Expert knowledge for Azure Quantum development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when running Q# jobs on IonQ/Quantinuum/Rigetti, managing quotas, RBAC access, hybrid jobs, or resource estimation, and other Azure Quantum related development tasks. Not for Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning), Azure Virtual Machines (use azure-virtual-machines).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-quantum
This skill provides expert guidance for Azure Quantum. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
> IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., security.md), use read_file on the linked reference file
> IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|----------|-------|-------------|
| Troubleshooting | L37-L44 | Troubleshooting Azure Quantum provider issues: diagnosing job failures and support/escalation policies and limits for IonQ, Quantinuum, and Rigetti hardware on Azure Quantum. |
| Best Practices | L45-L49 | Tools and techniques for testing, debugging, and validating quantum programs with the Azure Quantum Development Kit (QDK), including simulators, logging, and troubleshooting. |
| Decision Making | L50-L56 | Guidance on Azure Quantum costs, provider pricing and regions, workspace migration, choosing Q# dev tools, and planning quantum-safe cryptography with the resource estimator. |
| Architecture & Design Patterns | L57-L61 | Guidance on designing hybrid quantum-classical workflows in Azure Quantum, including architecture options, orchestration patterns, and when to offload tasks to quantum hardware. |
| Limits & Quotas | L62-L68 | Managing Azure Quantum quotas, job/session limits, timeouts, and Rigetti-specific hardware constraints and target capabilities. |
| Security | L69-L79 | Managing secure access to Azure Quantum workspaces: RBAC and access control, bulk user assignment, ARM locks, managed identities, service principals, and secure handling of access keys. |
| Configuration | L80-L91 | Configuring Azure Quantum workspaces, QDK tools, simulators, and hardware targets, plus setting up and customizing Quantum Resource Estimator models and outputs. |
| Integrations & Coding Patterns | L92-L104 | Integrating quantum frameworks (Q#, OpenQASM, QIR, Qiskit, Cirq, Pulser) with Azure Quantum, configuring simulators/noise models, visualization, hybrid jobs, and resource estimation. |
| Deployment | L105-L109 | Deploying Azure Quantum workspaces with Bicep and running/submitting Q# quantum programs from VS Code to Azure Quantum backends |
| Topic | URL |
|-------|-----|
| Diagnose and resolve common Azure Quantum issues | https://learn.microsoft.com/en-us/azure/quantum/azure-quantum-common-issues |
| Support and escalation policy for IonQ on Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/provider-support-ionq |
| Support policy for Quantinuum on Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/provider-support-quantinuum |
| Support policy for Rigetti on Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/provider-support-rigetti |
| Topic | URL |
|-------|-----|
| Test and debug quantum programs with QDK tools | https://learn.microsoft.com/en-us/azure/quantum/testing-debugging |
| Topic | URL |
|-------|-----|
| Migrate Azure Quantum workspace data between regions | https://learn.microsoft.com/en-us/azure/quantum/migration-guide |
| Compare Azure Quantum provider pricing plans | https://learn.microsoft.com/en-us/azure/quantum/pricing |
| Check regional availability of Azure Quantum providers | https://learn.microsoft.com/en-us/azure/quantum/provider-global-availability |
| Topic | URL |
|-------|-----|
| Choose hybrid quantum computing architectures in Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/hybrid-computing-overview |
| Topic | URL |
|-------|-----|
| Review and manage Azure Quantum usage quotas | https://learn.microsoft.com/en-us/azure/quantum/azure-quantum-quotas |
| Manage Azure Quantum sessions and avoid timeouts | https://learn.microsoft.com/en-us/azure/quantum/how-to-work-with-sessions |
| Rigetti provider targets and hardware limits in Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/provider-rigetti |
| Topic | URL |
|-------|-----|
| Bulk assign Azure Quantum workspace access via CSV | https://learn.microsoft.com/en-us/azure/quantum/bulk-add-users-to-a-workspace |
| Protect Azure Quantum resources with ARM locks | https://learn.microsoft.com/en-us/azure/quantum/how-to-set-resource-locks |
| Share Azure Quantum workspace using RBAC roles | https://learn.microsoft.com/en-us/azure/quantum/how-to-share-access-quantum-workspace |
| Configure Azure Quantum workspace access control | https://learn.microsoft.com/en-us/azure/quantum/manage-workspace-access |
| Authenticate to Azure Quantum using managed identity | https://learn.microsoft.com/en-us/azure/quantum/optimization-authenticate-managed-identity |
| Authenticate to Azure Quantum using service principals | https://learn.microsoft.com/en-us/azure/quantum/optimization-authenticate-service-principal |
| Manage Azure Quantum workspace access keys securely | https://learn.microsoft.com/en-us/azure/quantum/security-manage-access-keys |
| Topic | URL |
|-------|-----|
| Configure Azure Quantum workspaces with Azure CLI | https://learn.microsoft.com/en-us/azure/quantum/how-to-manage-quantum-workspaces-with-the-azure-cli |
| Use the QDK neutral atom device visualizer | https://learn.microsoft.com/en-us/azure/quantum/how-to-use-neutral-atom-visualizer |
| Install and configure QDK quantum simulators | https://learn.microsoft.com/en-us/azure/quantum/install-qdk-quantum-simulators |
| Configure and use IonQ targets in Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/provider-ionq |
| Configure hardware architecture models for the Quantum resource estimator | https://learn.microsoft.com/en-us/azure/quantum/qre-build-architecture-models |
| Define error correction and magic state models for resource estimation | https://learn.microsoft.com/en-us/azure/quantum/qre-build-error-correction-models |
| Build custom application models for the Quantum resource estimator | https://learn.microsoft.com/en-us/azure/quantum/qre-custom-applications |
| Access and customize Quantum resource estimator output | https://learn.microsoft.com/en-us/azure/quantum/qre-estimation-results |
| Topic | URL |
|-------|-----|
| Connect to Azure Quantum workspace via qdk.azure | https://learn.microsoft.com/en-us/azure/quantum/how-to-connect-workspace |
| Visualize Q# and OpenQASM circuits with QDK | https://learn.microsoft.com/en-us/azure/quantum/how-to-visualize-circuits |
| Run integrated hybrid quantum jobs with Adaptive RI in Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/hybrid-computing-integrated |
| Configure neutral atom noise models with QDK Python APIs | https://learn.microsoft.com/en-us/azure/quantum/neutral-atom-noise-models |
| Run OpenQASM programs with Azure Quantum QDK | https://learn.microsoft.com/en-us/azure/quantum/qdk-openqasm-integration |
| Build and configure QDK simulator noise models in Python | https://learn.microsoft.com/en-us/azure/quantum/qdk-simulator-noise-models |
| Create application models from quantum frameworks for resource estimation | https://learn.microsoft.com/en-us/azure/quantum/qre-supported-applications |
| Submit Cirq circuits to Azure Quantum with QDK | https://learn.microsoft.com/en-us/azure/quantum/quickstart-microsoft-cirq |
| Submit QIR, OpenQASM, and Pulser circuits to Azure Quantum | https://learn.microsoft.com/en-us/azure/quantum/quickstart-microsoft-provider-format |
| Topic | URL |
|-------|-----|
| Deploy Azure Quantum workspaces using Bicep templates | https://learn.microsoft.com/en-us/azure/quantum/how-to-manage-quantum-workspaces-using-bicep |
| Submit and run Q# programs on Azure Quantum from VS Code | https://learn.microsoft.com/en-us/azure/quantum/how-to-submit-jobs |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take microsoftdocs/azure-quantum from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.