IBM Quantum MCP. Manage the full quantum job lifecycle via your AI agent.
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IBM Quantum connects your AI agent to the IBM Quantum platform. It lets you manage the full lifecycle of quantum computation jobs—from submitting the initial circuit to retrieving the final results.
You can also list available quantum backends, check job status, and cancel jobs if they aren't needed. It's a direct way to run quantum algorithms through your AI client.
What your AI agents can do
Cancel job
Stops a running or pending quantum job using its unique ID.
Get backend details
Retrieves specific operational information for a chosen quantum hardware device.
Get job details
Gets the current status and metadata for a specific quantum job.
Your agent can use get_job_details and list_jobs to track the status and metadata of quantum jobs.
The agent uses submit_job to send a quantum computation job to the available backends.
You can use list_providers and list_backends to see what quantum hardware is available and what its current specs are.
Once a job is done, the agent calls get_job_result to pull the actual quantum output data.
If a job is finished or failed, you can call cancel_job to stop it and manage resource usage.
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Supported MCP Clients
Gemini IBM Quantum MCP Server: 8 Tools for Quantum Computing
These tools give your AI agent the power to manage every step of quantum job execution, from listing available hardware to retrieving final results.
019d75b7cancel job
Stops a running or pending quantum job using its unique ID.
019d75b7get backend details
Retrieves specific operational information for a chosen quantum hardware device.
019d75b7get job details
Gets the current status and metadata for a specific quantum job.
019d75b7get job result
Fetches the final computed output data for a job that has finished running.
019d75b7list backends
Lists all quantum hardware devices available for job submission.
019d75b7list jobs
Provides an overview list of all quantum jobs currently tracked.
019d75b7list providers
Lists all available IBM Quantum providers and resource types.
019d75b7submit job
Starts a new quantum computation job on a specified backend.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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What you can do with this MCP connector
IBM Quantum connects your AI agent right to the IBM Quantum platform. It lets you manage the full lifecycle of quantum computation jobs—from submitting the initial circuit to pulling the final results. You'll use this to run quantum algorithms straight through your agent. You can list all available IBM Quantum providers using list_providers and see all the quantum hardware devices with list_backends.
You can then get specific operational information for any chosen device with get_backend_details. When you're ready to run a computation, your agent uses submit_job to send the job to a specified backend. To keep tabs on what's happening, your agent can list all existing quantum jobs using list_jobs or check the detailed status and metadata for a single job with get_job_details.
Once a job finishes, you pull the actual quantum output data using get_job_result. If a job fails or you just don't need it anymore, your agent can stop the job using cancel_job.
How IBM Quantum MCP Works
- 1 First, use
list_providersorlist_backendsto identify the specific quantum hardware you want to use. - 2 Next, the agent calls
submit_job, providing the job definition and the chosen backend, which starts the quantum computation. - 3 Finally, the agent polls the job using
get_job_detailsuntil the status is 'completed,' then callsget_job_resultto get the data.
The bottom line is, your agent handles the entire quantum job workflow, letting you submit and monitor jobs without writing boilerplate API code.
Who Is IBM Quantum MCP For?
This is for quantum software engineers, computational scientists, and research leads. You're the person who spends days writing circuits and debugging why the results are weird. You need reliable, structured access to complex hardware APIs so your AI agent can automate the tedious job tracking and resource management parts of your workflow.
They use this to automate the submission and monitoring of quantum circuits, turning a manual, multi-step process into a simple chat command.
They use it to check the status of large batches of jobs across multiple backends, saving hours of manual dashboard checking.
They use it to build reliable pipelines that submit, track, and clean up quantum resources automatically, ensuring no jobs are left hanging.
What Changes When You Connect
- Track job status instantly. Instead of logging into a dashboard to check if your circuit ran, your agent uses
get_job_detailsto tell you the status right now. - Know your hardware limits. Use
list_providersandlist_backendsto see exactly what quantum devices are available before you write a single line of code. - Stop wasted compute time. If a job is finished or you change your mind,
cancel_jobstops it immediately, preventing unnecessary resource usage. - Retrieve results without hassle. Once the job is done,
get_job_resultpulls the final output data directly, skipping manual downloads and copy-pasting. - Build reliable pipelines. The agent can chain
submit_jobwith status checks and result retrieval, making your whole workflow automatic and repeatable. - See all your jobs in one place.
list_jobsgives you a quick, high-level overview of every job you've submitted.
Real-World Use Cases
Debugging a Failed Circuit
A researcher runs a complex circuit, but it fails. Instead of manually checking logs, they ask their agent to use get_job_details and get_job_result on the specific job ID. The agent reports the failure status and pulls the error log, letting them debug the circuit instantly.
