How to Use the IBM Quantum MCP in LlamaIndex

Q: How do I ingest IBM Quantum job histories into a LlamaIndex vector store?

Use BasicMCPClient to connect to the server, then fetch historical runs using listjobs. Pass these results to your LlamaIndex document ingest pipeline to vectorize and store the quantum execution data.

Q: Can a LlamaIndex agent select the best physical IBM Quantum backend automatically?

Yes, the agent can call listbackends and getbackenddetails to inspect active processors. By indexing these technical specs, your LlamaIndex agent matches your circuit's topological requirements with the best available physical hardware.

Q: How do I set up the IBM Quantum MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius endpoint URL. Convert the server's capabilities using McpToolSpec to expose the quantum tools directly to your FunctionAgent.

Q: What tools are available for monitoring active quantum jobs?

Your agent uses getjobdetails to check status and getjobresult to pull down the final data. If a job is taking too long on a crowded processor, the agent can call canceljob to free up your compute credits.

Index quantum job results directly into your LlamaIndex vector stores for grounded retrieval.

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Create your Vinkius account to connect IBM Quantum to LlamaIndex 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 LlamaIndex

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Index quantum hardware specs in LlamaIndex

This MCP Server lets your LlamaIndex agent query `list_backends` to gather real-time hardware configurations. The agent fetches gate fidelities and qubit counts using `get_backend_details` and indexes this data immediately. Your RAG applications query this local index instead of hitting the IBM API repeatedly. This prevents rate limiting and ensures your agent always selects the optimal processor for your circuit depth.

Grounded quantum job retrieval

The `get_job_result` tool provides the raw quantum circuit outcomes that LlamaIndex indexes to create a searchable history of your experiments. When you ask about past runs, the agent performs semantic search over actual execution data rather than hallucinating circuit outcomes. The agent calls `list_jobs` to pull historical records and updates your vector store automatically. This links natural language queries directly to physical quantum job IDs.

Submit circuits using the LlamaIndex MCP Server

The `submit_job` tool allows your agent to run new circuits directly from your indexed notebooks. The agent reads your target circuit, searches your vector store for similar past runs, and decides whether to submit a new job. Using `McpToolSpec` wrapped in a `FunctionAgent`, your pipeline runs asynchronously. The agent monitors execution via `get_job_details` and writes the final quantum state vector back into your index.

Setup guide

Set up IBM Quantum MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all IBM Quantum MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to IBM Quantum tools.",
)
response = await agent.run("List recent IBM Quantum data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by IBM Quantum. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about IBM Quantum MCP in LlamaIndex

Use `BasicMCPClient` to connect to the server, then fetch historical runs using `list_jobs`. Pass these results to your LlamaIndex document ingest pipeline to vectorize and store the quantum execution data.

Yes, the agent can call `list_backends` and `get_backend_details` to inspect active processors. By indexing these technical specs, your LlamaIndex agent matches your circuit's topological requirements with the best available physical hardware.

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius endpoint URL. Convert the server's capabilities using `McpToolSpec` to expose the quantum tools directly to your `FunctionAgent`.

Your agent uses `get_job_details` to check status and `get_job_result` to pull down the final data. If a job is taking too long on a crowded processor, the agent can call `cancel_job` to free up your compute credits.

Circuit payloads and job results pass through an isolated, zero-trust V8 sandbox on Vinkius. No data is cached on the host, ensuring your proprietary quantum algorithms remain private during MCP transmissions.

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