Statistics¶

The microquantum.analysis.statistics module provides small, dependency-light statistical helpers for repeated measurements. They accept any iterable of real numbers (lists, tuples, NumPy arrays) and reject non-numeric / non-finite input with ValueError.

Functions¶

mean() — arithmetic mean.
variance() — ddof=0 population variance, ddof=1 sample variance.
standard_deviation() — sqrt of variance.
standard_error() — sample standard error of the mean (sample std / sqrt(n); requires >= 2 samples).
confidence_interval() — normal-approximation CI for the mean (presets 0.90 / 0.95 / 0.99, or an explicit z).
minimum() / maximum() / count().

Example¶

from microquantum import (
    confidence_interval,
    count,
    maximum,
    mean,
    minimum,
    standard_deviation,
    standard_error,
    variance,
)

shots = [0.501, 0.504, 0.498, 0.501, 0.499, 0.497]
print(mean(shots))                # ~0.500
print(variance(shots))            # population variance
print(variance(shots, ddof=1))    # sample variance
print(standard_deviation(shots, ddof=1))
print(standard_error(shots))
print(confidence_interval(shots, confidence=0.95))

print(minimum(shots))             # 0.497
print(maximum(shots))             # 0.504
print(count(shots))               # 6

Usage¶

These helpers back SamplingAnalysis, ExpectationAnalysis and ResultAggregator; they are also exported at the package top level for direct use.