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Maha Strategies Quantum Measurement Uncertainty — Binomial Shot-Count Statistics
x402
Maha Strategies Quantum Measurement Uncertainty — Binomial Shot-Count Statistics is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012001/call, status unknown (last checked 2026-10-17).
Computes probabilities and binomial confidence intervals from shot-count arrays using a specified interval method and independence assumptions
Price / call$0.012001
Success—
StatusUnknown
How this service works
For agents reviewing shot-count results: return probabilities and binomial confidence intervals with the interval method and independence assumptions.
Output
Returns a structured result object containing computed probabilities and binomial confidence interval boundaries for each supplied shot-count value, along with a version identifier, offer ID (quantum-measurement-uncertainty), input digest, amount in base units (12001), and a receipt digest for auditability.
Example use cases
Quantum circuit shot-count analysis
“I ran a quantum circuit 100 times and got these shot counts: [34, 66, 51, 49, 29, 71]. The data is synthetic. Can you compute the binomial probabilities and confidence intervals so I can assess measurement uncertainty?”
Pass/fail binomial proportion estimation
“I have public pass/fail counts from a hardware trial: [88, 12, 75, 25, 60, 40]. Please calculate the binomial confidence intervals and probabilities for each pair so I know the uncertainty bounds.”
Repeated binary measurement uncertainty report
“For a series of repeated quantum measurements with shot counts [100, 0, 95, 5, 82, 18] — marked as synthetic data — can you give me the binomial confidence intervals and probability estimates assuming independence between shots?”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/micro/quantum-measurement-uncertainty?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have discrete shot-count arrays from quantum measurements or repeated binary trials and need statistically rigorous binomial probabilities with confidence intervals under independence assumptions. It is purpose-built for quantum measurement post-processing workflows where standard statistical libraries are not available inline, and where a compact, auditable per-call result with receipt digest is needed. Prefer it over generic statistics APIs when you need tight integration with the Maha microproduct receipt/audit trail.
Side effects
- Invalid or empty counts array (below minItems:1 or above maxItems:32) returns a validation error
- Unrecognized dataClass value (must be 'public' or 'synthetic') causes schema rejection
- Missing required body fields (dataClass or counts) returns a 400-level error
- Non-integer or null values in counts array may fail schema validation
- Payment failure or insufficient USDC balance prevents endpoint execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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