Docs preview for PR #4328.

cuda-quantum-bot · cuda-quantum-bot · commit 2e78236cecb2 · 2026-04-22T18:13:19.000Z
diff --git a/pr-4328/_sources/applications/python/divisive_clustering_coresets.ipynb.txt b/pr-4328/_sources/applications/python/divisive_clustering_coresets.ipynb.txt
@@ -651,7 +651,7 @@
    "source": [
     "threshold_height = 1\n",
     "clusters = dendo.get_clusters_using_height(threshold_height)\n",
-    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\"]\n",
+    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\", \"cyan\", \"magenta\", \"brown\"]\n",
     "dendo.plot_dendrogram(\n",
     "    plot_title=\"Dendrogram of Coreset using VQE\",\n",
     "    colors=colors,\n",
diff --git a/pr-4328/applications/python/adapt_qaoa.html b/pr-4328/applications/python/adapt_qaoa.html
@@ -1180,7 +1180,7 @@ <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="dd2235b724c74424b5ed74dca4247542" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 1000px;" /></p>
+<div><p><img alt="f4e3fc8001214892946d3aa0fe6d546d" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 1000px;" /></p>
 </div><div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
diff --git a/pr-4328/applications/python/adapt_qaoa.md b/pr-4328/applications/python/adapt_qaoa.md
@@ -2114,7 +2114,7 @@ explained above.
 
 <div>
 
-![dd2235b724c74424b5ed74dca4247542](../../_images/adapt-qaoa.png){.no-scaled-link
+![f4e3fc8001214892946d3aa0fe6d546d](../../_images/adapt-qaoa.png){.no-scaled-link
 style="width: 1000px;"}
 
 </div>
diff --git a/pr-4328/applications/python/adapt_vqe.html b/pr-4328/applications/python/adapt_vqe.html
@@ -1177,7 +1177,7 @@ <h1>ADAPT-VQE algorithm<a class="headerlink" href="#ADAPT-VQE-algorithm" title="
 <p>7- Perform a VQE experiment to re-optimize all parameters in the ansatz.</p>
 <p>8- go to step 4</p>
 <p>Below is a Schematic depiction of the ADAPT-VQE algorithm</p>
-<div><p><img alt="c726d9b3f6154a9c897a0890db8de4ed" class="no-scaled-link" src="../../_images/adapt-vqe.png" style="width: 800px;" /></p>
+<div><p><img alt="e3d96b9be3094d33b2a722953c0c0a38" class="no-scaled-link" src="../../_images/adapt-vqe.png" style="width: 800px;" /></p>
 </div><div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
diff --git a/pr-4328/applications/python/adapt_vqe.md b/pr-4328/applications/python/adapt_vqe.md
@@ -2082,7 +2082,7 @@ Below is a Schematic depiction of the ADAPT-VQE algorithm
 
 <div>
 
-![c726d9b3f6154a9c897a0890db8de4ed](../../_images/adapt-vqe.png){.no-scaled-link
+![e3d96b9be3094d33b2a722953c0c0a38](../../_images/adapt-vqe.png){.no-scaled-link
 style="width: 800px;"}
 
 </div>
diff --git a/pr-4328/applications/python/deutsch_algorithm.html b/pr-4328/applications/python/deutsch_algorithm.html
@@ -1237,7 +1237,7 @@ <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="f847b7e9f88c4f249183145b31781522" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
+<p><img alt="711ed9fd37664e06ad2e80b0f76ee811" 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">
@@ -1285,7 +1285,7 @@ <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="0b2d2200c1f34dc09d324a4bc247bb52" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
+<p><img alt="df6985f7b07143b6b51dfd97d5ec3093" 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>
diff --git a/pr-4328/applications/python/deutsch_algorithm.md b/pr-4328/applications/python/deutsch_algorithm.md
@@ -2163,7 +2163,7 @@ number, the result is 0 otherwise 1.
 ::: {#Quantum-oracles .section}
 ## Quantum oracles[¶](#Quantum-oracles "Permalink to this heading"){.headerlink}
 
-![f847b7e9f88c4f249183145b31781522](../../_images/oracle.png){.no-scaled-link
+![711ed9fd37664e06ad2e80b0f76ee811](../../_images/oracle.png){.no-scaled-link
 style="width: 300px; height: 150px;"}
 
 Suppose we have [\\(f(x): \\{0,1\\} \\longrightarrow \\{0,1\\}\\)]{.math
@@ -2269,7 +2269,7 @@ balanced function? If constant, [\\(f(0) = f(1)\\)]{.math .notranslate
 We step through the circuit diagram below and follow the math after the
 application of each gate.
 