Batch Processing Multiple Jobs
A scientist needs to run the same circuit on three different backends. The agent first uses list_backends to get the list, then loops through the list, calling submit_job for each one. It then monitors all three jobs using get_job_details until all are done.
Resource Cleanup After Testing
You finish a proof-of-concept job but forget to clean up the resources. You simply ask your agent to use cancel_job on the job ID. The job stops, and you're done with the resource, saving credits.
Comparing Provider Capabilities
A developer needs to decide which quantum provider is best. They start by calling list_providers to see options, then use list_backends and get_backend_details to compare key specs before finally calling submit_job on the chosen platform.
The Tradeoffs
Checking status manually
You run a job, then open the web portal, navigate to the job ID, and click 'Status' every five minutes. This is slow, and you risk missing status updates or getting stuck in a slow UI.
→
Let your agent handle it. Use get_job_details to poll the status programmatically. This keeps the tracking logic inside your workflow and is much more reliable.
Assuming job completion
You submit a job and immediately try to download the result. The API fails because the job is still running, forcing you to restart the whole process.
→
Always check the status first. Use get_job_details to verify the job status is 'completed' before calling get_job_result. This prevents useless API calls.
Ignoring resource management
You submit a job for testing and walk away, forgetting that it's still consuming credits. You only realize this when the bill comes.
→
Use cancel_job as soon as the job is no longer needed. It's the clean way to shut down compute resources and manage costs.
When It Fits, When It Doesn't
Use this server if your workflow requires managing the full, multi-step lifecycle of quantum computing jobs. You need to submit circuits, monitor them in real-time, and reliably retrieve the final output data.
Don't use this if you just need to read simple, static data, like a list of available backends. In that case, a basic list-only tool might suffice. You need the ability to track state changes and perform actions (submit_job, cancel_job).
If your main goal is simply data storage, you need a dedicated database connector, not this quantum job server. This server is for computation, not storage.
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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The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Tracking job status through dashboards is a massive time sink.
Today, running a quantum circuit means jumping between several dashboards. You submit the job, then you have to copy the job ID, paste it into the status screen, and then wait. Every status change—from 'queued' to 'running' to 'failed'—requires you to manually refresh the page and confirm it. It's clicks, copy-pasting, and context switching.
With the IBM Quantum MCP Server, your agent handles the whole sequence. You tell it to run the circuit, and it uses `get_job_details` to poll the status. The agent reports back the status directly, no manual refreshing required. You just get the answer.
IBM Quantum MCP Server: Get job results and manage resources.
You don't have to manually track the backend details or check job IDs across multiple tabs. The agent uses `list_backends` to find the best hardware and `submit_job` to send the job. It then uses `get_job_result` to fetch the output, consolidating several manual steps into one call.
It's about removing the hand-holding. You tell your AI agent to run the job, and it handles the submission, monitoring, and retrieval steps for you. Period.
Common Questions About IBM Quantum MCP
How do I use the `submit_job` tool with IBM Quantum? +
You pass the circuit definition and the target backend ID to submit_job. This starts the computation and returns a job ID. You then use get_job_details with that ID to monitor its progress.
Can I use `cancel_job` to stop a job? +
Yes, cancel_job stops any job running or pending, provided you give it the correct job ID. This is key for cost control when testing.
What is the difference between `list_jobs` and `get_job_details`? +
list_jobs gives you a quick, high-level list of all job IDs. get_job_details gives you the deep, current status and metadata for one specific job ID.
How do I find available quantum hardware? +
You first run list_providers to see available resource types. Then, use list_backends to get the specific device IDs you can submit jobs to.
Do I need to call `get_job_result` after `get_job_details`? +
No. get_job_details tells you the status. You only call get_job_result once the status confirms the job is complete and the output is available.
How can I check the health of a quantum device using `get_backend_details`? +
The get_backend_details tool provides operational data for a specific quantum machine. It returns metrics like connectivity and current uptime, which helps you assess the device's readiness before submitting a job.
What should I do if a job fails, and how does `get_job_result` help? +
The get_job_result tool retrieves the final output and associated error logs for a completed job. This allows your agent to pinpoint the exact failure reason and start debugging the quantum circuit.
How do I manage multiple quantum jobs using `list_jobs` and `list_providers`? +
You use list_providers to see which quantum services are available. Then, list_jobs lets you see all your active and completed jobs across those providers, giving you a full job history overview.
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Connect this server to Cursor, Claude, VS Code, and more.
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