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Randomized-Benchmarking Fit
x402
Randomized-Benchmarking Fit is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.100014/call, status unknown (last checked 2026-10-20).
Fits randomized-benchmarking decay observations to extract fidelity estimates, residuals, and protocol-bound error bounds for quantum gate characterization.
Price / call$0.100014
Success—
StatusUnknown
How this service works
Fit supplied randomized-benchmarking decay observations with residuals and protocol-bound error estimates. Not a platform-independent quantum-computer ranking. New modular adapter: retained result recovery for 24 hours, minimal receipt metadata for 90 days. Existing live route unchanged.
Output
Returns a fitted decay model for the supplied randomized-benchmarking survival probability observations, including fit parameters (e.g. depolarizing rate, SPAM offset), per-point residuals, and protocol-bound error estimates on the extracted gate fidelity or average error per gate.
Example use cases
Single-qubit gate fidelity extraction
“I ran a single-qubit randomized benchmarking experiment and got survival probabilities [0.97, 0.93, 0.85, 0.74, 0.58] at sequence lengths [1, 5, 10, 20, 40]. Can you fit the decay curve for a dimension-2 system with offset 0.25 using public data class and give me the fidelity and residuals?”
Two-qubit RB protocol error estimation
“Fit my two-qubit randomized benchmarking results: lengths [2, 4, 8, 16, 32, 64] and survival probabilities [0.96, 0.91, 0.83, 0.71, 0.55, 0.38], dimension 4, no offset, public data — I need the error-per-gate estimate with protocol-bound error bars.”
Synthetic RB data validation for simulator
“I have synthetic randomized benchmarking data for a 16-dimensional system: sequence lengths [1, 3, 10, 30, 100] and survival probabilities [0.99, 0.97, 0.91, 0.78, 0.52], offset 0.5, data class synthetic. Fit the decay and return residuals so I can validate my quantum simulator's noise model.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/compute/quantum/randomized-benchmarking-fit?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have raw randomized-benchmarking experimental or synthetic sequence-length/survival-probability data and need a rigorous curve fit with residuals and protocol-bound error estimates. Prefer this over general curve-fitting tools when you need quantum-protocol-aware error bounds rather than generic statistical fits, and when your data is already in the RB decay format.
Side effects
- Mismatched array lengths between 'lengths' and 'survival' — returns validation error
- 'dimension' outside [2, 256] — rejected with parameter bounds error
- 'offset' outside [0, 1] — rejected with range validation error
- 'survival' values outside physically valid probability range may degrade fit quality
- 'dataClass' enum not matching 'public' or 'synthetic' — schema rejection
- Fewer than 3 data points may cause underdetermined fit with unreliable error bounds
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| offset | number | |
| lengths | array | |
| survival | array | |
| dataClass | string | |
| dimension | integer |
Tags
x402
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