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pygsti.algorithms.do_lgst.html
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pygsti.algorithms.do_lgst.html
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<h1>pygsti.algorithms.do_lgst<a class="headerlink" href="#pygsti-algorithms-do-lgst" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pygsti.algorithms.do_lgst">
<code class="descclassname">pygsti.algorithms.</code><code class="descname">do_lgst</code><span class="sig-paren">(</span><em>dataset</em>, <em>specs</em>, <em>targetGateset=None</em>, <em>gateLabels=None</em>, <em>gateLabelAliases={}</em>, <em>spamDict=None</em>, <em>guessGatesetForGauge=None</em>, <em>svdTruncateTo=0</em>, <em>identityVec=None</em>, <em>verbosity=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pygsti.algorithms.do_lgst" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Linear-inversion Gate Set Tomography on the dataset.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>dataset</strong> (<a class="reference internal" href="pygsti.objects.DataSet.html#pygsti.objects.DataSet" title="pygsti.objects.DataSet"><em>DataSet</em></a>) – The data used to generate the LGST estimates</li>
<li><strong>specs</strong> (<em>2-tuple</em>) – A (rhoSpecs,ESpecs) tuple usually generated by calling build_spam_specs(...)</li>
<li><strong>targetGateset</strong> (<em>GateSet, optional</em>) – A gateset used to specify which gate labels should be estimated, a guess
for the in which gauge these estimates should be returned, and the SPAM
labels used to connect the dataset values to rhoVec and EVec indices.</li>
<li><strong>gateLabels</strong> (<em>list, optional</em>) – A list of which gate labels (or aliases) should be estimated.
Defaults to the gate labels in targetGateset.
e.g. [‘Gi’,’Gx’,’Gy’,’Gx2’]</li>
<li><strong>gateLabelAliases</strong> (<em>dictionary, optional</em>) – Dictionary whose keys are gate label “aliases” and whose values are tuples
corresponding to what that gate label should be expanded into before querying
the dataset.
Defaults to the empty dictionary (no aliases defined)
e.g. gateLabelAliases[‘Gx^3’] = (‘Gx’,’Gx’,’Gx’)</li>
<li><strong>spamDict</strong> (<em>dictionary, optional</em>) – Dictionary mapping (rhoVec_index,EVec_index) integer tuples to string spam labels.
Defaults to the spam dictionary of targetGateset
e.g. spamDict[(0,0)] == “plus”</li>
<li><strong>guessGatesetForGauge</strong> (<em>GateSet, optional</em>) – A gateset used to compute a gauge transformation that is applied to
the LGST estimates before they are returned. This gauge transformation
is computed such that if the estimated gates matched the gateset given,
then the gate matrices would match, i.e. the gauge would be the same as
the gateset supplied.
Defaults to targetGateset.</li>
<li><strong>svdTruncateTo</strong> (<em>int, optional</em>) – The Hilbert space dimension to truncate the gate matrices to using
a SVD to keep only the largest svdToTruncateTo singular values of
the I_tildle LGST matrix.
Defaults to 0 (no truncation)</li>
<li><strong>identityVec</strong> (<em>numpy array, optional</em>) – The vectorized identity density matrix in whatever basis is being
used. Size should be [ dmDim^2, 1], e.g. numpy.array([[1.41],[0],[0],[0]]).
Defaults to that of the targetGateset.</li>
<li><strong>verbosity</strong> (<em>int, optional</em>) – How much detail to send to stdout.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">A gateset containing all of the estimated labels (or aliases)</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Gateset</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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