Quantum Expectations MCP
A remote MCP server that evaluates quantum computing feasibility by computing success probability, qubit overhead, and error correction requirements based on qubit error rate, count, and circuit depth. It provides access to sourced hardware records, historic trend series, and logical error suppression fits for trapped-ion, superconducting, and neutral atom platforms.
Endpoint and access
No sign-in to connect and list tools. Individual tools may still need a vendor account, credentials, or permissions; check the documented prerequisites.
https://www.quantum-expectations.com/api/mcpWhat its tools do
13 tools, grouped by the hints the server advertises. Hints are self-declared, not verified behavior.
Read-only (13)
Checks and signals
Each line is separate evidence, not a trust score.
- Passed Endpoint answered the live check ·
- Passed Official MCP Registry namespace is tied to quantum-expectations.com ·
- Passed Endpoint is on the registry namespace domain ·
- Availability 1 of 1 checks passed in 30 days
What you can do with it
- Compute quantum computation feasibility with and without error correction
- Retrieve historic two-qubit gate error rates and physical qubit counts
- Extrapolate hardware trends to predict target-crossing years
- Compare hardware scenarios against leading machines per platform
Source and maintenance
Protocol and capabilities
- Server
- quantum-expectations 0.8.1
- MCP protocol negotiated in check
- 2025-11-25
- Tools
- 13 advertised
- Resources
- 9 advertised
- Resource templates
- 0 advertised
- Prompts
- 3 advertised
{
"tools": {
"listChanged": true
},
"prompts": {
"listChanged": true
},
"resources": {
"listChanged": true
}
}Server-provided instructions
Untrusted server output shown for inspection. It is not Koskamo guidance and is excluded from knowledge search.
Tools
13 advertised
compute_expectation — Compute Quantum Expectation
Given a quantum circuit (2-qubit error rate p, qubit count n, depth d, optional connectivity class), compute the effective error rate, success probability, and optional surface-code or qLDPC overhead. Without error correction a 2d-lattice connectivity is charged as a routing multiplier on depth (result.routingOverheadFactor); hardwareId supplies the device's class automatically. The response is self-describing (formulas, assumptions, caveats, glossary, SOTA hardware, historic series with source URLs) so an agent can reason from one call. For the inverse ("what hardware do I need?") use compute_required_error_rate; to rank multiple platforms in one call use compare_hardware_scenarios.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"numQubits",
"compDepth"
],
"properties": {
"verbose": {
"type": "boolean",
"default": true,
"description": "When false, omits hardwareContext, formulas, assumptions, caveats, glossary, examples, and exampleProblems from the response — leaving only modelVersion, scenario, and result. Use for parameter sweeps where that context would repeat unchanged."
},
"compDepth": {
"type": "integer",
"maximum": 10000000000000,
"minimum": 1
},
"numQubits": {
"type": "integer",
"maximum": 1000000,
"minimum": 1
},
"hardwareId": {
"enum": [
"trapped-ion",
"superconducting",
"neutral-atom"
],
"type": "string",
"description": "Alias for qubitErrorRate: resolves to the 2-qubit error rate of the given SOTA hardware entry from list_current_quantum_computers. Use this, not a generic qubitErrorRate, whenever the question names a platform without stating its own error rate (superconducting, trapped-ion, neutral-atom). Supply exactly one of qubitErrorRate or hardwareId."
},
"connectivity": {
"enum": [
"all-to-all",
"reconfigurable",
"2d-lattice"
],
"type": "string",
"description": "Two-qubit connectivity of the device: \"all-to-all\" (ion transport), \"reconfigurable\" (atom shuttling, treated as all-to-all) or \"2d-lattice\" (fixed nearest-neighbour couplers, charged as a routing multiplier on depth in the no-EC path). Defaults to the hardware entry's class when hardwareId is supplied, else to all-to-all."
},
"qubitErrorRate": {
"type": "number",
"maximum": 0.1,
"description": "Per-gate 2-qubit error rate p, in (0, 0.1]. Supply exactly one of qubitErrorRate or hardwareId.",
"exclusiveMinimum": 0
},
"useErrorCorrection": {
"type": "boolean",
"default": false
},
"distanceSurfaceCode": {
"type": "number",
"description": "Surface code distance: odd integer in [3, 31]. Required when useErrorCorrection=true and the surface code is selected; ignored when a qLDPC code is selected."
},
"errorCorrectionCode": {
"type": "string",
"description": "Either \"surface\" (default when useErrorCorrection=true) or a qLDPC code id from list_qldpc_codes. \"surface-code\" is accepted as an alias for \"surface\"."
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"modelVersion",
"scenario",
"result"
],
"properties": {
"result": {
"type": "object",
"required": [
"effectiveErrorRate",
"successProbability",
"routingOverheadFactor",
"routedDepth",
"logicalErrorRatePerGate",
"logicalErrorRatePerCycle",
"physicalQubitsPerLogicalQubit",
"totalPhysicalQubits"
],
"properties": {
"routedDepth": {
"type": "number",
"description": "compDepth after routing: the depth the effective-error formula consumed."
},
"effectiveErrorRate": {
"type": "number",
"description": "Probability that at least one gate fails over the (routed) circuit."
},
"successProbability": {
"type": "number",
"description": "1 - effectiveErrorRate."
},
"totalPhysicalQubits": {
"type": [
"number",
"null"
]
},
"routingOverheadFactor": {
"type": "number",
"description": "Multiplier the connectivity class applied to compDepth (1 for all-to-all and reconfigurable)."
},
"logicalErrorRatePerGate": {
"type": [
"number",
"null"
],
"description": "Per-logical-qubit, per-logical-operation error rate; null without error correction."
},
"logicalErrorRatePerCycle": {
"type": [
"number",
"null"
],
"description": "Per-syndrome-cycle logical error rate as the calibration source reports it; null without error correction."
