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QPanda3 Runtime MCP Server

by OriginQ

create_circuit_observable_binding_tool

Bind multiple quantum circuits to multiple observables for multi-objective decision analysis. This tool creates a program set to evaluate different quantum strategies against various objective dimensions in quantum computing scenarios.

Instructions

Create a CircuitObservableBinding for multi-objective quantum decision making.

This creates a program set that manages multiple quantum circuits and multiple observables for multi-target decision paradigms. This is useful for quantum multi-objective decision scenarios where you need to evaluate multiple strategies (circuits) against multiple objective dimensions (observables).

Args: circuits: List of quantum circuits in QProg/QCircuit/OriginIR format. Each circuit represents a different strategy to evaluate. observables: List of observables (Hamiltonians). Each observable represents a different objective dimension to measure. Supports dict format: {"Z0 Z1": 0.5, "X0": -0.3}

Returns: Dictionary containing: - status: "success" or "error" - binding_id: Unique ID for referencing this binding (use with add_product_rule_tool, add_zip_rule_tool, and estimate_with_binding_tool) - num_circuits: Number of circuits in the binding - num_observables: Number of observables in the binding - message: Status description

Example: # Create binding with 3 circuits and 2 observables binding = create_circuit_observable_binding_tool( circuits=[ "QINIT 2\nCREG 2\nH q[0]\nCNOT q[0],q[1]", "QINIT 2\nCREG 2\nX q[0]\nCNOT q[0],q[1]", "QINIT 2\nCREG 2\nZ q[0]\nSWAP q[0],q[1]" ], observables=[ {"Z0 Z1": 1.0}, {"X0": 0.5} ] ) binding_id = binding["binding_id"]

Note: After creating a binding, use add_product_rule_tool or add_zip_rule_tool to define the combinations, then use estimate_with_binding_tool to submit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
circuitsYes
observablesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by explaining the tool creates a binding with a unique ID for referencing, describes the return structure, and mentions follow-up tools needed. It doesn't cover error conditions, performance characteristics, or authentication requirements, but provides substantial behavioral context for a creation operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, args, returns, example, note) and every sentence adds value. It could be slightly more concise in the opening paragraph but remains efficient overall with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of quantum multi-objective decision scenarios, 2 parameters with 0% schema coverage, no annotations, but with output schema, the description provides excellent completeness. It explains the purpose, parameters, return values, workflow context, and includes a practical example - covering everything needed to understand and use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for 2 parameters, the description fully compensates by explaining both parameters in detail: 'circuits' as 'List of quantum circuits in QProg/QCircuit/OriginIR format' where 'Each circuit represents a different strategy to evaluate', and 'observables' as 'List of observables (Hamiltonians)' where 'Each observable represents a different objective dimension to measure' with format examples. This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates a 'CircuitObservableBinding' for 'multi-objective quantum decision making' and specifies it manages 'multiple quantum circuits and multiple observables'. It distinguishes this from siblings like add_product_rule_tool and estimate_with_binding_tool by explaining this is the initial creation step before those operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('for quantum multi-objective decision scenarios where you need to evaluate multiple strategies against multiple objective dimensions') and provides clear guidance on what to do after creation ('use add_product_rule_tool or add_zip_rule_tool to define the combinations, then use estimate_with_binding_tool to submit'). It effectively distinguishes this from sibling tools in the workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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