Mitigation (Zero-Noise Extrapolation & Density-Matrix Diagnostics)
Standard error-mitigation entry points, named the way the field already names them
(Richardson extrapolation, noise factors, zero-noise extrapolation), plus a density-matrix
extension (physical-cone projection, Uhlmann fidelity) and a jax.jit-compatible variant
of every entry point.
::: dense_evolution.mitigation.zne
Density-matrix diagnostics
Two further density-matrix diagnostics, both originated as Colab proposals with real bugs,
fixed and validated in Dense-Evolution-Discovery
before promotion here: a non-commuting-aware divergence (sandwiched_renyi_divergence) and a
single-qubit non-stabilizerness measure (magic_entropy). Both are validation-only, like
uhlmann_fidelity above -- meant to grade a correction against a known reference state, not to
feed into one.
::: dense_evolution.mitigation.renyi
::: dense_evolution.mitigation.magic_entropy
Shadow-based estimation
A classical-shadows-based estimator for magic_entropy above, estimating it from randomized
measurement snapshots instead of the exact density matrix. Different API shape from everything
else on this page -- sampling (sample_classical_shadow) and estimation
(magic_entropy_from_shadows) are separate steps, since shadow data can come from this
simulator's own Born-rule oracle sampling or, in principle, real hardware measurement logs
reconstructed the same way. Not jax.jit-compatible (median-of-means uses numpy.median).
::: dense_evolution.mitigation.magic_entropy_shadows
See also: dense_evolution.healing for the predictive-healing primitives
(calculate_delta_preemp) the healing-adapted extrapolation branch is built on, and
NoiseModel for the Kraus-channel noise used to build the noisy ensembles
these functions correct. Full worked example: Density-matrix ZNE healing.