},
"minimumClockRuntimeSeconds": {
"type": "number",
"description": "Lower bound on wall-clock runtime; present only when hardwareId was supplied."
},
"physicalQubitsPerLogicalQubit": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"caveats": {
"type": "array",
"items": {
"type": "string"
}
},
"examples": {
"type": "array",
"items": {}
},
"formulas": {
"type": "object",
"description": "Verbose only: formula specs keyed by name.",
"propertyNames": {
"type": "string"
},
"additionalProperties": {}
},
"glossary": {},
"scenario": {
"type": "object",
"required": [
"qubitErrorRate",
"hardwareId",
"connectivity",
"numQubits",
"compDepth",
"useErrorCorrection",
"distanceSurfaceCode",
"errorCorrectionCode"
],
"properties": {
"compDepth": {
"type": "number"
},
"numQubits": {
"type": "number"
},
"hardwareId": {
"type": [
"string",
"null"
],
"description": "Non-null when qubitErrorRate was resolved from a current-hardware entry."
},
"connectivity": {
"enum": [
"all-to-all",
"reconfigurable",
"2d-lattice"
],
"type": "string"
},
"qubitErrorRate": {
"type": "number"
},
"useErrorCorrection": {
"type": "boolean"
},
"distanceSurfaceCode": {
"type": [
"number",
"null"
]
},
"errorCorrectionCode": {
"type": [
"string",
"null"
]
}
},
"additionalProperties": {}
},
"assumptions": {
"type": "array",
"items": {
"type": "string"
}
},
"methodology": {},
"modelVersion": {
"type": "string",
"description": "Noise-model version; bumps when a formula or constant changes."
},
"exampleProblems": {
"type": "array",
"items": {}
},
"hardwareContext": {
"type": "object",
"properties": {},
"description": "Verbose only: current hardware, record series, timings, measured QUOPS scores.",
"additionalProperties": {}
},
"exampleProblemProvenance": {}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}compute_required_error_rate — Compute Required Error Rate (Inverse)
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?".
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"numQubits",
"compDepth",
"acceptableErrorRatePercent"
],
"properties": {
"compDepth": {
"type": "integer",
"maximum": 10000000000000,
"minimum": 1,
"description": "Circuit depth d (sequential 2-qubit gate layers). Accepts values up to 1e13."
},
"numQubits": {
"type": "integer",
"maximum": 1000000,
"minimum": 1,
"description": "Number of logical qubits n in the circuit."
},
"acceptableErrorRatePercent": {
"type": "number",
"maximum": 100,
"description": "Upper bound on the effective error rate, as a percent. Default website convention is 33 (i.e. ≤33% effective error is \"acceptable\").",
"exclusiveMinimum": 0
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"scenario",
"requiredLogicalErrorRatePerGate",
"noEC",
"surfaceCode",
"qldpcCodes",
"formulas",
"assumptions",
"caveats"
],
"properties": {
"noEC": {
"type": "object",
"required": [
"optionId",
"label",
"requiredPhysicalErrorRate",
"physicalQubitsPerLogicalQubit",
"totalPhysicalQubits",
"qualifyingHardware"
],
"properties": {
"note": {
"type": "string",
"description": "Present when requiredPhysicalErrorRate is a threshold cap rather than an exact inversion."
},
"label": {
"type": "string"
},
"optionId": {
"type": "string",
"description": "\"no-ec\", \"surface-d<odd distance>\", or a qLDPC code id."
},
"unreachableReason": {
"type": "string"
},
"qualifyingHardware": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"hardwareType",
"errorRate",
"physicalQubits"
],
"properties": {
"id": {
"type": "string"
},
"errorRate": {
"type": "number"
},
"hardwareType": {
"type": "string"
},
"physicalQubits": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
}
},
"additionalProperties": {}
},
"description": "Current-hardware entries whose errorRate already meets requiredPhysicalErrorRate."
},
"totalPhysicalQubits": {
"type": [
"number",
"null"
]
},
"requiredPhysicalErrorRate": {
"type": [
"number",
"null"
],
"description": "Physical per-gate error rate the hardware must reach; null when the option can never satisfy the target."
},
"physicalQubitsPerLogicalQubit": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"caveats": {
"type": "array",
"items": {
"type": "string"
}
},
"formulas": {
"type": "object",
"required": [
"inverseEffectiveError",
"inverseSurfaceCode",
"inverseQLDPC"
],
"properties": {
"inverseQLDPC": {
"type": "string"
},
"inverseSurfaceCode": {
"type": "string"
},
"inverseEffectiveError": {
"type": "string"
}
},
"additionalProperties": {}
},
"scenario": {
"type": "object",
"required": [
"numQubits",
"compDepth",
"acceptableErrorRatePercent"
],
"properties": {
"compDepth": {
"type": "number"
},
"numQubits": {
"type": "number"
},
"acceptableErrorRatePercent": {
"type": "number"
}
},
"additionalProperties": {}
},
"qldpcCodes": {
"type": "array",
"items": {
"type": "object",
"required": [
"optionId",
"label",
"requiredPhysicalErrorRate",
"physicalQubitsPerLogicalQubit",
"totalPhysicalQubits",
"qualifyingHardware"
],
"properties": {
"note": {
"type": "string",
"description": "Present when requiredPhysicalErrorRate is a threshold cap rather than an exact inversion."
},
"label": {
"type": "string"
},
"optionId": {
"type": "string",
"description": "\"no-ec\", \"surface-d<odd distance>\", or a qLDPC code id."
},
"unreachableReason": {
"type": "string"
},
"qualifyingHardware": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"hardwareType",
"errorRate",
"physicalQubits"
],
"properties": {
"id": {
"type": "string"
},
"errorRate": {
"type": "number"
},
"hardwareType": {
"type": "string"
},
"physicalQubits": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
}
},
"additionalProperties": {}
},
"description": "Current-hardware entries whose errorRate already meets requiredPhysicalErrorRate."
