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pygsti.algorithms.do_iterative_exlgst.html
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<h1>pygsti.algorithms.do_iterative_exlgst<a class="headerlink" href="#pygsti-algorithms-do-iterative-exlgst" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pygsti.algorithms.do_iterative_exlgst">
<code class="descclassname">pygsti.algorithms.</code><code class="descname">do_iterative_exlgst</code><span class="sig-paren">(</span><em>dataset</em>, <em>startGateset</em>, <em>specs</em>, <em>gateStringSetsToUseInEstimation</em>, <em>targetGateset=None</em>, <em>spamDict=None</em>, <em>guessGatesetForGauge=None</em>, <em>svdTruncateTo=0</em>, <em>maxiter=100000</em>, <em>maxfev=None</em>, <em>tol=1e-06</em>, <em>opt_gates=True</em>, <em>opt_G0=True</em>, <em>regularizeFactor=0</em>, <em>returnErrorVec=False</em>, <em>returnAll=False</em>, <em>gateStringSetLabels=None</em>, <em>verbosity=0</em>, <em>check_jacobian=False</em><span class="sig-paren">)</span><a class="headerlink" href="#pygsti.algorithms.do_iterative_exlgst" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Iterated Extended 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 Extended-LGST estimates</li>
<li><strong>startGateset</strong> (<a class="reference internal" href="pygsti.objects.GateSet.html#pygsti.objects.GateSet" title="pygsti.objects.GateSet"><em>GateSet</em></a>) – The GateSet used as a starting point for the least-squares
optimization.</li>
<li><strong>specs</strong> (<em>2-tuple</em>) – A (rhoSpecs,ESpecs) tuple usually generated by calling build_spam_specs(...)</li>
<li><strong>gateStringSetsToUseInEstimation</strong> (<em>list of lists of (tuples or GateStrings)</em>) – The i-th element is a list of the gate strings to be used in the i-th iteration
of extended-LGST. Each element of these lists is a gate string, specifed as
either a GateString object or as a tuple of gate labels (but all must be specified
using the same type).
e.g. [ [ (), (‘Gx’,) ], [ (), (‘Gx’,), (‘Gy’,) ], [ (), (‘Gx’,), (‘Gy’,), (‘Gx’,’Gy’) ] ]</li>
<li><strong>targetGateset</strong> (<em>GateSet, optional</em>) – A gateset used to provide a guess for gauge in which LGST estimates should be returned,
and the SPAM labels used to connect the dataset values to rhoVec and EVec indices.</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.
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>maxiter</strong> (<em>int, optional</em>) – Maximum number of iterations in each of the least squares optimizations</li>
<li><strong>maxfev</strong> (<em>int, optional</em>) – Maximum number of function evaluations for each of the least squares optimizations
Defaults to maxiter</li>
<li><strong>tol</strong> (<em>float, optional</em>) – The tolerance for each of the least squares optimizations.</li>
<li><strong>opt_gates</strong> (<em>bool, optional</em>) – Whether the gate matrices should be optimized</li>
<li><strong>opt_G0</strong> (<em>bool, optional</em>) – Whether the first row of gate matrices should be optimized. If False, then
when the startGateset has TP gates this will now be changed during the
optimization and the resulting gates are guaranteed to be TP.</li>
<li><strong>regularizeFactor</strong> (<em>float, optional</em>) – Multiplicative prefactor of L2-like regularization term that penalizes gateset entries
which have absolute value greater than 1. When set to 0, no regularization is applied.</li>
<li><strong>returnErrorVec</strong> (<em>bool, optional</em>) – If True, return (errorVec, gateset), or (errorVecs, gatesets) if
returnAll == True, instead of just the gateset or gatesets.</li>
<li><strong>returnAll</strong> (<em>bool, optional</em>) – If True return a list of gatesets (and errorVecs if returnErrorVec == True),
one per iteration, instead of the results from just the final iteration.</li>
<li><strong>gateStringSetLabels</strong> (<em>list of strings, optional</em>) – An identification label for each of the gate string sets (used for displaying
progress). Must be the same length as gateStringSetsToUseInEstimation.</li>
<li><strong>verbosity</strong> (<em>int, optional</em>) – How much detail to send to stdout.</li>
<li><strong>check_jacobian</strong> (<em>boolean, optional</em>) – If True, compare the analytic jacobian with a forward finite difference jacobean
and print warning messages if there is disagreement.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
<li><em>gateset if returnAll == False and returnErrorVec == False</em></li>
<li><em>gatesets if returnAll == True and returnErrorVec == False</em></li>
<li><em>(errorVec, gateset) if returnAll == False and returnErrorVec == True</em></li>
<li><em>(errorVecs, gatesets) if returnAll == True and returnErrorVec == True</em> –
where errorVec is a numpy array of minimum error values v = f(x_min), where f(x)**2 is
the function being minimized, gateset is the GateSet containing the final estimated gates.
In cases when returnAll == True, gatesets and errorVecs are lists whose i-th elements are the
errorVec and gateset corresponding to the results of the i-th iteration.</li>
</ul>
</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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