How to Use the IBM Quantum MCP in Pydantic AI

Q: Can Pydantic AI manage multiple quantum providers on IBM Quantum?

Yes. The agent calls listproviders to discover your active hubs and groups. It validates the provider list against its schema, allowing the agent to safely route jobs.

Q: How do I handle asynchronous quantum job polling in Pydantic AI?

Your agent calls getjobdetails inside a polling loop to check the execution status. Because the status payload is strictly validated, your agent can reliably decide when to call getjobresult.

Run type-safe quantum execution pipelines on IBM Quantum with Pydantic AI runtime validation.

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Connect IBM Quantum MCP to Pydantic AI

Create your Vinkius account to connect IBM Quantum to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup IBM Quantum with Pydantic AI

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Query validated quantum backends in Pydantic AI

`list_backends` fetches the available IBM Quantum hardware and simulators, immediately validating the JSON response against Pydantic AI's strict type schemas. This type-safe discovery prevents runtime crashes in Pydantic AI caused by unexpected API schema changes from the IBM quantum provider.

Execute validated quantum jobs with Pydantic AI

`submit_job` sends your compiled quantum circuits to IBM Quantum with full runtime schema validation on the parameters via this MCP Server. If the IBM job encounters an unrecoverable queue delay, the Pydantic AI agent calls `cancel_job` to abort the run safely.

Process verified quantum results in Pydantic AI

`get_job_result` retrieves the IBM execution counts and measurement data, parsing them directly into strongly-typed Pydantic AI models. By calling `list_jobs` and `list_providers`, your Pydantic AI agent manages historical IBM runs and provider allocations with complete type safety.

Setup guide

Set up IBM Quantum MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "ibm-quantum-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to IBM Quantum tools.",
)

result = await agent.run("List recent IBM Quantum transactions")
print(result.output)

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Common questions about IBM Quantum MCP in Pydantic AI

Use the unified MCPToolset pointing to your Vinkius HTTP endpoint. Pass this toolset into the toolsets parameter of your Pydantic AI Agent.

If the API returns a schema that violates your models, Pydantic AI raises a validation error immediately. This prevents your agent from operating on bad data or hallucinating qubit properties.

Yes. The agent calls list_providers to discover your active hubs and groups. It validates the provider list against its schema, allowing the agent to safely route jobs.

Your agent calls get_job_details inside a polling loop to check the execution status. Because the status payload is strictly validated, your agent can reliably decide when to call get_job_result.

Your circuit designs and IBM Quantum credentials are never exposed directly to the LLM or stored long-term. They are processed in ephemeral V8 isolates on Vinkius, meaning your Pydantic AI agent communicates via a single secure token to the MCP Server, isolating your core API keys.

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