},
"totalPhysicalQubits": {
"type": [
"number",
"null"
]
},
"requiredPhysicalErrorRate": {
"type": [
"number",
"null"
],
"description": "Physical per-gate error rate the hardware must reach; null when the option can never satisfy the target."
},
"physicalQubitsPerLogicalQubit": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"description": "One option per cataloged qLDPC code."
},
"assumptions": {
"type": "array",
"items": {
"type": "string"
}
},
"surfaceCode": {
"type": "array",
"items": {
"type": "object",
"required": [
"optionId",
"label",
"requiredPhysicalErrorRate",
"physicalQubitsPerLogicalQubit",
"totalPhysicalQubits",
"qualifyingHardware"
],
"properties": {
"note": {
"type": "string",
"description": "Present when requiredPhysicalErrorRate is a threshold cap rather than an exact inversion."
},
"label": {
"type": "string"
},
"optionId": {
"type": "string",
"description": "\"no-ec\", \"surface-d<odd distance>\", or a qLDPC code id."
},
"unreachableReason": {
"type": "string"
},
"qualifyingHardware": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"hardwareType",
"errorRate",
"physicalQubits"
],
"properties": {
"id": {
"type": "string"
},
"errorRate": {
"type": "number"
},
"hardwareType": {
"type": "string"
},
"physicalQubits": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
}
},
"additionalProperties": {}
},
"description": "Current-hardware entries whose errorRate already meets requiredPhysicalErrorRate."
},
"totalPhysicalQubits": {
"type": [
"number",
"null"
]
},
"requiredPhysicalErrorRate": {
"type": [
"number",
"null"
],
"description": "Physical per-gate error rate the hardware must reach; null when the option can never satisfy the target."
},
"physicalQubitsPerLogicalQubit": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"description": "One option per odd surface-code distance."
},
"requiredLogicalErrorRatePerGate": {
"type": [
"number",
"null"
],
"description": "Per-gate logical error rate every option must deliver; null only when the target is unreachable, never 0."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}compute_fault_tolerant_resources — Compute Fault-Tolerant Resources
Given an algorithm stated as (numLogicalQubits, tCount) and a physical error rate (or hardwareId), derive the full surface-code + magic-state-distillation footprint from the general laws of the Litinski lattice-surgery cost model (no per-scenario constants): distillation factory choice, tile layout, required code distance, total physical qubits, and wall-clock time. Results are reported under TWO published logical-error fits (conservative + optimistic) because they disagree by 13-268x (d=7 to d=25) - always state both. Returns an explicit `infeasible` block when no cataloged factory or code distance can satisfy the error budget. Computes numbers only: comparing against classical alternatives and concluding "should this run on a quantum computer" stays with you, the calling agent (state the caveats when you do).
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"numLogicalQubits",
"tCount"
],
"properties": {
"tCount": {
"type": "integer",
"maximum": 10000000000000,
"minimum": 1,
"description": "Total number of T gates (magic states consumed). Convention: state Toffoli-counted algorithms in T gates before calling (1 Toffoli ≈ 4–7 T depending on decomposition)."
},
"dataBlock": {
"enum": [
"compact",
"intermediate",
"fast"
],
"type": "string",
"default": "compact",
"description": "Data-block layout (Litinski §2): compact = fewest qubits, fast = shortest time per T gate."
},
"hardwareId": {
"enum": [
"trapped-ion",
"superconducting",
"neutral-atom"
],
"type": "string",
"description": "Alias for qubitErrorRate: resolves to the 2-qubit error rate of the given SOTA hardware entry from list_current_quantum_computers. Use this, not a generic qubitErrorRate such as 0.001, whenever the question names a platform without stating its own error rate (superconducting, trapped-ion, neutral-atom). Supply exactly one of qubitErrorRate or hardwareId."
},
"qubitErrorRate": {
"type": "number",
"maximum": 0.1,
"description": "Physical 2-qubit error rate p in (0, 0.1]. Supply exactly one of qubitErrorRate or hardwareId.",
"exclusiveMinimum": 0
},
"cycleTimeSeconds": {
"type": "number",
"default": 0.000001,
"maximum": 1,
"description": "Surface-code cycle time in seconds. Default 1 µs (Litinski convention; Google 2024 measured 1.1 µs on superconducting hardware). Trapped-ion/neutral-atom cycles are orders of magnitude slower.",
"exclusiveMinimum": 0
},
"numLogicalQubits": {
"type": "integer",
"maximum": 1000000,
"minimum": 1,
"description": "Number of logical data qubits the algorithm needs."
},
"targetSuccessProbability": {
"type": "number",
"default": 0.98,
"description": "Target end-to-end success probability. The failure budget (1 − target) is split evenly between T-state error and logical (memory/surgery) error, matching Litinski §4.",
"exclusiveMaximum": 1,
"exclusiveMinimum": 0
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"scenario",
"infeasible",
"factory",
"layout",
"effectiveStepsPerTGate",
"totalTimeSteps",
"results",
"formulas",
"assumptions",
"caveats",
"sources"
],
"properties": {
"layout": {
"anyOf": [
{
"type": "object",
"required": [
"dataBlockTiles",
"factoryTiles",
"storageTiles",
"totalTiles"
],
"properties": {
"totalTiles": {
"type": "number"
},
"factoryTiles": {
"type": "number"
},
"storageTiles": {
"type": "number"
},
"dataBlockTiles": {
"type": "number"
}
},
"additionalProperties": {}
},
{
"type": "null"
}
]
},
"caveats": {
"type": "array",
"items": {
"type": "string"
}
},
"factory": {
"anyOf": [
{
"type": "object",
"required": [
"id",
"tiles",
"outputErrorRate",
"successProbability",
"stepsPerOutputState"
],
"properties": {
"id": {
"type": "string"
},
"tiles": {
"type": "number"
},
"outputErrorRate": {
"type": "number"
},
"successProbability": {
"type": "number"
},
"stepsPerOutputState": {
"type": "number"
}
},
"additionalProperties": {}
},
{
"type": "null"
}
]
},
"results": {
"anyOf": [
{
"type": "object",
"required": [
"conservative",
"optimistic"
],
"properties": {
"optimistic": {
"type": "object",
"required": [
"fit",
"distance",
"physicalQubits",
"wallClockSeconds",
"errorBudget"
],
"properties": {
"fit": {
"enum": [
"litinski",
"repo"
],
"type": "string"
},
"distance": {
"type": [
"number",
"null"
],
"description": "Required surface-code distance (odd); null when no cataloged distance suffices."
},
"errorBudget": {
"type": "object",
"required": [
"logicalContribution",
"tStateContribution",
"totalFailureUpperBound"
],
"properties": {
"tStateContribution": {
"type": "number"
},
"logicalContribution": {
"type": [
"number",
"null"
]
},
"totalFailureUpperBound": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"physicalQubits": {
"type": [
"number",
"null"
]
},
"wallClockSeconds": {
"type": [
"number",
"null"
]
},
"unreachableReason": {
"type": "string"
}
},
"description": "Willow-2024-calibrated fit.",
"additionalProperties": {}
},
"conservative": {
"type": "object",
"required": [
"fit",
"distance",
"physicalQubits",
"wallClockSeconds",
"errorBudget"
],
"properties": {
"fit": {
"enum": [
"litinski",
"repo"
],
"type": "string"
},
"distance": {
"type": [
"number",
"null"
],
"description": "Required surface-code distance (odd); null when no cataloged distance suffices."
},
"errorBudget": {
"type": "object",
"required": [
"logicalContribution",
"tStateContribution",
"totalFailureUpperBound"
],
"properties": {
"tStateContribution": {
"type": "number"
},
"logicalContribution": {
"type": [
"number",
"null"
]
},
"totalFailureUpperBound": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"physicalQubits": {
"type": [
"number",
"null"
]
},
"wallClockSeconds": {
"type": [
"number",
"null"
]
},
"unreachableReason": {
"type": "string"
}
},
"description": "Litinski logical-error fit.",
"additionalProperties": {}
}
},
"additionalProperties": {}
},
{
"type": "null"
}
],
"description": "Both fits bracket the model uncertainty; report both."
},
"sources": {
"type": "array",
"items": {
"type": "string"
}
},
"formulas": {
"type": "object",
"propertyNames": {
"type": "string"
},
"additionalProperties": {
"type": "string"
}
},
"scenario": {
"type": "object",
"required": [
"numLogicalQubits",
"tCount",
"qubitErrorRate",
"hardwareId",
"targetSuccessProbability",
"dataBlock",
"cycleTimeSeconds"
],
"properties": {
"tCount": {
"type": "number"
},
"dataBlock": {
"enum": [
"compact",
"intermediate",
"fast"
],
"type": "string"
},
"hardwareId": {
"type": [
"string",
"null"
]
},
"qubitErrorRate": {
"type": "number"
},
"cycleTimeSeconds": {
"type": "number"
},
"numLogicalQubits": {
"type": "number"
},
"targetSuccessProbability": {
"type": "number"
}
},
"additionalProperties": {}
},
"infeasible": {
"anyOf": [
{
"type": "object",
"required": [
"reason"
],
"properties": {
"reason": {
"type": "string"
}
},
"additionalProperties": {}
},
{
"type": "null"
}
],
"description": "Non-null when the scenario is infeasible as modeled: no cataloged factory meets the T-error budget (factory and layout are then null) or no distance up to the cap meets it under either fit (per-fit distance, qubits and wall clock are then null)."
},
"assumptions": {
"type": "array",
"items": {
"type": "string"
}
},
"totalTimeSteps": {
"type": [
"number",
"null"
]
},
"effectiveStepsPerTGate": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}compare_hardware_scenarios — Compare Hardware Scenarios
Run the same circuit against multiple current SOTA hardware entries in one call so an agent can rank platforms without N sequential compute_expectation calls. Defaults to every entry in list_current_quantum_computers when hardwareIds is omitted.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"numQubits",
"compDepth"
],
"properties": {
"compDepth": {
"type": "integer",
"maximum": 10000000000000,
"minimum": 1
},
"numQubits": {
"type": "integer",
"maximum": 1000000,
"minimum": 1
},
"hardwareIds": {
"type": "array",
"items": {
"type": "string"
},
"description": "Subset of QUANTUM_COMPUTERS ids to compare. Omit to compare every entry."
},
"useErrorCorrection": {
"type": "boolean",
"default": false
},
"distanceSurfaceCode": {
"type": "number",
"description": "Surface code distance: odd integer in [3, 31]. Required when useErrorCorrection=true and the surface code is selected; ignored when a qLDPC code is selected."
},
"errorCorrectionCode": {
"enum": [
"surface",
"bb-144-12-12",
"bb-288-12-18",
"surface-code"
],
"type": "string",
"description": "Either \"surface\" (default when useErrorCorrection=true) or a qLDPC code id. \"surface-code\" is accepted as an alias for \"surface\"."
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"scenario",
"results",
"unresolvedHardwareIds"
],
"properties": {
"results": {
"type": "array",
"items": {
"type": "object",
"required": [
"hardware",
"qubitErrorRateUsed",
"result"
],
"properties": {
"result": {
"type": "object",
"required": [
"effectiveErrorRate",
"successProbability",
"routingOverheadFactor",
"routedDepth",
"logicalErrorRatePerGate",
"logicalErrorRatePerCycle",
"physicalQubitsPerLogicalQubit",
"totalPhysicalQubits"
],
"properties": {
"routedDepth": {
"type": "number",
"description": "compDepth after routing: the depth the effective-error formula consumed."
},
"effectiveErrorRate": {
"type": "number",
"description": "Probability that at least one gate fails over the (routed) circuit."
},
"successProbability": {
"type": "number",
"description": "1 - effectiveErrorRate."
},
"totalPhysicalQubits": {
"type": [
"number",
"null"
]
},
"routingOverheadFactor": {
"type": "number",
"description": "Multiplier the connectivity class applied to compDepth (1 for all-to-all and reconfigurable)."
},
"logicalErrorRatePerGate": {
"type": [
"number",
"null"
],
"description": "Per-logical-qubit, per-logical-operation error rate; null without error correction."
},
"logicalErrorRatePerCycle": {
"type": [
"number",
"null"
],
"description": "Per-syndrome-cycle logical error rate as the calibration source reports it; null without error correction."
},
"minimumClockRuntimeSeconds": {
"type": "number",
"description": "Lower bound on wall-clock runtime; present only when hardwareId was supplied."
},
"physicalQubitsPerLogicalQubit": {
"type": [
"number",
"null"
]
}
},
"additionalProperties": {}
},
"hardware": {
"type": "object",
"required": [
"id",
"hardwareType",
"physicalQubits",
"errorRate",
"errorRateDisplay",
"connectivity"
],
"properties": {
"id": {
"type": "string"
},
"errorRate": {
"type": "number"
},
"connectivity": {
"enum": [
"all-to-all",
"reconfigurable",
"2d-lattice"
],
"type": "string"
},
"hardwareType": {
"type": "string"
},
"physicalQubits": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"errorRateDisplay": {
"type": "string"
}
},
"additionalProperties": {}
},
"qubitErrorRateUsed": {
"type": "number"
}
},
"additionalProperties": {}
}
},
"scenario": {
"type": "object",
"required": [
"numQubits",
"compDepth",
"useErrorCorrection",
"distanceSurfaceCode",
"errorCorrectionCode"
],
"properties": {
"compDepth": {
"type": "number"
},
"numQubits": {
"type": "number"
},
"useErrorCorrection": {
"type": "boolean"
},
"distanceSurfaceCode": {
"type": [
"number",
"null"
]
},
"errorCorrectionCode": {
"type": [
"string",
"null"
]
}
},
"additionalProperties": {}
},
"unresolvedHardwareIds": {
"type": "array",
"items": {
"type": "string"
},
"description": "Requested hardwareIds that match no current-hardware entry."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}list_current_quantum_computers — Current Quantum Computers
Return the representative-entry table of current SOTA quantum computers (id, hardware type, physical qubit count, 2-qubit error rate, connectivity class with a note on the coupling graph, source URL). Same data that powers the website's "Current Quantum Computers" table. For the model's capability prediction per entry in QUOPS units, read hardwareContext.currentQuantumComputers[].modelCapability from compute_expectation; for measured scores, list_quops_scores.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"items"
],
"properties": {
"items": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"physicalQubits",
"errorRate",
"errorRateDisplay",
"hardwareType",
"connectivity",
"connectivityNote",
"source"
],
"properties": {
"id": {
"type": "string",
"description": "Stable alias accepted as hardwareId by the compute tools."
},
"source": {
"type": "string",
"format": "uri"
},
"errorRate": {
"type": "number",
"description": "2-qubit gate error rate as a probability."
},
"connectivity": {
"enum": [
"all-to-all",
"reconfigurable",
"2d-lattice"
],
"type": "string"
},
"hardwareType": {
"type": "string"
},
"physicalQubits": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"connectivityNote": {
"type": "string"
},
"errorRateDisplay": {
"type": "string"
}
},
"additionalProperties": {}
},
"description": "One representative entry per hardware type."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}list_quops_scores — Measured QUOPS Capability Scores
Return the measured QUOPS (quantum universal operation performance system, a cross-platform capability benchmark) scores (arXiv:2609.12146): per device, the largest random universal circuit size executed at polarization >= 1/sqrt(e) inside the cone w^2 <= s <= w^3, with width, QUOPS rate, architecture (physical, physical-postselected, logical) and source URL, plus the utility-scale targets in the same unit (RSA-2048 and FeMoco). These are measurements, the yardstick the site's own model is checked against: compare them with modelCapability.quopsEquivalent on the compute_expectation hardware context. Vendor and device names appear here because this table is agent-facing only.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"definition",
"polarizationThreshold",
"admissibleCone",
"scores",
"targets"
],
"properties": {
"scores": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"device",
"vendor",
"hardwareType",
"physicalQubits",
"architecture",
"code",
"quopsScore",
"width",
"quopsRatePerSecond",
"discardFraction",
"year",
"source"
],
"properties": {
"id": {
"type": "string"
},
"code": {
"type": [
"string",
"null"
]
},
"year": {
"type": "number"
},
"width": {
"type": "number"
},
"device": {
"type": "string"
},
"source": {
"type": "string",
"format": "uri"
},
"vendor": {
"type": "string"
},
"quopsScore": {
"type": "number"
},
"architecture": {
"enum": [
"physical",
"physical-postselected",
"logical"
],
"type": "string"
},
"hardwareType": {
"type": "string"
},
"physicalQubits": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"discardFraction": {
"type": [
"number",
"null"
]
},
"quopsRatePerSecond": {
"type": "number"
}
},
"additionalProperties": {}
}
},
"targets": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"problem",
"logicalQubits",
"toffoliCount",
"quopsTarget",
"quopsRateTargetPerSecond",
"timeBudget",
"resourceEstimateSource",
"source",
"note"
],
"properties": {
"id": {
"type": "string"
},
"note": {
"type": [
"string",
"null"
]
},
"source": {
"type": "string",
"format": "uri"
},
"problem": {
"type": "string"
},
"timeBudget": {
"type": "string"
},
"quopsTarget": {
"type": "number"
},
"toffoliCount": {
"type": "number"
},
"logicalQubits": {
"type": "number"
},
"resourceEstimateSource": {
"type": "string",
"format": "uri"
},
"quopsRateTargetPerSecond": {
"type": "number"
}
},
"additionalProperties": {}
}
},
"definition": {
"type": "string"
},
"admissibleCone": {},
"polarizationThreshold": {
"type": "number"
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}list_hardware_timings — Hardware Gate-Cycle Timings
Return per-platform gate-cycle timings (2Q gate time, readout time, in SI seconds) plus the testbed they were measured on (`representativeDevice`) and the native 2Q gate name, with source URLs. Joins list_current_quantum_computers via `hardwareType`, but the numbers are BEST-CASE DEMONSTRATIONS from small testbeds, not measurements on the joined devices, which run their gates and array readout orders of magnitude slower. Use for ratio analysis or as optimistic lower-bound inputs to runtime estimates and compute_quantum_volume_rate, and say so when you quote a runtime.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"items"
],
"properties": {
"items": {
"type": "array",
"items": {
"type": "object",
"required": [
"hardwareType",
"representativeDevice",
"native2QGate",
"t2QGateTime",
"tMeasurement"
],
"properties": {
"notes": {
"type": "string"
},
"t2QGateTime": {
"type": "object",
"required": [
"seconds",
"source"
],
"properties": {
"source": {
"type": "string",
"format": "uri"
},
"seconds": {
"type": "number",
"description": "SI seconds."
