiarity including basic NetworkX usage",
        "Quick Start to Quantum Computing or equivalent variational-algorithm background",
        "qubits, Hamiltonians, and expectation values"
      ],
      "keywords": [
        "QAOA",
        "Max Cut",
        "graphs",
        "variational optimization",
        "CUDA-Q"
      ],
      "summary": "Introduces QAOA on small Max-Cut instances and sets up the divide-and-conquer extension used later in the track.",
      "metadata_source": "module README + notebook intro + llms.txt",
      "widget_ids": []
    },
    "divide-and-conquer-qaoa-one-level": {
      "title": "Divide-and-Conquer QAOA (One Level)",
      "primary_track_id": "qaoa-for-max-cut",
      "track_ids": [
        "qaoa-for-max-cut"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qaoa-for-max-cut/02_One-level-divide-and-conquer-QAOA.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qaoa-for-max-cut/02_One-level-divide-and-conquer-QAOA.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Max Cut with QAOA",
        "graph partitioning intuition",
        "Python and NetworkX familiarity"
      ],
      "keywords": [
        "divide-and-conquer",
        "QAOA",
        "graph partitioning",
        "subproblem recombination",
        "circuit cutting"
      ],
      "summary": "Shows how a one-level divide-and-conquer strategy breaks larger Max-Cut problems into tractable QAOA subproblems.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": []
    },
    "recursive-divide-and-conquer-qaoa": {
      "title": "Recursive Divide-and-Conquer QAOA",
      "primary_track_id": "qaoa-for-max-cut",
      "track_ids": [
        "qaoa-for-max-cut"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qaoa-for-max-cut/03_Recursive-divide-and-conquer.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qaoa-for-max-cut/03_Recursive-divide-and-conquer.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Divide-and-Conquer QAOA (One Level)",
        "recursive algorithm intuition",
        "graph-partitioning workflow familiarity"
      ],
      "keywords": [
        "recursive circuit cutting",
        "QAOA",
        "large graphs",
        "divide-and-conquer",
        "Max Cut"
      ],
      "summary": "Extends divide-and-conquer QAOA recursively to larger graph instances and deeper cutting workflows.",
      "metadata_source": "llms.txt + module README + notebook title",
      "widget_ids": []
    },
    "weighted-max-cut-project": {
      "title": "Weighted Max Cut Project",
      "primary_track_id": "qaoa-for-max-cut",
      "track_ids": [
        "qaoa-for-max-cut"
      ],
      "source_kind": "local_notebook",
      "repo_path": "qaoa-for-max-cut/04_Assessment.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/qaoa-for-max-cut/04_Assessment.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "QAOA for Max Cut Labs 1-3",
        "weighted graph intuition",
        "variational optimization practice"
      ],
      "keywords": [
        "weighted Max Cut",
        "QAOA project",
        "assessment",
        "graph weights",
        "optimization"
      ],
      "summary": "Serves as a capstone-style weighted Max-Cut assessment using the QAOA techniques developed earlier in the track.",
      "metadata_source": "llms.txt + notebook title",
      "widget_ids": []
    },
    "compiling-unitaries-with-diffusion-models": {
      "title": "Compiling Unitaries with Diffusion Models",
      "primary_track_id": "ai-for-quantum",
      "track_ids": [
        "ai-for-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "ai-for-quantum/01_compiling_unitaries_diffusion.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/ai-for-quantum/01_compiling_unitaries_diffusion.ipynb",
      "is_new": true,
      "added_date": "2026-03-28",
      "difficulty": "intermediate",
      "prerequisites": [
        "basic quantum-computing concepts",
        "Quick Start to Quantum Computing or equivalent",
        "no prior diffusion-model experience required"
      ],
      "keywords": [
        "diffusion models",
        "unitary synthesis",
        "circuit compilation",
        "AI for quantum",
        "quantum circuits"
      ],
      "summary": "Uses diffusion models to synthesize quantum circuits that implement target unitaries.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "unitary-circuit-synthesis",
        "diffusion-model-visualization"
      ]
    },
    "jaynes-cummings-hamiltonian": {
      "title": "Jaynes-Cummings Hamiltonian",
      "primary_track_id": "dynamics-101",
      "track_ids": [
        "dynamics-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "dynamics101/01_Jaynes_Cummings.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/dynamics101/01_Jaynes_Cummings.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python programming",
        "solid quantum-mechanics background",
        "Hamiltonians and time-evolution intuition",
        "linear algebra"
      ],
      "keywords": [
        "Jaynes-Cummings model",
        "atom-cavity coupling",
        "Rabi oscillations",
        "open-system evolution",
        "quantum dynamics"
      ],
      "summary": "Introduces GPU-accelerated dynamics through the Jaynes-Cummings model and atom-cavity time evolution.",
      "metadata_source": "module README + llms.txt + notebook title",
      "widget_ids": []
    },
    "time-dependent-hamiltonians": {
      "title": "Time-Dependent Hamiltonians",
      "primary_track_id": "dynamics-101",
      "track_ids": [
        "dynamics-101"
      ],
      "source_kind": "local_notebook",
      "repo_path": "dynamics101/02_Time_Dependent.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/dynamics101/02_Time_Dependent.ipynb",
      "difficulty": "intermediate",
      "prerequisites": [
        "Jaynes-Cummings Hamiltonian or equivalent dynamics background",
        "Python programming",
        "quantum time evolution",
        "time-dependent Hamiltonian intuition"
      ],
      "keywords": [
        "time-dependent Hamiltonians",
        "driven systems",
        "Landau-Zener",
        "custom operators",
        "quantum dynamics"
      ],
      "summary": "Extends CUDA-Q Dynamics to driven systems with explicitly time-dependent Hamiltonian terms.",
      "metadata_source": "module README + llms.txt + notebook title",
      "widget_ids": []
    },
    "labs-optimization": {
      "title": "Quantum-Enhanced Optimization (LABS)",
      "primary_track_id": "hybrid-workflows",
      "track_ids": [
        "hybrid-workflows"
      ],
      "source_kind": "local_notebook",
      "repo_path": "hybrid-workflows/01_quantum_enhanced_optimization_LABS.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/hybrid-workflows/01_quantum_enhanced_optimization_LABS.ipynb",
      "is_new": true,
      "added_date": "2026-01-28",
      "difficulty": "intermediate",
      "prerequisites": [
        "basic quantum-computing knowledge",
        "Quick Start to Quantum Computing or equivalent",
        "interest in optimization workflows"
      ],
      "keywords": [
        "LABS",
        "hybrid optimization",
        "memetic tabu search",
        "counteradiabatic optimization",
        "radar"
      ],
      "summary": "Builds a quantum-enhanced workflow for the low-autocorrelation binary sequence optimization problem.",
      "metadata_source": "notebook intro cell + llms.txt",
      "widget_ids": [
        "labs-optimization-search"
      ]
    },
    "qaoa-max-cut-and-gpt-qaoa-workshop": {
      "title": "QAOA for Max Cut and GPT-QAOA Workshop",
      "primary_track_id": "hybrid-workflows",
      "track_ids": [
        "hybrid-workflows",
        "ai-for-quantum"
      ],
      "source_kind": "local_notebook",
      "repo_path": "hybrid-workflows/02_qaoa_for_max_cut_and_gpt_qaoa.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/hybrid-workflows/02_qaoa_for_max_cut_and_gpt_qaoa.ipynb",
      "is_new": true,
      "added_date": "2026-05-18",
      "difficulty": "intermediate",
      "prerequisites": [
        "Python and Jupyter familiarity",
        "basic quantum computing concepts",
        "QAOA intuition (angles, variational optimization)",
        "basic graph concepts (vertices, edges, cuts)"
      ],
      "keywords": [
        "QAOA",
        "Max Cut",
        "hybrid workflows",
        "GPT-QAOA",
        "variational optimization"
      ],
      "summary": "Workshop-style notebook that develops Max-Cut QAOA intuition and extends to hybrid GPT-assisted QAOA workflows.",
      "metadata_source": "2026 workshop notebook adapted for hybrid-workflows track",
      "widget_ids": [
        "borns-rule-and-sampling",
        "qaoa-max-cut-explorer",
        "qaoa-optimization-landscape",
        "choosing-the-next-operator-adapt-qaoa",
        "adapt-qaoa-vs-standard-qaoa"
      ]
    },
    "calibration-start-here": {
      "title": "Start Here — Calibration Track",
      "primary_track_id": "calibration",
      "track_ids": [
        "calibration"
      ],
      "source_kind": "notebook",
      "repo_path": "calibration/00_StartHere.ipynb",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/calibration/00_StartHere.ipynb",
      "is_new": true,
      "added_date": "2026-05-18",
      "difficulty": "intermediate",
