Quantum Expectations

Compute Required Error Rate (Inverse)

compute_required_error_rate

Read-onlyIdempotent

Inverse of compute_expectation. Given a circuit (numQubits, compDepth) and an acceptable effective error rate, return the required per-gate logical error rate and, for every EC option (no-EC, surface-code per distance, every qLDPC code), the required physical error rate plus the subset of current SOTA hardware that already qualifies. Answers "what hardware do I need to run this algorithm?".

Input Schema

TableJSON Schema

Name	Required	Description
`compDepth`	Yes	Circuit depth d (sequential 2-qubit gate layers). Accepts values up to 1e13.
`numQubits`	Yes	Number of logical qubits n in the circuit.
`acceptableErrorRatePercent`	Yes	Upper bound on the effective error rate, as a percent. Default website convention is 33 (i.e. ≤33% effective error is "acceptable").

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. Description adds behavioral context by specifying the inverse computation and the breadth of outputs across every EC option and current SOTA hardware. It does not mention model assumptions or performance caveats, but these are not critical given the annotation coverage.

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

Conciseness5/5

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

Two sentences, first front-loads the core inverse relationship, second compactly lists outputs and the question answered. No filler or redundancy.

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

Completeness4/5

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

Given no output schema, the description helpfully enumerates the return classes (logical error rate, physical error rates per EC option, qualifying hardware). It could add more detail on output structure or thresholds, but the essentials are covered for a compute tool with documented inputs.

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

Parameters3/5

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

Schema covers 100% of parameters with descriptions, so description needs to add little. It reinforces the role of numQubits/compDepth as circuit inputs and acceptableErrorRatePercent as the effective error bound, but adds no new syntax or format details.

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?

Description uses specific verb ('compute required error rate', 'inverse of compute_expectation') and names exact outputs: per-gate logical error rate, per-EC physical error rate, qualifying SOTA hardware. This clearly distinguishes it from the sibling compute_expectation.

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

Usage Guidelines4/5

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

The phrase 'Inverse of compute_expectation' and the framing 'Answers what hardware do I need...' establishes when to use the tool. However, it doesn't explicitly state when not to use alternatives like compute_fault_tolerant_resources, though the inverse relation gives adequate context.

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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TDQS

A4.4/5.0

Disambiguation5/5

Every tool has a clearly distinct role: compute_expectation and compute_required_error_rate are forward/inverse pairs, compare_hardware_scenarios is a batch wrapper for compute_expectation, and the list_* and get_* tools each target a different data category (hardware, timing, historic, examples, codes). The fit_historic_series tool is distinct from get_historic_series (analysis vs. retrieval). No two tools appear ambiguous.

Naming Consistency5/5

All tool names follow a strict lower_snake_case verb_noun pattern: compute_*, get_*, list_*, fit_*, compare_*. The verbs precisely indicate the action (computation, retrieval, fitting, comparison) and are used consistently across the set.

Tool Count5/5

With 12 tools, the server is well within the ideal 3-15 range. Each tool serves a distinct purpose in the quantum resource estimation workflow: data listing, forward/inverse expectation calculations, fault-tolerant resource estimation, rate computation, historic analysis, and context retrieval. No tool feels redundant or unnecessary.

Completeness5/5

The tool surface comprehensively covers the domain: current hardware data (list_current_quantum_computers, list_hardware_timings), historical trends (get_historic_series, fit_historic_series), algorithm resource examples (list_example_algorithms), error correction codes (list_qldpc_codes), forward and inverse error calculations (compute_expectation, compute_required_error_rate), batch comparison (compare_hardware_scenarios), deep resource estimation (compute_fault_tolerant_resources), a throughput metric (compute_quantum_volume_rate), and the agent brief. There are no obvious missing operations for the stated purpose.

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