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pygsti.algorithms.do_mc2gst.html
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<h1>pygsti.algorithms.do_mc2gst<a class="headerlink" href="#pygsti-algorithms-do-mc2gst" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pygsti.algorithms.do_mc2gst">
<code class="descclassname">pygsti.algorithms.</code><code class="descname">do_mc2gst</code><span class="sig-paren">(</span><em>dataset</em>, <em>startGateset</em>, <em>gateStringsToUse</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>opt_SPAM=True</em>, <em>opt_SP0=True</em>, <em>cptp_penalty_factor=0</em>, <em>minProbClipForWeighting=0.0001</em>, <em>probClipInterval=None</em>, <em>useFreqWeightedChiSq=False</em>, <em>regularizeFactor=0</em>, <em>verbosity=0</em>, <em>check=False</em>, <em>check_jacobian=False</em>, <em>gatestringWeights=None</em>, <em>gateLabelAliases=None</em>, <em>memLimit=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pygsti.algorithms.do_mc2gst" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Least-Squares 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 dataset to obtain counts from.</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>gateStringsToUse</strong> (<em>list of (tuples or GateStrings)</em>) – Each tuple contains gate labels and specifies a gate string whose
probabilities are considered when trying to least-squares-fit the
probabilities given in the dataset.
e.g. [ (), (‘Gx’,), (‘Gx’,’Gy’) ]</li>
<li><strong>maxiter</strong> (<em>int, optional</em>) – Maximum number of iterations for the least squares optimization.</li>
<li><strong>maxfev</strong> (<em>int, optional</em>) – Maximum number of function evaluations for the least squares optimization.
Defaults to maxiter.</li>
<li><strong>tol</strong> (<em>float, optional</em>) – The tolerance for the least squares optimization.</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>opt_SPAM</strong> (<em>bool, optional</em>) – Whether the rhoVecs and EVecs should be optimized</li>
<li><strong>opt_SP0</strong> (<em>bool, optional</em>) – Whether the first element of the state preparation vectors
(i.e. the rhoVecs) should be optimized. If False, then rhoVecs
in startingGateset that are trace == 1 will remain trace == 1
after the optimization.</li>
<li><strong>cptp_penalty_factor</strong> (<em>float, optional</em>) – If greater than zero, the least squares optimization also contains CPTP penalty
terms which penalize non-CPTP-ness of the gateset being optimized. This factor
multiplies these CPTP penalty terms.</li>
<li><strong>minProbClipForWeighting</strong> (<em>float, optional</em>) – Sets the minimum and maximum probability p allowed in the chi^2 weights: N/(p*(1-p))
by clipping probability p values to lie within the interval
[ minProbClipForWeighting, 1-minProbClipForWeighting ].</li>
<li><strong>probClipInterval</strong> (<em>2-tuple or None, optional</em>) – (min,max) values used to clip the probabilities predicted by gatesets during LSGST’s
least squares search for an optimal gateset (if not None). Defaults to no clipping.</li>
<li><strong>useFreqWeightedChiSq</strong> (<em>bool, optional</em>) – If True, objective function uses only an approximate chi^2 weighting: N/(f*(1-f))
where f is the frequency obtained from the dataset, instead of the true chi^2: N/(p*(1-p))
where p is a predicted probability. Defaults to False, and only should use
True for backward compatibility.</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>verbosity</strong> (<em>int, optional</em>) – How much detail to send to stdout.</li>
<li><strong>check</strong> (<em>boolean, optional</em>) – If True, perform extra checks within code to verify correctness. Used
for testing, and runs much slower when True.</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. Defaults to False.</li>
<li><strong>gatestringWeights</strong> (<em>numpy array, optional</em>) – An array of length len(gateStringsToUse). Each element scales the
least-squares term of the corresponding gate string in gateStringsToUse.
The default is no weight scaling at all.</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>memLimit</strong> (<em>int, optional</em>) – A rough memory limit in bytes which restricts the amount of intermediate
values that are computed and stored.</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><strong>errorVec</strong> (<em>numpy array</em>) –
Minimum error values v = f(x_best), where f(x)**2 is the function being minimized</li>
<li><strong>gateset</strong> (<em>GateSet</em>) –
GateSet containing the estimated gates.</li>
</ul>
</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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