      "prerequisites": [
        "familiarity with reading quantum hardware calibration plots (e.g. Rabi, T1, T2, randomized benchmarking)"
      ],
      "keywords": [
        "calibration",
        "Ising Calibration",
        "NIM",
        "vision-language model",
        "QCalEval",
        "hardware characterization",
        "instructor resource",
        "coming soon"
      ],
      "summary": "Coming-soon landing notebook for the Calibration track. Points instructors and learners to the calibration/ README, which covers using NVIDIA's Ising Calibration NIM playground for structured analysis of calibration experiment plots and links to the QCalEval sample dataset.",
      "metadata_source": "calibration/README.md",
      "widget_ids": []
    },
    "quantum-ai-project-template-overview": {
      "title": "Quantum AI Project Template",
      "primary_track_id": "quantum-ai-project-template",
      "track_ids": [
        "quantum-ai-project-template"
      ],
      "source_kind": "instructor_template",
      "repo_path": "quantum-ai-project-template/README.md",
      "link": "https://github.com/NVIDIA/cuda-q-academic/blob/main/quantum-ai-project-template/README.md",
      "difficulty": "intermediate",
      "prerequisites": [
        "Quick Start to Quantum Computing or equivalent CUDA-Q familiarity",
        "basic hybrid quantum-classical algorithm concepts"
      ],
      "keywords": [
        "group project",
        "role-based learning",
        "agentic AI",
        "quantum-GPU",
        "POGIL",
        "course template",
        "instructor resource"
      ],
      "summary": "Role-based group project template for deploying a quantum-GPU computing project in a course. Covers four student roles (Project Lead, Performance Optimization PIC, QA PIC, Technical Marketing PIC), four milestones, AI agent configurations, and a deliverable template. Faculty deployment guide and pedagogical rationale included.",
      "metadata_source": "quantum-ai-project-template/README.md"
    }
  },
  "widget_gallery_order": [
    "bloch-sphere-visualizer",
    "borns-rule-and-sampling",
    "cudaq-hello-world",
    "quantum-gate-visualization",
    "quantum-random-walk",
    "classic-and-split-step-random-walk",
    "diffusion-model-visualization",
    "unitary-circuit-synthesis",
    "cnot-gate-visualizer",
    "vqe-convergence",
    "qubo-portfolio-optimization",
    "q-chop-vs-adiabatic",
    "adapt-vqe-widget",
    "krylov-subspace-widget",
    "active-space-selection",
    "hybrid-system-qmmm",
    "labs-optimization-search",
    "binary-fraction-explorer",
    "pauli-propagation-visualizer",
    "interactive-qft-visualizer",
    "steane-code-noise-model-explorer",
    "hamming-7-4-3-visualizer",
    "trajectory-noise-demo",
    "mwpm-decoding-challenge",
    "error-digitization-explorer",
    "qaoa-max-cut-explorer",
    "qaoa-optimization-landscape",
    "choosing-the-next-operator-adapt-qaoa",
    "adapt-qaoa-vs-standard-qaoa",
    "bits-vs-qubits-vertex-coloring"
  ],
  "widgets": {
    "bloch-sphere-visualizer": {
      "title": "Bloch Sphere Visualizer",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quantum-applications-to-finance/images/bloch_sphere_visualization.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quantum-applications-to-finance/images/bloch_sphere_visualization.html",
      "source_repo_path": "quantum-applications-to-finance/images/bloch_sphere_visualization.html",
      "related_content": [
        "Quick Start to Quantum",
        "Applications to Finance"
      ],
      "related_track_ids": [
        "quick-start-to-quantum",
        "applications-to-finance"
      ],
      "summary": "Interactive 3D representation of a qubit. Visualize state vectors, phase changes, and the difference between bit-flips and phase-flips.",
      "keywords": [
        "Bloch sphere",
        "single qubit",
        "state vector",
        "phase",
        "bit flip"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "borns-rule-and-sampling": {
      "title": "Born's Rule and Sampling",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quick-start-to-quantum/interactive_widget/borns-rule-widget-sampling.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quick-start-to-quantum/interactive_widget/borns-rule-widget-sampling.html",
      "source_repo_path": "quick-start-to-quantum/interactive_widget/borns-rule-widget-sampling.html",
      "related_content": [
        "Quick Start to Quantum"
      ],
      "related_track_ids": [
        "quick-start-to-quantum"
      ],
      "summary": "Explore probability amplitudes on the Bloch Sphere. Demonstrates Born's Rule, sampling histograms, and measurement outcomes.",