}
},
"additionalProperties": {}
},
"hardwareType": {
"type": "string",
"description": "Join key to list_current_quantum_computers."
},
"native2QGate": {
"type": "string"
},
"tMeasurement": {
"type": "object",
"required": [
"seconds",
"source"
],
"properties": {
"source": {
"type": "string",
"format": "uri"
},
"seconds": {
"type": "number",
"description": "SI seconds."
}
},
"additionalProperties": {}
},
"representativeDevice": {
"type": "string",
"description": "The testbed measured, not the joined current-hardware device."
}
},
"additionalProperties": {}
},
"description": "Best-case gate and readout times per platform."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}compute_quantum_volume_rate — Compute Quantum Volume Rate
Compute the Quantum Volume Rate (QV/second): QVR = V_Q / (log2(V_Q) * t_2Q + t_meas). First-order estimate of how fast a device prepares one QV-sized square circuit (one native 2Q gate per QV layer + one end-of-circuit measurement). OVERSTATES achievable rate: real compilation inflates the 2Q-gate count per layer; omits reset/SPAM, mid-circuit measurement, and classical-control latency. For a production throughput metric, see IBM's CLOPS (arXiv:2110.14108).
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"quantumVolume",
"t2QSeconds",
"tMeasurementSeconds"
],
"properties": {
"t2QSeconds": {
"type": "number",
"description": "Native 2-qubit gate time in seconds (e.g. 60e-9 for a 60 ns CZ).",
"exclusiveMinimum": 0
},
"quantumVolume": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": 2,
"description": "Quantum volume V_Q (integer ≥ 2, e.g. 64 for a depth-log2=6 square circuit)."
},
"tMeasurementSeconds": {
"type": "number",
"description": "End-of-circuit measurement/readout time in seconds (e.g. 5e-3 for 5 ms).",
"exclusiveMinimum": 0
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"qvrPerSecond",
"unit",
"formula",
"inputs",
"assumptions",
"caveats"
],
"properties": {
"unit": {
"type": "string",
"const": "QV/s"
},
"inputs": {
"type": "object",
"required": [
"quantumVolume",
"t2QSeconds",
"tMeasurementSeconds"
],
"properties": {
"t2QSeconds": {
"type": "number"
},
"quantumVolume": {
"type": "number"
},
"tMeasurementSeconds": {
"type": "number"
}
},
"additionalProperties": {}
},
"caveats": {
"type": "array",
"items": {
"type": "string"
}
},
"formula": {
"type": "string"
},
"assumptions": {
"type": "array",
"items": {
"type": "string"
}
},
"qvrPerSecond": {
"type": "number"
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}fit_historic_series — Fit Historic Series (log-linear)
Fit a log-linear trend (ln(value) = slope * year + intercept) to one historic series — fidelity or qubit-count — for one hardware type. Atomic primitive: compose with list_current_quantum_computers, compute_required_error_rate, or your own modelling to answer "when might hardware reach X?". residualStdDev is the BIASED (maximum-likelihood) RMS — divides by n, not (n - 2); on small series (n ≈ 3–5) inflate by √(n / (n - 2)) before building confidence intervals.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"seriesType",
"hardwareType"
],
"properties": {
"seriesType": {
"enum": [
"fidelity",
"qubit-count"
],
"type": "string",
"description": "Which historic series to fit: \"fidelity\" (2-qubit gate error rate) or \"qubit-count\" (physical qubits)."
},
"targetValue": {
"type": "number",
"description": "Optional. When supplied, the response includes yearAtTargetValue — the extrapolated year the fit crosses this value (error rate for fidelity series, qubit count for qubit-count series). Null if slope is flat.",
"exclusiveMinimum": 0
},
"hardwareType": {
"enum": [
"Neutral Atom",
"Superconducting",
"Trapped-Ion"
],
"type": "string",
"description": "Hardware platform as used by get_historic_series."
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"seriesType",
"hardwareType",
"model",
"formula",
"slope",
"intercept",
"nPoints",
"residualStdDev",
"yearRange",
"halvingPeriodYears",
"doublingPeriodYears"
],
"properties": {
"model": {
"type": "string",
"const": "log-linear"
},
"slope": {
"type": "number"
},
"formula": {
"type": "string",
"const": "ln(value) = slope * year + intercept"
},
"nPoints": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"intercept": {
"type": "number"
},
"yearRange": {
"type": "array",
"items": false,
"maxItems": 2,
"minItems": 2,
"prefixItems": [
{
"type": "number"
},
{
"type": "number"
}
]
},
"seriesType": {
"enum": [
"fidelity",
"qubit-count"
],
"type": "string"
},
"hardwareType": {
"type": "string"
},
"residualStdDev": {
"type": "number",
"description": "Biased (divide-by-n) RMS of the ln residuals."
