Servidor MCP
Quantum Expectations
com.quantum-expectations/quantum-expectations
Qué hace este MCP
Models quantum-computing error correction, hardware feasibility, logical and physical resource requirements, performance rates, and historical hardware trends.
Capacidades
Herramientas
13
herramientas
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.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['numQubits', 'compDepth'], 'properties': {'compDepth': {'type': 'integer', 'maximum': 10000000000000, 'minimum': 1}, 'numQubits': {'type': 'integer', 'maximum': 1000000, 'minimum': 1}, 'hardwareIds': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Subset of QUANTUM_COMPUTERS ids to compare. Omit to compare every entry.'}, 'useErrorCorrection': {'type': 'boolean', 'default': False}, 'distanceSurfaceCode': {'type': 'number', 'description': 'Surface code distance: odd integer in [3, 31]. Required when useErrorCorrection=true and the surface code is selected; ignored when a qLDPC code is selected.'}, 'errorCorrectionCode': {'enum': ['surface', 'bb-144-12-12', 'bb-288-12-18', 'surface-code'], 'type': 'string', 'description': 'Either "surface" (default when useErrorCorrection=true) or a qLDPC code id. "surface-code" is accepted as an alias for "surface".'}}}
compute_expectation
Compute Quantum Expectation
Given a quantum circuit (2-qubit error rate p, qubit count n, depth d, optional connectivity class), compute the effective error rate, success probability, and optional surface-code or qLDPC overhead. Without error correction a 2d-lattice connectivity is charged as a routing multiplier on depth (result.routingOverheadFactor); hardwareId supplies the device's class automatically. The response is self-describing (formulas, assumptions, caveats, glossary, SOTA hardware, historic series with source URLs) so an agent can reason from one call. For the inverse ("what hardware do I need?") use compute_required_error_rate; to rank multiple platforms in one call use compare_hardware_scenarios.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$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 â\x80\x94 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. 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".'}}}
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).
Solo lectura
Idempotente
Esquema de entrada
{'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 â\x89\x88 4â\x80\x937 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 error rate of a SOTA hardware entry.'}, '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': 1e-06, '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 â\x88\x92 target) is split evenly between T-state error and logical (memory/surgery) error, matching Litinski §4.', 'exclusiveMaximum': 1, 'exclusiveMinimum': 0}}}
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).
Solo lectura
Idempotente
Esquema de entrada
{'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 â\x89¥ 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}}}
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?".
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['numQubits', 'compDepth', 'acceptableErrorRatePercent'], 'properties': {'compDepth': {'type': 'integer', 'maximum': 10000000000000, 'minimum': 1, 'description': 'Circuit depth d (sequential 2-qubit gate layers). Accepts values up to 1e13.'}, 'numQubits': {'type': 'integer', 'maximum': 1000000, 'minimum': 1, 'description': 'Number of logical qubits n in the circuit.'}, 'acceptableErrorRatePercent': {'type': 'number', 'maximum': 100, 'description': 'Upper bound on the effective error rate, as a percent. Default website convention is 33 (i.e. â\x89¤33% effective error is "acceptable").', 'exclusiveMinimum': 0}}}
fit_historic_series
Fit Historic Series (log-linear)
Fit a log-linear trend (ln(value) = slope * year + intercept) to one historic series â fidelity or qubit-count â for one hardware type. Atomic primitive: compose with list_current_quantum_computers, compute_required_error_rate, or your own modelling to answer "when might hardware reach X?". residualStdDev is the BIASED (maximum-likelihood) RMS â divides by n, not (n - 2); on small series (n â 3â5) inflate by â(n / (n - 2)) before building confidence intervals.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$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 â\x80\x94 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.'}}}
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.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
get_historic_series
Historic Hardware Series (Fidelity or Qubit Count)
Return the full historic time series â either two-qubit gate error rates ("fidelity") or physical qubit counts ("qubit-count") â broken down by hardware type. Each datapoint carries a source URL. Use this to extrapolate trends â "when might hardware reach X?" â or pair with fit_historic_series for a log-linear fit on one hardware type.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['seriesType'], 'properties': {'seriesType': {'enum': ['fidelity', 'qubit-count'], 'type': 'string', 'description': '"fidelity" â\x86\x92 2-qubit gate error rates; "qubit-count" â\x86\x92 physical qubit counts.'}}}
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.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
list_example_algorithms
Example Quantum Algorithms
Return the curated list of example quantum algorithms with published resource estimates (qubit count, depth/gate count, source paper URL). Useful for comparing what algorithms need vs. what hardware can deliver. Each entry carries a `provenance` field: 'published-circuit' means the figure is reproducible from the source, 'attested-estimate' means the source withholds the circuit and the figure rests on the authors' attestation, with a `provenanceNote` giving the specifics. Carry that caveat whenever you quote an attested figure; do not present it as equivalently sourced.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
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.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
list_qldpc_codes
qLDPC Code Catalog
Return the catalog of supported qLDPC codes (id, label, family, n, k, d, circuitLevelDistance, ancilla counts, roundsPerLogicalOp, threshold, fitCoefficients {c0, c1, c2}, logicalErrorExponent [alpha = d_circ/2], source URLs, provenance, caveats). The per-block, per-syndrome-cycle logical error rate is p^alpha * exp(c0 + c1*p + c2*p^2) for p <= threshold (the source paper's own fitting form; c1 = c2 = 0 means a plain power law). `provenance` names the table or section each constant was read from. `caveats` is an array of source-level qualifications on the entry's constants (loose distance bounds, values a source marks as assumed, numbers that differ between sources); empty when the sources carry none. Read it before quoting a code's logical error rate as firm. Use a code's `id` as the `errorCorrectionCode` input to `compute_expectation`.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
list_quops_scores
Measured QUOPS Scores
Return the measured QUOPS capability 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.
Solo lectura
Idempotente
Esquema de entrada
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
Historial
Cambios recientes en herramientas
Añadido
get_agent_brief
17 de September de 2026 a las 12:37
Añadido
get_historic_series
17 de September de 2026 a las 12:37
Añadido
list_qldpc_codes
17 de September de 2026 a las 12:37
Añadido
list_example_algorithms
17 de September de 2026 a las 12:37
Añadido
fit_historic_series
17 de September de 2026 a las 12:37
Añadido
compute_quantum_volume_rate
17 de September de 2026 a las 12:37
Añadido
list_hardware_timings
17 de September de 2026 a las 12:37
Añadido
list_quops_scores
17 de September de 2026 a las 12:37
Añadido
list_current_quantum_computers
17 de September de 2026 a las 12:37
Añadido
compare_hardware_scenarios
17 de September de 2026 a las 12:37
Añadido
compute_fault_tolerant_resources
17 de September de 2026 a las 12:37
Añadido
compute_required_error_rate
17 de September de 2026 a las 12:37
Añadido
compute_expectation
17 de September de 2026 a las 12:37
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