      "keywords": [
        "Born rule",
        "measurement",
        "sampling",
        "probability amplitudes",
        "Bloch sphere"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "cudaq-hello-world": {
      "title": "CUDA-Q Hello World",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quick-start-to-quantum/interactive_widget/cudaq-hello-world.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quick-start-to-quantum/interactive_widget/cudaq-hello-world.html",
      "source_repo_path": "quick-start-to-quantum/interactive_widget/cudaq-hello-world.html",
      "related_content": [
        "Quick Start to Quantum"
      ],
      "related_track_ids": [
        "quick-start-to-quantum"
      ],
      "summary": "Visual circuit builder that autogenerates CUDA-Q code. Explore 'Get State', 'Sample', and 'Run' modes.",
      "keywords": [
        "CUDA-Q",
        "circuit builder",
        "statevector",
        "sampling",
        "intro programming"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "quantum-gate-visualization": {
      "title": "Quantum Gate Visualization",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quantum-applications-to-finance/images/gate-widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quantum-applications-to-finance/images/gate-widget.html",
      "source_repo_path": "quantum-applications-to-finance/images/gate-widget.html",
      "related_content": [
        "Quick Start to Quantum",
        "Applications to Finance"
      ],
      "related_track_ids": [
        "quick-start-to-quantum",
        "applications-to-finance"
      ],
      "summary": "A sandbox for single-qubit quantum gates. Visualize quantum gates on the Bloch Sphere.",
      "keywords": [
        "single-qubit gates",
        "Bloch sphere",
        "gate sandbox",
        "Pauli gates",
        "rotations"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "quantum-random-walk": {
      "title": "Quantum Random Walk",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quick-start-to-quantum/interactive_widget/quantum-walk-coin-flip.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quick-start-to-quantum/interactive_widget/quantum-walk-coin-flip.html",
      "source_repo_path": "quick-start-to-quantum/interactive_widget/quantum-walk-coin-flip.html",
      "related_content": [
        "Quick Start to Quantum",
        "Applications to Finance"
      ],
      "related_track_ids": [
        "quick-start-to-quantum",
        "applications-to-finance"
      ],
      "summary": "Explore a quantum random walk driven by a Hadamard gate acting on a quantum coin.",
      "keywords": [
        "quantum walk",
        "Hadamard coin",
        "superposition",
        "stochastic process",
        "finance"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "classic-and-split-step-random-walk": {
      "title": "Classic and Split Step Random Walk",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quantum-applications-to-finance/images/ss-random-walk.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quantum-applications-to-finance/images/ss-random-walk.html",
      "source_repo_path": "quantum-applications-to-finance/images/ss-random-walk.html",
      "related_content": [
        "Applications to Finance"
      ],
      "related_track_ids": [
        "applications-to-finance"
      ],
      "summary": "Classic random walk and a variation where steps are split into two decisions.",
      "keywords": [
        "classical walk",
        "split-step walk",
        "random walk",
        "finance",
        "time evolution"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "diffusion-model-visualization": {
      "title": "Diffusion Model Visualization",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/ai-for-quantum/widgets/diffusion.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/ai-for-quantum/widgets/diffusion.html",
      "source_repo_path": "ai-for-quantum/widgets/diffusion.html",
      "related_content": [
        "AI for Quantum"
      ],
      "related_track_ids": [
        "ai-for-quantum"
      ],
      "summary": "Visualizes diffusion, showing how noise is added or removed from data—key in thermodynamics and GenAI.",
      "keywords": [
        "diffusion models",
        "noise",
        "generative AI",
        "forward process",
        "reverse process"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "unitary-circuit-synthesis": {
      "title": "Unitary Circuit Synthesis",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/ai-for-quantum/widgets/unitary_widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/ai-for-quantum/widgets/unitary_widget.html",
