Ein Model Context Protocol (MCP)-Server, mit dem Claude Desktop Quantenschaltkreise mithilfe von Qiskit und Qiskit Aer erstellen, visualisieren und simulieren kann.
Sobald die Verbindung zu Claude Desktop hergestellt ist, können Sie natürliche Unterhaltungen führen wie:
"Erstelle einen Bell-Zustand und simuliere ihn mit 1024 Durchläufen"
"Zeige mir das GHZ-Schaltkreisdiagramm"
"Führe den Bell-Zustand mit hohem Rauschen aus und erkläre, was sich geändert hat"
"Erstelle einen benutzerdefinierten Schaltkreis aus diesem QASM-String"
Claude ruft die zugrunde liegenden Tools automatisch auf – kein manueller Tool-Aufruf erforderlich.
Related MCP server: Qiskit MCP Server
3 Tools: create_circuit, visualize_circuit, run_simulation
5 voreingestellte Schaltkreise: Bell-Zustand, GHZ-3, Superposition, Deutsch-Jozsa, Random-4
Benutzerdefiniertes QASM: Akzeptiert jeden gültigen OpenQASM 2.0-String
Rauschmodellierung: Ideal, geringes Rauschen (p=0.001), hohes Rauschen (p=0.01) durch depolarisierenden Fehler
1 Ressource: resource://noise-presets — listet verfügbare Rauschkonfigurationen auf
1 Prompt: simulate_walkthrough — Vorlage für eine geführte Unterhaltung
Python 3.13+
Anaconda oder ein beliebiger Python-Umgebungsmanager
git clone <repo-url>
cd qiskit-sim-mcppip install -e .Dies installiert mcp[cli], qiskit und qiskit-aer in Ihrer Python-Umgebung.
python -c "
import asyncio
from quantum_mcp_demo.server import mcp
tools = asyncio.run(mcp.list_tools())
resources = asyncio.run(mcp.list_resources())
prompts = asyncio.run(mcp.list_prompts())
print('Tools:', [t.name for t in tools])
print('Resources:', [r.uri for r in resources])
print('Prompts:', [p.name for p in prompts])
"Erwartete Ausgabe:
Tools: ['create_circuit', 'visualize_circuit', 'run_simulation']
Resources: [AnyUrl('resource://noise-presets')]
Prompts: ['simulate_walkthrough']uv run mcp dev src/quantum_mcp_demo/server.pyÖffnen Sie http://localhost:6274 in Ihrem Browser. Verwenden Sie den Tab Tools, um jedes Tool manuell aufzurufen, bevor Sie eine Verbindung zu Claude Desktop herstellen.
Suchen Sie Ihre Claude Desktop-Konfigurationsdatei:
Betriebssystem | Pfad |
macOS |
|
Windows |
|
Fügen Sie diesen Eintrag innerhalb des mcpServers-Objekts hinzu:
{
"mcpServers": {
"qiskit-sim-mcp": {
"command": "/opt/anaconda3/bin/python",
"args": [
"-m",
"quantum_mcp_demo.server"
]
}
}
}Hinweis: Ersetzen Sie
/opt/anaconda3/bin/pythondurch Ihren tatsächlichen Python-Pfad. Führen Siewhich pythonaus, um ihn zu finden.
Beenden Sie Claude Desktop vollständig (Cmd+Q unter macOS) und öffnen Sie es dann erneut. In einer neuen Unterhaltung bestätigt das Hammer-Symbol (🔨) in der Chateingabe, dass die MCP-Tools geladen sind.
"Erstelle einen Bell-Zustandsschaltkreis, zeige mir das Diagramm und simuliere ihn dann 1024 Mal mit idealem und hohem Rauschen."
Claude wird Folgendes aufrufen:
create_circuit(preset="bell") → erhält eine circuit_id
visualize_circuit(circuit_id=...) → zeigt das H + CNOT-Gatter-Diagramm
run_simulation(circuit_id=..., shots=1024, noise_preset="ideal") → ~50% |00⟩, ~50% |11⟩
run_simulation(circuit_id=..., shots=1024, noise_preset="high_noise") → verschlechterte Zählwerte, |01⟩ und |10⟩ erscheinen
"Führe diesen Schaltkreis aus: OPENQASM 2.0; include "qelib1.inc"; qreg q[2]; creg c[2]; h q[0]; cx q[0],q[1]; measure q -> c;"
Claude analysiert den QASM-String und führt ihn durch dieselbe Simulations-Pipeline aus.
"Vergleiche den GHZ-Zustand unter idealem vs. hohem Rauschen – was bewirkt das Rauschen bei der Verschränkung?"
src/quantum_mcp_demo/
├── server.py # MCP server entry point
├── circuits/
│ ├── presets.py # 5 named preset circuits
│ └── store.py # In-memory circuit storage (UUID-keyed)
├── tools/
│ ├── create_circuit.py # Tool: create from preset or QASM
│ ├── visualize.py # Tool: HTML diagram + gate JSON
│ └── simulate.py # Tool: AerSimulator + noise modeling
├── resources/
│ └── noise_presets.py # Resource: noise configuration list
├── prompts/
│ └── walkthrough.py # Prompt: guided simulation conversation
└── utils/
├── serialization.py # numpy → Python int conversion
└── errors.py # MCP error response builderVoreinstellung | Qubits | Demonstriert | ||
| 2 | Verschränkung — 50/50-Aufteilung auf ` | 00⟩ | 11⟩` |
| 3 | Multi-Qubit-Verschränkung | ||
| 1 | Gleiche Superposition durch Hadamard | ||
| 3 | Quantenalgorithmus — Interferenz | ||
| 4 | Rausch-Stresstest — zufällige Gatter mit festem Seed |
Voreinstellung | Depolarisierend p | Beschreibung |
| keine | Perfekte Simulation |
| 0.001 | Realistisches Gerät der nahen Zukunft |
| 0.01 | Aggressives Rauschen — sichtbare Verschlechterung |
Claude Desktop zeigt das Hammer-Symbol nicht an
Beenden Sie Claude Desktop vollständig und starten Sie es nach dem Bearbeiten der Konfiguration neu
Überprüfen Sie, ob das Konfigurations-JSON gültig ist (keine nachgestellten Kommas)
Überprüfen Sie den Python-Pfad: which python
python -m quantum_mcp_demo.server schlägt fehl
Stellen Sie sicher, dass Sie pip install -e . im Projektstammverzeichnis ausgeführt haben
Überprüfen Sie die Python-Version: python --version (erfordert 3.13+)
Port-Konflikt beim Ausführen des MCP Inspector
kill -9 $(lsof -t -i :6274) 2>/dev/null; kill -9 $(lsof -t -i :6277) 2>/dev/nullSimulation liefert unerwartete Zählwerte
Abweichungen bei der Anzahl der Durchläufe werden automatisch erkannt und als isError: true zurückgegeben
Versuchen Sie, die shots (z. B. 512) zu reduzieren, wenn Fehler auftreten
Create a quantum circuit from a named preset or an OpenQASM 2.0 string. Provide exactly one of: preset (bell|ghz_3|superposition|deutsch_jozsa|random_4) or qasm.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | ||
| qasm | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full burden. It only states 'create', which implies mutation, but does not disclose side effects, permissions, rate limits, or whether the operation is reversible. Minimal behavioral context beyond the action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose, and contains no unnecessary words. Every sentence adds value, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (implied), the description need not explain return values. It covers the essential parameter modes and preset list. However, it lacks error handling guidance (e.g., what happens if both or neither are provided) and interaction with sibling tools, but the description remains adequate for a simple creation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description compensates by specifying that exactly one parameter must be provided and listing the preset options. It clarifies that qasm is an OpenQASM 2.0 string, adding semantic value. However, it does not detail the format or constraints further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a quantum circuit from a named preset or an OpenQASM 2.0 string, listing specific preset options. This distinguishes it from siblings (run_simulation, visualize_circuit) which perform different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for circuit creation but does not explicitly state when to use this tool over alternatives or provide exclusions. It lacks guidance on prerequisites or context for using presets versus QASM.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a stored circuit on AerSimulator with optional depolarizing noise. noise_preset: ideal | low_noise | high_noise. Returns counts, probabilities, metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| circuit_id | Yes | ||
| shots | Yes | ||
| noise_preset | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions returns (counts, probabilities, metadata) but does not state whether the operation is read-only, has side effects, or requires specific permissions. The behavior is partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the core purpose, and presents noise options succinctly. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 required parameters, no annotations, and an output schema, the description covers the platform and noise but omits parameter constraints and fails to distinguish output structure beyond mentioning three fields. It is adequate but not rich.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It explains noise_preset with enumerated values, but leaves circuit_id and shots undocumented (no hint on circuit_id source or shots default/range). Only 1 of 3 parameters gains meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a stored circuit on AerSimulator with optional noise, and the verb 'Run' paired with 'stored circuit' distinguishes it from sibling tools 'create_circuit' and 'visualize_circuit'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (run a stored circuit) and provides specific noise preset options (ideal, low_noise, high_noise), but does not explicitly exclude alternatives or state prerequisites like needing a pre-existing circuit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Visualize a stored circuit as HTML and a structured gate list for narration.
| Name | Required | Description | Default |
|---|---|---|---|
| circuit_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the operation is read-only, any side effects, or prerequisites (e.g., circuit must exist). This leaves uncertainty about its safety and failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no unnecessary words. It front-loads the key action and outputs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and no annotations, the description is adequate but incomplete. It fails to specify error conditions, the format of the gate list, or prerequisite existence checks for the circuit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'circuit_id' has 0% schema description coverage, and the description does not add any meaning beyond the schema's title. No guidance on what constitutes a valid circuit_id or how to obtain it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'visualize' and resource 'stored circuit', and specifies the output as HTML and a gate list for narration. This distinguishes it from siblings 'create_circuit' and 'run_simulation'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is provided. Usage is implied by the context of having a stored circuit to visualize, but alternatives like run_simulation are not differentiated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0create_circuitrun_simulationvisualize_circuitScored across 3 tools
Each tool addresses a distinct phase of quantum circuit workflow: creation, simulation, and visualization. No overlap in functionality.
All tools follow a consistent verb_noun convention (create_circuit, run_simulation, visualize_circuit), making their purpose immediately clear.
Three tools cover the core lifecycle adequately. While more tools (e.g., list, delete) could be added, the current count is reasonable for a focused server.
The tools enable creating, running with noise, and visualizing circuits. Missing operations like listing or deleting circuits are minor gaps but do not severely hinder basic usage.
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