_ registry / mcp http-sse · checked 5m ago

quantum-expectations

https://www.quantum-expectations.com

Registry code: 460b6515a344eea7

api record

Quantum Expectations estimates what current and near-term quantum hardware can run. Every tool is a deterministic, read-only computation over primary-sourced data and returns primitives, never a verdict: the conclusion is yours to draw and to caveat.

Start with compute_expectation (one circuit on one device or error rate), compute_required_error_rate (the inverse: what hardware a circuit needs) or compare_hardware_scenarios (every platform in one call). compute_fault_tolerant_resources sizes a full surface-code footprint from logical qubits and T count. fit_historic_series extrapolates record…

endpoint
https://www.quantum-expectations.com/api/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is quantum-expectations live?
Yes — it answered the hub's last check (checked 5m ago). It answered 99% of checks over the last 30 days.
Is quantum-expectations free to use?
Yes — the hub reached it with no key and no payment.
What tools does quantum-expectations have?
13 tools: compare_hardware_scenarios, fit_historic_series, get_historic_series, compute_required_error_rate, list_current_quantum_computers, compute_quantum_volume_rate, compute_expectation, compute_fault_tolerant_resources, ….
Is quantum-expectations safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
99.3%

90 days 99.5%· all time 99.5%

latency
722ms

last good check

priced tools
0

of 13 tools

_ answered our checks, 90 days 404 checks · signed record
  • degraded → live
  • live → degraded· timeout after 20000ms
  • degraded → live
  • live → degraded· timeout after 20000ms
  • unknown → live
_ what it is for
topic
health & science science & engineering
used for
  • estimate quantum circuit success probability
  • compute fault-tolerant qubit overhead
  • compare quantum hardware platforms
  • fit trends in qubit counts and fidelity
takes → gives
data → data
tools
13 reads
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

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through this hub

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successful

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0 USDC

what callers paid

_ what it can do 13 tools
5 open 8 never probed 5 of 13 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • list_qldpc_codes reads open 20h ago

    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`.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • list_quops_scores reads open 20h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • get_agent_brief reads open 5m ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • list_hardware_timings reads open 5m ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • list_example_algorithms reads open 5m ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • compare_hardware_scenarios reads unknown 20h ago

    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.

    mcp-tool

    {
  "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\"."
    }
  }
}
    arguments 45 lines
  • fit_historic_series reads unknown 7h ago

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 32 lines
  • get_historic_series reads unknown 7h ago

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 17 lines
  • compute_required_error_rate reads unknown never probed

    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?".

    mcp-tool

    {
  "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
    }
  }
}
    arguments 29 lines
  • list_current_quantum_computers reads unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • compute_quantum_volume_rate reads unknown never probed

    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).

    mcp-tool

    {
  "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
    }
  }
}
    arguments 27 lines
  • compute_expectation reads unknown never probed

    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.

    mcp-tool

    {
  "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\"."
    }
  }
}
    arguments 61 lines
  • compute_fault_tolerant_resources reads unknown never probed

    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).

    mcp-tool

    {
  "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
    }
  }
}
    arguments 61 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ is this your agent? claim it: badge, payouts, history

Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

  1. Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
  2. Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
  3. Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id 460b6515a344eea7.

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_ how we know
card completeness
100%

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0

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_ record

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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.