      "source_repo_path": "ai-for-quantum/widgets/unitary_widget.html",
      "related_content": [
        "AI for Quantum"
      ],
      "related_track_ids": [
        "ai-for-quantum"
      ],
      "summary": "Visualize the link between Unitary matrices and quantum gates on the Bloch Sphere.",
      "keywords": [
        "unitary synthesis",
        "quantum gates",
        "Bloch sphere",
        "circuit compilation",
        "AI for quantum"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "cnot-gate-visualizer": {
      "title": "CNOT Gate Visualizer",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quick-start-to-quantum/interactive_widget/cnot-visualization.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quick-start-to-quantum/interactive_widget/cnot-visualization.html",
      "source_repo_path": "quick-start-to-quantum/interactive_widget/cnot-visualization.html",
      "related_content": [
        "Quick Start to Quantum"
      ],
      "related_track_ids": [
        "quick-start-to-quantum"
      ],
      "summary": "Interactive breakdown of the CNOT gate. Visualize how control and target qubits interact to create entanglement.",
      "keywords": [
        "CNOT",
        "entanglement",
        "control qubit",
        "target qubit",
        "two-qubit gates"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "vqe-convergence": {
      "title": "VQE Convergence",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/chemistry-simulations/Images/VQE_Widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/chemistry-simulations/Images/VQE_Widget.html",
      "source_repo_path": "chemistry-simulations/Images/VQE_Widget.html",
      "related_content": [
        "Chemistry Simulations"
      ],
      "related_track_ids": [
        "chemistry-simulations"
      ],
      "summary": "Dynamically visualizes VQE, showing how an optimizer updates parameters to find the ground state.",
      "keywords": [
        "VQE",
        "optimizer",
        "ground state",
        "energy minimization",
        "chemistry"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "qubo-portfolio-optimization": {
      "title": "QUBO Portfolio Optimization",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quantum-applications-to-finance/images/QUBO_widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quantum-applications-to-finance/images/QUBO_widget.html",
      "source_repo_path": "quantum-applications-to-finance/images/QUBO_widget.html",
      "related_content": [
        "Applications to Finance"
      ],
      "related_track_ids": [
        "applications-to-finance"
      ],
      "summary": "Visualizes the solution to a Quadratic Unconstrained Binary Optimization (QUBO) problem for portfolios.",
      "keywords": [
        "QUBO",
        "portfolio optimization",
        "finance",
        "binary optimization",
        "objective function"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "q-chop-vs-adiabatic": {
      "title": "Q-CHOP vs Adiabatic",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/quantum-applications-to-finance/images/Q_CHOP_Animated.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/quantum-applications-to-finance/images/Q_CHOP_Animated.html",
      "source_repo_path": "quantum-applications-to-finance/images/Q_CHOP_Animated.html",
      "related_content": [
        "Applications to Finance"
      ],
      "related_track_ids": [
        "applications-to-finance"
      ],
      "summary": "Animated comparison of standard adiabatic evolution vs Q-Chop, comparing convergence speed.",
      "keywords": [
        "Q-CHOP",
        "adiabatic evolution",
        "portfolio optimization",
        "convergence",
        "finance"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "adapt-vqe-widget": {
      "title": "ADAPT-VQE",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/chemistry-simulations/Images/adapt_vqe/adapt_widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/chemistry-simulations/Images/adapt_vqe/adapt_widget.html",
      "source_repo_path": "chemistry-simulations/Images/adapt_vqe/adapt_widget.html",
      "related_content": [
        "Chemistry Simulations"
      ],
      "related_track_ids": [
        "chemistry-simulations"
      ],
      "summary": "Demonstrates an adaptive algorithm that constructs its own ansatz iteratively to minimize energy.",
      "keywords": [
        "ADAPT-VQE",
        "ansatz growth",
        "operator pool",
        "energy minimization",
