Overview

Analysis is backend-independent and consensus-based: it consumes the existing result/record contracts rather than inventing new output formats. The four analysers share a consistent JSON-safe surface (to_dict() / to_json()).

The families

Analyser	What it computes
SamplingAnalysis	Measurement counts: outcome probabilities, most/least likely outcome, Shannon entropy, marginals, observable mean/variance/std.
ExpectationAnalysis	The BackendResult.expectations {label: value} contract: per-label mean / variance / std / standard error and a parameter-to-expectation mapping.
StateAnalysis	Statevectors (normalization, probabilities, most probable state, diagonal observables) and density matrices (trace, purity, diagonal measurement probabilities).
ResultAggregator	Group records by parameter bindings, backend, status or dotted-path accessors — always preserving the original records.
microquantum.analysis.statistics	Reusable population-vs-sample variance (ddof), standard error, confidence intervals and min/max/count.
microquantum.analysis.aggregation	Grouping helpers that map raw results -> aggregation -> derived analysis (never replacing raw results with summaries).

Inputs accepted

Analysers accept a BackendResult, an ExecutionRecord, an ExperimentResult, raw dictionaries, or the serialized to_dict() form — so analysis works no matter how the results were produced or transported.

Design principle

raw results -> aggregation -> derived analysis, never raw results -> replace with summary. Experiments stay re-analysable without re-execution.