},
"yearAtTargetValue": {
"type": [
"number",
"null"
],
"description": "Present only when targetValue was supplied."
},
"halvingPeriodYears": {
"type": [
"number",
"null"
],
"description": "Non-null iff slope < 0."
},
"doublingPeriodYears": {
"type": [
"number",
"null"
],
"description": "Non-null iff slope > 0."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}list_example_algorithms — Example Quantum Algorithms
Return the curated list of example quantum algorithms with published resource estimates (qubit count, depth/gate count, source paper URL). Useful for comparing what algorithms need vs. what hardware can deliver. Each entry carries a `provenance` field: 'published-circuit' means the figure is reproducible from the source, 'attested-estimate' means the source withholds the circuit and the figure rests on the authors' attestation, with a `provenanceNote` giving the specifics. Carry that caveat whenever you quote an attested figure; do not present it as equivalently sourced.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"items"
],
"properties": {
"items": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"name",
"numQubits",
"compDepth",
"compDepthDisplay",
"source",
"provenance",
"provenanceNote"
],
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"source": {
"type": "string",
"format": "uri"
},
"compDepth": {
"type": "number"
},
"numQubits": {
"type": "number"
},
"provenance": {
"enum": [
"published-circuit",
"attested-estimate"
],
"type": "string"
},
"provenanceNote": {
"type": [
"string",
"null"
]
},
"compDepthDisplay": {
"type": "string"
}
},
"additionalProperties": {}
},
"description": "Curated algorithms with published resource estimates."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}list_qldpc_codes — qLDPC Code Catalog (Quantum Low-Density Parity-Check)
Return the catalog of supported qLDPC (quantum low-density parity-check) error-correction codes (id, label, family, n, k, d, circuitLevelDistance, ancilla counts, roundsPerLogicalOp, threshold, fitCoefficients {c0, c1, c2}, logicalErrorExponent [alpha = d_circ/2], source URLs, provenance, caveats). The per-block, per-syndrome-cycle logical error rate is p^alpha * exp(c0 + c1*p + c2*p^2) for p <= threshold (the source paper's own fitting form; c1 = c2 = 0 means a plain power law). `provenance` names the table or section each constant was read from. `caveats` is an array of source-level qualifications on the entry's constants (loose distance bounds, values a source marks as assumed, numbers that differ between sources); empty when the sources carry none. Read it before quoting a code's logical error rate as firm. Use a code's `id` as the `errorCorrectionCode` input to `compute_expectation`.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"items"
],
"properties": {
"items": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"label",
"family",
"n",
"k",
"d",
"circuitLevelDistance",
"checkAncillasPerBlock",
"logicAncillasPerBlock",
"roundsPerLogicalOp",
"threshold",
"fitCoefficients",
"logicalErrorExponent",
"provenance",
"source",
"caveats"
],
"properties": {
"d": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"k": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"n": {
"type": "integer",
"maximum": 9007199254740991,
"minimum": -9007199254740991
},
"id": {
"type": "string",
"description": "Accepted as errorCorrectionCode by compute_expectation."
},
"label": {
"type": "string"
},
"family": {
"type": "string"
},
"source": {
"type": "string",
"format": "uri"
},
"caveats": {
"type": "array",
"items": {
"type": "string"
}
},
"threshold": {
"type": "number"
},
"provenance": {
"type": "object",
"propertyNames": {
"type": "string"
},
"additionalProperties": {
"type": "string"
}
},
"fitCoefficients": {
"type": "object",
"required": [
"c0",
"c1",
"c2"
],
"properties": {
"c0": {
"type": "number"
},
"c1": {
"type": "number"
},
"c2": {
"type": "number"
}
},
"additionalProperties": {}
},
"roundsPerLogicalOp": {
"type": "number"
},
"circuitLevelDistance": {
"type": "number"
},
"logicalErrorExponent": {
"type": "number"
},
"checkAncillasPerBlock": {
"type": "number"
},
"logicAncillasPerBlock": {
"type": "number"
}
},
"additionalProperties": {}
},
"description": "Supported qLDPC codes."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}get_historic_series — Historic Hardware Series (Fidelity or Qubit Count)
Return the full historic time series — either two-qubit gate error rates ("fidelity") or physical qubit counts ("qubit-count") — broken down by hardware type. Each datapoint carries a source URL. Use this to extrapolate trends — "when might hardware reach X?" — or pair with fit_historic_series for a log-linear fit on one hardware type.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"seriesType"
],
"properties": {
"seriesType": {
"enum": [
"fidelity",
"qubit-count"
],
"type": "string",
"description": "\"fidelity\" → 2-qubit gate error rates; \"qubit-count\" → physical qubit counts."
}
}
}{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"items"
],
"properties": {
"items": {
"type": "array",
"items": {
"type": "object",
"required": [
"name",
"data"
],
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"required": [
"year",
"source"
],
"properties": {
"year": {
"type": "number"
},
"label": {
"type": "string"
},
"source": {
"type": "string",
"format": "uri"
},
"infidelity": {
"type": "number",
"description": "Fidelity series only: 2-qubit gate infidelity."
},
"qubitCount": {
"type": "number",
"description": "Qubit-count series only."
}
},
"additionalProperties": {}
}
},
"name": {
"type": "string",
"description": "Hardware type."
},
"note": {
"type": "string"
}
},
"additionalProperties": {}
},
"description": "One series per hardware type."
}
},
"additionalProperties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}get_agent_brief — Agent Brief
Return the plain-text site brief describing scope, assumptions, the honesty clause, and the API contract. Mirrors the /agent.txt document served by the website.
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {}
}{
"readOnlyHint": true,
"openWorldHint": false,
"idempotentHint": true,
"destructiveHint": false
}Prompts
3 advertised
assess_feasibility — Assess Feasibility of a Quantum Circuit
Guided analysis: when could a circuit (logical qubits × depth, target error budget) become feasible on real hardware? Composes compute_required_error_rate and fit_historic_series; the agent draws the conclusions and states every assumption.