        "chemistry"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "krylov-subspace-widget": {
      "title": "Krylov Subspace",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/chemistry-simulations/Images/krylov.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/chemistry-simulations/Images/krylov.html",
      "source_repo_path": "chemistry-simulations/Images/krylov.html",
      "related_content": [
        "Chemistry Simulations"
      ],
      "related_track_ids": [
        "chemistry-simulations"
      ],
      "summary": "Visualizes Krylov subspace methods used to approximate eigenvalues of large sparse matrices.",
      "keywords": [
        "Krylov subspace",
        "eigenvalues",
        "quantum chemistry",
        "sparse matrices",
        "subspace methods"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "active-space-selection": {
      "title": "Active Space Selection",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/chemistry-simulations/Images/activespace.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/chemistry-simulations/Images/activespace.html",
      "source_repo_path": "chemistry-simulations/Images/activespace.html",
      "related_content": [
        "Chemistry Simulations"
      ],
      "related_track_ids": [
        "chemistry-simulations"
      ],
      "summary": "Visualizes choosing a subset of variables (orbitals) to simulate while freezing others.",
      "keywords": [
        "active space",
        "orbitals",
        "chemistry",
        "freezing orbitals",
        "model reduction"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "hybrid-system-qmmm": {
      "title": "Hybrid System (QM/MM)",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/chemistry-simulations/Images/qmmm_widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/chemistry-simulations/Images/qmmm_widget.html",
      "source_repo_path": "chemistry-simulations/Images/qmmm_widget.html",
      "related_content": [
        "Chemistry Simulations"
      ],
      "related_track_ids": [
        "chemistry-simulations"
      ],
      "summary": "Visualizes a multi-scale simulation where a quantum region is embedded within a classical environment.",
      "keywords": [
        "QM/MM",
        "hybrid simulation",
        "embedding",
        "chemistry",
        "multi-scale"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "pauli-propagation-visualizer": {
      "title": "Pauli Propagation Visualizer",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/pauli_prop_widget.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/pauli_prop_widget.html",
      "source_repo_path": "interactive_widgets/pauli_prop_widget.html",
      "related_content": [
        "Simulation 101"
      ],
      "related_track_ids": [
        "simulation-101"
      ],
      "summary": "Step through the Heisenberg picture of quantum evolution: pick an observable, then watch it propagate backward through H, S, CNOT, and RX gates according to Heisenberg commutation rules.",
      "keywords": [
        "Pauli propagation",
        "Heisenberg picture",
        "Clifford gates",
        "observable",
        "simulation"
      ],
      "metadata_source": "widget HTML + notebook scan (widgets-as-html branch)"
    },
    "labs-optimization-search": {
      "title": "LABS Optimization Search",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/labs_visualization.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/labs_visualization.html",
      "source_repo_path": "interactive_widgets/labs_visualization.html",
      "related_content": [
        "Hybrid Workflows"
      ],
      "related_track_ids": [
        "hybrid-workflows"
      ],
      "summary": "Visualizes the energy landscape of the Low-Autocorrelation Binary Sequence (LABS) problem.",
      "keywords": [
        "LABS",
        "optimization landscape",
        "hybrid workflows",
        "binary sequences",
        "energy landscape"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "binary-fraction-explorer": {
      "title": "Binary Fraction Explorer",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/binary_fraction.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/binary_fraction.html",
      "source_repo_path": "interactive_widgets/binary_fraction.html",
      "related_content": [],
      "related_track_ids": [
        "qis-foundational-algorithms",
        "chemistry-simulations"
      ],
      "summary": "Toggle bits to accumulate fractions and match a target value. Builds intuition for binary representations used in quantum phase estimation.",
      "keywords": [
        "binary fractions",
        "phase estimation",
