Load IBM Quantum data to DuckDB

IBM Quantum Services provides a REST API to access quantum processors, submit jobs, and manage sessions. Everything needed to build a working IBM Quantum → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your IBM Quantum to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from IBM Quantum to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the IBM Quantum API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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IBM Quantum API at a glance

These values come from the IBM Quantum API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the IBM Quantum API?

Authentication requires an IBM Cloud IAM bearer token, generated by exchanging an API key via the IAM Identity Services API. Requests must include the 'Authorization: Bearer ' and 'Service-CRN: ' headers, along with an 'IBM-API-Version' header.

1. Get your credentials

To obtain your credentials, navigate to the IBM Quantum Platform dashboard. Under your account settings or the dashboard home, locate the section for API keys to create a new 44-character API key. Copy this key immediately, as it will not be visible again. If necessary, also locate your Instance CRN from the Instances page in the dashboard menu. Use the API key to generate a short-lived IAM bearer token via a POST request to the IBM Cloud IAM Identity Services API before calling other REST API endpoints.

2. Add them to .dlt/secrets.toml

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[sources.ibm_quantum_source]
ibm_quantum_api_key = "your_44_character_api_key_here"
ibm_quantum_service_crn = "your_instance_crn_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What IBM Quantum data can I load into DuckDB?

These are the IBM Quantum endpoints dlt can load into DuckDB:

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How do I load only new IBM Quantum records?

IBM Quantum exposes offset on v1/jobs, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "jobs", "endpoint": {
    "path": "v1/jobs",
    "data_selector": "jobs",
    "incremental": {"cursor_path": "offset", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated IBM Quantum pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading usage and backends from the IBM Quantum API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def ibm_quantum_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://quantum.cloud.ibm.com/api/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "v1/jobs", "data_selector": "jobs"}},
            {"name": "backends", "endpoint": {"path": "backends"}}
        ],
    }
    yield from rest_api_resources(config)


def load_ibm_quantum_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ibm_quantum_pipeline",
        destination="duckdb",
        dataset_name="ibm_quantum_data",
    )
    load_info = pipeline.run(ibm_quantum_source())
    print(load_info)


if __name__ == "__main__":
    load_ibm_quantum_to_duckdb()

Run it with python ibm_quantum_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query IBM Quantum data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("ibm_quantum_pipeline").dataset()
df = data.jobs.df()
print(df.head())

SQL:

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SELECT * FROM ibm_quantum_data.jobs LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the IBM Quantum to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw IBM Quantum loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load IBM Quantum data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack