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Quantum Expectations

by com.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 (requiredLogicalErrorRatePerGate: a number, or null only when the target is genuinely unreachable; never 0) 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. An option whose inverse lands above the code threshold is capped at min(MAX_P, threshold) and carries a note (any sub-threshold p satisfies it); unreachableReason is reserved for genuinely unreachable options. Answers "what hardware do I need to run this algorithm?".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
compDepthYesCircuit depth d (sequential 2-qubit gate layers). Accepts values up to 1e13.
numQubitsYesNumber of logical qubits n in the circuit.
acceptableErrorRatePercentYesUpper bound on the effective error rate, as a percent. Default website convention is 33 (i.e. ≤33% effective error is "acceptable").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare readOnly and idempotent. The description adds substantial behavioral detail beyond that: requiredLogicalErrorRatePerGate can be null only when genuinely unreachable and never 0, options above the threshold are capped with a note, and unreachableReason is reserved for genuinely unreachable options. This gives the agent a clear model of edge-case behavior.

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 dense but generally efficient, front-loading the inverse relationship and then enumerating output semantics and edge cases. The closing 'Answers...' sentence slightly restates the purpose but reinforces user intent; overall it earns its length for a tool with this complexity.

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?

For a complex read-only computation with no output schema, the description covers the full scope: all EC options, hardware qualification subset, per-gate logical vs physical error rates, threshold capping, notes, and null/unreachable handling. An agent has enough information to know what will be returned and how to interpret unusual results.

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 description coverage is 100%, and the schema already documents all three parameters, including constraints, units, and even the default website convention for acceptableErrorRatePercent. The description does not add much per-parameter meaning beyond tying them to the inverse calculation, so the baseline 3 is appropriate.

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 opens with 'Inverse of compute_expectation' and names the exact computation: given qubits, depth, and acceptable error, return required per-gate logical error rate and physical error rates per EC option. This clearly distinguishes it from its forward counterpart compute_expectation and other sibling tools.

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 description makes the intended use case explicit by framing the tool as answering 'what hardware do I need to run this algorithm?' and by identifying the inputs and outputs. It does not explicitly state when to avoid this tool in favor of siblings like compare_hardware_scenarios, but the inverse relationship to compute_expectation provides strong contextual guidance.

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

A4.5/5.0
Disambiguation5/5

Every tool has a clearly distinct role: forward expectation, inverse required error rate, multi-hardware comparison, fault-tolerant resource estimation, quantum volume rate, plus separate list/get/fit tools for hardware, historic data, algorithms, timings, and qLDPC codes. Cross-references in descriptions make selection unambiguous.

Naming Consistency5/5

Tool names follow a predictable verb_noun pattern: compute_* for estimation operations, list_* for catalogs, get_* for retrieving specific data, plus compare_hardware_scenarios and fit_historic_series as clear descriptive exceptions. The naming style is consistent and readable.

Tool Count5/5

Twelve tools is well-scoped for a quantum computing estimation and hardware comparison server. Each tool covers a distinct capability without redundancy, and the count feels appropriate for the domain's breadth.

Completeness5/5

The tool surface covers forward computation, inverse computation, multi-hardware comparison, fault-tolerant resource estimation, historical trends, current hardware catalogs, algorithm examples, hardware timings, and error-correction code data. There are no obvious dead ends; tools compose naturally to answer 'what hardware do I need and when will it exist?'.

Resources