        "QPE",
        "bits",
        "number representation"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "interactive-qft-visualizer": {
      "title": "Interactive QFT Visualizer",
      "gallery_section": "core",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/qft.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/qft.html",
      "source_repo_path": "interactive_widgets/qft.html",
      "related_content": [],
      "related_track_ids": [
        "qis-foundational-algorithms",
        "chemistry-simulations"
      ],
      "summary": "Explore the Quantum Fourier Transform by interacting with phase rotations on the unit circle and observing the resulting matrix construction.",
      "keywords": [
        "QFT",
        "phase rotations",
        "unit circle",
        "Fourier transform",
        "matrix construction"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "steane-code-noise-model-explorer": {
      "title": "Steane Code Noise Model Explorer",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/steane_code_error_models.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/steane_code_error_models.html",
      "source_repo_path": "interactive_widgets/steane_code_error_models.html",
      "related_content": [],
      "related_track_ids": [
        "qec-101"
      ],
      "summary": "Simulate and correct quantum errors using Code Capacity, Circuit Level, and Phenomenological noise models on the Steane [7,1,3] code.",
      "keywords": [
        "Steane code",
        "noise models",
        "error correction",
        "QEC",
        "logical errors"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "hamming-7-4-3-visualizer": {
      "title": "Hamming [7,4,3] Visualizer",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/hamming.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/hamming.html",
      "source_repo_path": "interactive_widgets/hamming.html",
      "related_content": [],
      "related_track_ids": [
        "qec-101"
      ],
      "summary": "Visualizes error correction using parity bits and matrix multiplication to detect and correct single-bit errors in the classical Hamming code.",
      "keywords": [
        "Hamming code",
        "parity checks",
        "classical coding theory",
        "syndrome decoding",
        "error correction"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "trajectory-noise-demo": {
      "title": "Trajectory Noise Demo",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/trajectory_noise_demo.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/trajectory_noise_demo.html",
      "source_repo_path": "interactive_widgets/trajectory_noise_demo.html",
      "related_content": [],
      "related_track_ids": [
        "qec-101"
      ],
      "summary": "Visualize trajectory-based error models showing how noise affects qubit state evolution over time.",
      "keywords": [
        "trajectory simulation",
        "noise",
        "qubit evolution",
        "QEC",
        "open systems"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "mwpm-decoding-challenge": {
      "title": "MWPM Decoding Challenge",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/mwpm_decoding_challenge.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/mwpm_decoding_challenge.html",
      "source_repo_path": "interactive_widgets/mwpm_decoding_challenge.html",
      "related_content": [
        "QEC 101: Topological Codes"
      ],
      "related_track_ids": [
        "qec-101"
      ],
      "summary": "Pair syndrome defects on a toric code lattice to find the minimum-weight perfect matching. Explore code distance and pattern difficulty.",
      "keywords": [
        "MWPM",
        "surface code",
        "toric code",
        "syndrome matching",
        "decoder"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "error-digitization-explorer": {
      "title": "Error Digitization Explorer",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/error_digitization.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/error_digitization.html",
      "source_repo_path": "interactive_widgets/error_digitization.html",
      "related_content": [
        "QEC 101: Introduction"
      ],
      "related_track_ids": [
        "qec-101"
      ],
      "summary": "See how small coherent rotation errors accumulate over many gate applications. Ideal vs noisy angle per gate with adjustable n.",
      "keywords": [
        "coherent errors",
        "digitization",
        "gate errors",
        "noise accumulation",
        "QEC"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "qaoa-max-cut-explorer": {
