Docs preview for PR #2644.

Author: cuda-quantum-bot

pr-2644/applications/python/adapt_qaoa.html

@@ -783,7 +783,7 @@ <h1>ADAPT-QAOA algorithm<a class="headerlink" href="#ADAPT-QAOA-algorithm" title
 parameter</p>
 <p>3- Optimize all parameters currently in the Ansatz <span class="math notranslate nohighlight">\(\beta_m, \gamma_m = 1, 2, ...k\)</span> such that <span class="math notranslate nohighlight">\(\braket{\psi (k)|H_C|\psi(k)}\)</span> is minimized, and return to the second step.</p>
 <p>Below is a schematic representation of the ADAPT-QAOA algorithm explained above.</p>
-<div><p><img alt="fd64962134fa41acbff8628095498fbb" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 600px;" /></p>
+<div><p><img alt="9a1400bc79e5461c8299ff924c80d942" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 600px;" /></p>
 </div><div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>

pr-2644/applications/python/deutschs_algorithm.html

@@ -840,7 +840,7 @@ <h2>XOR <span class="math notranslate nohighlight">\(\oplus\)</span><a class="he
 </section>
 <section id="Quantum-oracles">
 <h2>Quantum oracles<a class="headerlink" href="#Quantum-oracles" title="Permalink to this heading">¶</a></h2>
-<p><img alt="80d1638c8a9e43358fb80a0e98b6d8a3" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
+<p><img alt="ff1669d820134a7080c442a4b9d05c32" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
 <p>Suppose we have <span class="math notranslate nohighlight">\(f(x): \{0,1\} \longrightarrow \{0,1\}\)</span>. We can compute this function on a quantum computer using oracles which we treat as black box functions that yield the output with an appropriate sequence of logical gates.</p>
 <p>Above you see an oracle represented as <span class="math notranslate nohighlight">\(U_f\)</span> which allows us to transform the state <span class="math notranslate nohighlight">\(\ket{x}\ket{y}\)</span> into:</p>
 <div class="math notranslate nohighlight">
@@ -888,7 +888,7 @@ <h2>Quantum parallelism<a class="headerlink" href="#Quantum-parallelism" title="
 <h2>Deutsch’s Algorithm:<a class="headerlink" href="#Deutsch's-Algorithm:" title="Permalink to this heading">¶</a></h2>
 <p>Our aim is to find out if <span class="math notranslate nohighlight">\(f: \{0,1\} \longrightarrow \{0,1\}\)</span> is a constant or a balanced function? If constant, <span class="math notranslate nohighlight">\(f(0) = f(1)\)</span>, and if balanced, <span class="math notranslate nohighlight">\(f(0) \neq f(1)\)</span>.</p>
 <p>We step through the circuit diagram below and follow the math after the application of each gate.</p>
-<p><img alt="f5faf4f8c88648d2a26940509771c3ba" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
+<p><img alt="a186cbb9cdfd44e1b5ed4775a1cb5c1f" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
 <div class="math notranslate nohighlight">
 \[\ket{\psi_0}  =  \ket{01}
 \tag{1}\]</div>

pr-2644/applications/python/quantum_transformer.html

@@ -768,9 +768,9 @@ <h1>Optimizing Performance<a class="headerlink" href="#Optimizing-Performance" t
 <section id="Gate-Fusion">
 <h2>Gate Fusion<a class="headerlink" href="#Gate-Fusion" title="Permalink to this heading">¶</a></h2>
 <p>Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below). By targeting the <code class="docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend and setting the <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code> environment variable, you can select the degree of fusion that takes place. A full command line example would look like <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE=4</span> <span class="pre">python</span> <span class="pre">c2h2VQE.py</span> <span class="pre">--target</span> <span class="pre">nvidia</span> <span class="pre">--target-option</span> <span class="pre">fp64,mgpu</span></code></p>
-<p><img alt="f688bfad97964a5e99ea16f4d012f82b" src="../../_images/gate-fuse.png" /></p>
+<p><img alt="8158404b9fac43dcb94ccad916fe26b8" src="../../_images/gate-fuse.png" /></p>
 <p>The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation. See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.</p>
-<p><img alt="e12d610f3960497184062d0f2272acc1" src="../../_images/gatefusion.png" /></p>
+<p><img alt="7bbf3b984f194e179f35d91408bbb836" src="../../_images/gatefusion.png" /></p>
 </section>
 </section>
 

pr-2644/examples/python/performance_optimizations.html

@@ -102,6 +102,11 @@ def print_results(success, failed, skipped=[]):
             ## See: https://github.com/NVIDIA/cuda-quantum/issues/2577
             if os.path.basename(notebook_filename) in ["afqmc.ipynb"]:
                 notebooks_skipped.append(notebook_filename)
+            ## See: https://github.com/NVIDIA/cuda-quantum/issues/2689
+            if os.path.basename(notebook_filename) in [
+                    "quantum_transformer.ipynb"
+            ]:
+                notebooks_skipped.append(notebook_filename)
             elif (validate(notebook_filename, available_backends)):
                 if (execute(notebook_filename)):
                     notebooks_success.append(notebook_filename)