Quantum Expectations
Quantum error-correction feasibility: success probability, qubit overhead, records, trends.
| Endpoint | https://www.quantum-expectations.com/api/mcp (streamable-http) |
|---|---|
| Registry version | 0.8.1 |
| Now | 13 tools · grade A (93) · pin ts_7ece1c94a814b7021c57a01b686dd068 · protocol 2025-06-18 |
| First / last listed | 2026-11-05 01:20 UTC / 2026-11-05 01:20 UTC |
| Grade over time | 2026-11-05: A (93), 13 tools |
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Tools: compute_expectation compute_required_error_rate compute_fault_tolerant_resources compare_hardware_scenarios list_current_quantum_computers list_quops_scores list_hardware_timings compute_quantum_volume_rate fit_historic_series list_example_algorithms list_qldpc_codes get_historic_series get_agent_brief
compute_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 se params: qubitErrorRate, hardwareId, connectivity, numQubits*, compDepth*, useErrorCorrection, errorCorrectionCode, distanceSurfaceCode, verbose |
compute_required_error_rate | 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 cu params: numQubits*, compDepth*, acceptableErrorRatePercent* |
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 params: numLogicalQubits*, tCount*, qubitErrorRate, hardwareId, targetSuccessProbability, dataBlock, cycleTimeSeconds |
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. params: numQubits*, compDepth*, useErrorCorrection, errorCorrectionCode, distanceSurfaceCode, hardwareIds |
list_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 f params: none |
list_quops_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 params: none |
list_hardware_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 ord params: none |
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 prod params: quantumVolume*, t2QSeconds*, tMeasurementSeconds* |
fit_historic_series | 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) params: seriesType*, hardwareType*, targetValue |
list_example_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 res params: none |
list_qldpc_codes | 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 <= t params: none |
get_historic_series | 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. params: seriesType* |
get_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. params: none |
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