      "title": "QAOA Max Cut Explorer",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/triangle-qaoa.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/triangle-qaoa.html",
      "source_repo_path": "interactive_widgets/triangle-qaoa.html",
      "related_content": [
        "GTC Accelerated Quantum Supercomputing Workshop"
      ],
      "related_track_ids": [
        "qaoa-for-max-cut"
      ],
      "summary": "See QAOA working on the simplest interesting graph: a triangle (3 nodes, 3 edges). Play the role of the classical optimizer — two sliders control the cost angle (α) and mixer angle (β) — and watch quantum interference steer the probability distribution toward the max cut.",
      "keywords": [
        "QAOA",
        "Max Cut",
        "triangle graph",
        "cost angle",
        "mixer angle"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "qaoa-optimization-landscape": {
      "title": "QAOA Optimization Landscape",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/qaoa-landscape.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/qaoa-landscape.html",
      "source_repo_path": "interactive_widgets/qaoa-landscape.html",
      "related_content": [
        "GTC Accelerated Quantum Supercomputing Workshop"
      ],
      "related_track_ids": [
        "qaoa-for-max-cut"
      ],
      "summary": "Explore the 3-D cost landscape of a 1-layer QAOA circuit, see how tuning parameters helps the optimizer find the optimal Max Cut, and discover why optimization gets harder at scale — the barren plateau problem.",
      "keywords": [
        "QAOA",
        "optimization landscape",
        "barren plateaus",
        "Max Cut",
        "variational tuning"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "choosing-the-next-operator-adapt-qaoa": {
      "title": "Choosing the Next Operator (Adapt-QAOA)",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/operator-gradient.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/operator-gradient.html",
      "source_repo_path": "interactive_widgets/operator-gradient.html",
      "related_content": [
        "GTC Accelerated Quantum Supercomputing Workshop"
      ],
      "related_track_ids": [
        "qaoa-for-max-cut"
      ],
      "summary": "One QAOA layer has already been applied to a triangle graph. Now choose which operator to add next. The gradient tells you — drag the θ slider to see how each candidate operator changes the cost and which one improves the circuit the most (the core idea behind Adapt-QAOA).",
      "keywords": [
        "Adapt-QAOA",
        "operator gradients",
        "ansatz growth",
        "QAOA",
        "Max Cut"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "adapt-qaoa-vs-standard-qaoa": {
      "title": "ADAPT-QAOA vs. Standard QAOA",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/adapt-qaoa.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/adapt-qaoa.html",
      "source_repo_path": "interactive_widgets/adapt-qaoa.html",
      "related_content": [
        "GTC Accelerated Quantum Supercomputing Workshop"
      ],
      "related_track_ids": [
        "qaoa-for-max-cut"
      ],
      "summary": "Run a full ADAPT-QAOA side-by-side with standard QAOA on a Max-Cut problem. Build the ansatz layer-by-layer from the mixer pool using gradient-based operator selection and compare convergence.",
      "keywords": [
        "Adapt-QAOA",
        "standard QAOA",
        "Max Cut",
        "convergence",
        "variational algorithms"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    },
    "bits-vs-qubits-vertex-coloring": {
      "title": "Bits vs Qubits: Vertex Coloring",
      "gallery_section": "domain-specific",
      "live_url": "https://nvidia.github.io/cuda-q-academic/interactive_widgets/bits-vs-qubits-coloring.html",
      "source_url": "https://github.com/NVIDIA/cuda-q-academic/blob/widgets-as-html/interactive_widgets/bits-vs-qubits-coloring.html",
      "source_repo_path": "interactive_widgets/bits-vs-qubits-coloring.html",
      "related_content": [
        "GTC Accelerated Quantum Supercomputing Workshop"
      ],
      "related_track_ids": [
        "qaoa-for-max-cut"
      ],
      "summary": "Compare how classical bits and quantum qubits encode 2-colorings of a 7-vertex graph — one assignment at a time vs. all 128 simultaneously. Toggle bits, measure qubits, and build intuition for superposition and QAOA.",
      "keywords": [
        "vertex coloring",
        "bits vs qubits",
        "superposition",
        "QAOA",
        "graph problems"
      ],
      "metadata_source": "visualization-gallery.html (widgets-as-html branch)"
    }
  }
}