gauge_variant.txt

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# Gauge Variant Outputs
The raw estimated gate set, and then some useful derived quantities.  These quanties are <q>gauge-dependent</q>, meaning they will depend on unphysical gauge degrees of freedom that are a necessary byproduct of estimating an entire gate set at once (akin to a freedom of reference frame).  After finding a best-estimate based on the (physical) data, GST optimizes within the space of all (unphysical) gauge degrees of freedom using the parameters in Table

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### Gauge Optimization Details. 
A list of the parameters used when performing the gauge optimization that produced the final GST results found in subsequent tables and figures.
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ws.GaugeOptParamsTable(goparams)

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### SPAM error metrics
Metrics that quantify errors in the *SPAM operations* -- the estimated initial state preparation[s] and POVM measurement -- with respect to the ideal target operations
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ws.SpamVsTargetTable(mdl, target_model, cri)

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### Individual gate error metrics
Metrics that quantify errors in each individual estimated logic gate, with respect to the ideal target gates.
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ws.GatesVsTargetTable(mdl, target_model, cri, 
                      display=('inf','agi','trace','diamond','nuinf','nuagi'))

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### Per-germ error metrics
Metrics that quantify errors in each germ, with respect to the germ composed from the ideal target gates.
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ws.GatesVsTargetTable(mdl, target_model, None, 
                      display=('inf','trace','nuinf'), virtual_ops=germs)




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## Raw Estimates

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### The GST estimate of the SPAM operations. Compares the estimated SPAM operations to those desired for convenience.
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ws.SpamTable([target_model, mdl],
             ['Target','Estimated'],
             'boxes', cri, include_hs_vec=False)

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### The GST estimate of the logic gate operations.
Compares the *ideal* (generally unitary) logic gates (second column, also in targetGatesetTable) with those *estimated* by GST (third column).  Each gate is represented as a $d^2\times d^2$ *superoperator* that acts by matrix multiplication on vectors in $\mathcal{B}(\mathcal{H})$.  Matrices are displayed using a heat map that ranges between 1.0 (red) and -1.0 (blue).  Note that it is impossible to discern even order-1%% deviations from the ideal using this color scale, and one should rely on other analysis for more a precise comparison.
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ws.GatesTable([target_model, mdl], ['Target','Estimated'], "boxes", cri)



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## Gate Decompositions

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### Decomposition of estimated gates.
A rotation axis and angle are extracted from each gate by considering the projection of its logarithm onto a the Pauli Hamiltonian projectors.  The direction and magnitude (up to a conventional constant) give the axis and angle respectively.
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ws.GateDecompTable(mdl, target_model, None) #using cri can take a long time

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### Spectra of estimated gates' Choi representation
The eigenvalues of the Choi representation of each estimated gate.  Magnitudes of *negative* values are plotted using <span style="color:red">red</span> bars. Unitary gates have a spectrum $(1,0,0\ldots)$, just like pure quantum states.  Negative eigenvalues are non-physical, and may represent either statistical fluctuations or violations of the CPTP gate model.
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ws.ChoiTable(mdl, None, cri, display=("barplot",))



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## Gate Error Generators

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### Logic gate error generators.
A heat map of the Error Generator for each gate, which is the Lindbladian $\mathbb{L}$ that describes *how* the gate is failing to match the target, along with the result of projecting each generator onto some subspaces of the error generator space.
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errgen_type = "logTiG" # or "logGTi" or "logG-logT"
ws.ErrgenTable(mdl, target_model, cri, ("errgen","H","S","A"), "boxes", errgen_type)