- numQubits
- Number of logical qubits in the circuit (integer, e.g. "100"). (required)
- compDepth
- Circuit depth / total gate count (integer, e.g. "1000"). (required)
- acceptableErrorRatePercent
- Target upper bound on the effective error rate, in percent (default "33").
assess_fault_tolerant_feasibility — Assess Fault-Tolerant Feasibility of an Algorithm
Guided analysis: what would it take to run an algorithm (logical qubits × T count) fault-tolerantly, and is that worth it vs classical compute? Composes compute_fault_tolerant_resources and list_current_quantum_computers; the agent draws the conclusions and states the fit-spread caveat.
- numLogicalQubits
- Number of logical data qubits the algorithm needs (integer, e.g. "100"). (required)
- tCount
- Total T-gate count of the algorithm (integer, e.g. "1e9" style values written out). (required)
- qubitErrorRate
- Physical 2-qubit error rate p (e.g. "0.001"). Omit to survey current SOTA hardware instead.
compare_platforms — Compare Hardware Platforms for a Circuit
Guided analysis: rank current hardware platforms for a given circuit and explain which constraint binds on each. Composes compare_hardware_scenarios and list_hardware_timings; the agent draws the conclusions.
- numQubits
- Number of logical qubits in the circuit (integer, e.g. "100"). (required)
- compDepth
- Circuit depth / total gate count (integer, e.g. "1000"). (required)
Resources
9 advertised
historic-fidelity — Historic 2-Qubit Gate Fidelity
Historic 2-qubit gate error rates by hardware type (with source URLs).
resource://quantum-expectations/historic-fidelityhistoric-qubit-count — Historic Physical Qubit Counts
Historic physical qubit counts by hardware type (with source URLs).
resource://quantum-expectations/historic-qubit-countcurrent-quantum-computers — Current Quantum Computers
Representative-entry table of current SOTA quantum computers.
resource://quantum-expectations/current-quantum-computershardware-timings — Hardware Gate-Cycle Timings
Per-platform best-case demonstrated 2-qubit gate time and readout time (SI seconds) with source URLs. Joins with current-quantum-computers via hardwareType, but the numbers come from small testbeds, not the joined devices; runtime figures built on them are optimistic lower bounds.
resource://quantum-expectations/hardware-timingsexample-algorithms — Example Quantum Algorithms
Curated list of quantum algorithms with published resource estimates, each tagged with a `provenance` value (published-circuit vs attested-estimate) and, where applicable, a note on what is and is not independently verifiable.
resource://quantum-expectations/example-algorithmsexample-algorithm-provenance — Example Algorithm Provenance Legend
Gloss for the `provenance` values carried by every entry in example-algorithms, so the verifiability caveat travels with the data instead of living only in prose.
resource://quantum-expectations/example-algorithm-provenanceqldpc-codes — qLDPC Code Catalog (Quantum Low-Density Parity-Check)
Supported qLDPC codes: threshold, fitCoefficients {c0, c1, c2} and logicalErrorExponent (alpha) for P_L = p^alpha * exp(c0 + c1*p + c2*p^2) per block per syndrome cycle for p <= threshold (above it the server continues with a power law anchored at P_L(threshold)), ancilla counts, source URLs, provenance and per-entry caveats.
resource://quantum-expectations/qldpc-codesquops-scores — Measured QUOPS Capability Scores
Measured QUOPS capability scores per device (arXiv:2609.12146) with width, rate, architecture and source URL, plus utility-scale targets in the same unit. The measured yardstick for the model's modelCapability predictions.
resource://quantum-expectations/quops-scoresagent-brief — Agent Brief
Plain-text site brief with scope, assumptions, honesty clause, and API contract.
resource://quantum-expectations/agent-brief90-day uptime
A lightweight connection check about every six hours, sampled on hosts with many listings. It shows the server answered, not that its tools work.
- 2026-11-06: 1 of 1 checks answered
- 2026-11-07: 1 of 1 checks answered
30-day availability
Aggregated from read-only remote checks; this is not a tool-execution or security guarantee.
- Successful checks
- 1 of 1 (100%)
- Average latency
- 1374 ms
- 95th percentile latency
- 1374 ms
Maintenance history
Public check and source-refresh events. Editorial and ownership records remain private.
- · probe · passed · 1374 ms · metadata changed · 13 tool(s), 9 resource(s), 0 resource template(s), 3 prompt(s)
Connect from your client
Each snippet registers the same endpoint; pick the client you use.
Claude Code
claude mcp add --transport http quantum-expectations-mcp https://www.quantum-expectations.com/api/mcpCodex
codex mcp add quantum-expectations-mcp --url https://www.quantum-expectations.com/api/mcpCursor
{
"mcpServers": {
"quantum-expectations-mcp": {
"url": "https://www.quantum-expectations.com/api/mcp"
}
}
}Cline
{
"mcpServers": {
"quantum-expectations-mcp": {
"type": "streamableHttp",
"url": "https://www.quantum-expectations.com/api/mcp",
"disabled": false
}
}
}VS Code
{
"servers": {
"quantum-expectations-mcp": {
"type": "http",
"url": "https://www.quantum-expectations.com/api/mcp"
}
}
}Gemini CLI
gemini mcp add --transport http quantum-expectations-mcp https://www.quantum-expectations.com/api/mcpClaude Desktop
https://www.quantum-expectations.com/api/mcpWindsurf
{
"mcpServers": {
"quantum-expectations-mcp": {
"serverUrl": "https://www.quantum-expectations.com/api/mcp"
}
}
}Let your agent find its tools
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Do you run Quantum Expectations MCP?
Prove ownership with a DNS record, a well-known file, or your Official MCP Registry entry, and your edits publish immediately. Proofs are re-checked weekly; admins can still hide or remove a listing.