-![0b2d2200c1f34dc09d324a4bc247bb52](../../_images/deutsch.png){.no-scaled-link
+![df6985f7b07143b6b51dfd97d5ec3093](../../_images/deutsch.png){.no-scaled-link
 style="width: 500px; height: 210px;"}
 
 ::: {.math .notranslate .nohighlight}
diff --git a/pr-4328/applications/python/divisive_clustering_coresets.html b/pr-4328/applications/python/divisive_clustering_coresets.html
@@ -1653,7 +1653,7 @@ <h2>Divisive Clustering Function<a class="headerlink" href="#Divisive-Clustering
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">threshold_height</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">clusters</span> <span class="o">=</span> <span class="n">dendo</span><span class="o">.</span><span class="n">get_clusters_using_height</span><span class="p">(</span><span class="n">threshold_height</span><span class="p">)</span>
-<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;purple&quot;</span><span class="p">,</span> <span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="s2">&quot;yellow&quot;</span><span class="p">]</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;purple&quot;</span><span class="p">,</span> <span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="s2">&quot;yellow&quot;</span><span class="p">,</span> <span class="s2">&quot;cyan&quot;</span><span class="p">,</span> <span class="s2">&quot;magenta&quot;</span><span class="p">,</span> <span class="s2">&quot;brown&quot;</span><span class="p">]</span>
 <span class="n">dendo</span><span class="o">.</span><span class="n">plot_dendrogram</span><span class="p">(</span>
     <span class="n">plot_title</span><span class="o">=</span><span class="s2">&quot;Dendrogram of Coreset using VQE&quot;</span><span class="p">,</span>
     <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
diff --git a/pr-4328/applications/python/divisive_clustering_coresets.ipynb b/pr-4328/applications/python/divisive_clustering_coresets.ipynb
@@ -651,7 +651,7 @@
    "source": [
     "threshold_height = 1\n",
     "clusters = dendo.get_clusters_using_height(threshold_height)\n",
-    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\"]\n",
+    "colors = [\"red\", \"blue\", \"green\", \"black\", \"purple\", \"orange\", \"yellow\", \"cyan\", \"magenta\", \"brown\"]\n",
     "dendo.plot_dendrogram(\n",
     "    plot_title=\"Dendrogram of Coreset using VQE\",\n",
     "    colors=colors,\n",
diff --git a/pr-4328/applications/python/divisive_clustering_coresets.md b/pr-4328/applications/python/divisive_clustering_coresets.md
@@ -2783,7 +2783,7 @@ figure below shows the clusters that are formed at threshold height of
 ::: highlight
     threshold_height = 1
     clusters = dendo.get_clusters_using_height(threshold_height)
-    colors = ["red", "blue", "green", "black", "purple", "orange", "yellow"]
+    colors = ["red", "blue", "green", "black", "purple", "orange", "yellow", "cyan", "magenta", "brown"]
     dendo.plot_dendrogram(
         plot_title="Dendrogram of Coreset using VQE",
         colors=colors,
diff --git a/pr-4328/applications/python/edge_detection.html b/pr-4328/applications/python/edge_detection.html
@@ -1217,7 +1217,7 @@ <h2>Image<a class="headerlink" href="#Image" title="Permalink to this heading">
 <section id="Quantum-Probability-Image-Encoding-(QPIE):">
 <h2>Quantum Probability Image Encoding (QPIE):<a class="headerlink" href="#Quantum-Probability-Image-Encoding-(QPIE):" title="Permalink to this heading">¶</a></h2>
 <p>Lets take as an example a classical 2x2 image (4 pixels). We can label each pixel with its position</p>
-<div><p><img alt="c1fa5e2db8b94f01a530f831d2c08bc2" class="no-scaled-link" src="../../_images/pixels-img.png" style="width: 200px;" /></p>
+<div><p><img alt="1fb6913d47004518994124a77418199e" class="no-scaled-link" src="../../_images/pixels-img.png" style="width: 200px;" /></p>
 </div><p>Each pixel will have its own color intensity represented along with its position label as an 8-bit black and white color. To convert the pixel intensity to probability amplitudes of a quantum state</p>
 <div class="math notranslate nohighlight">
 \[c_i = \frac{I_{yx}}{\sqrt(\sum I^2_{yx})}\]</div>
diff --git a/pr-4328/applications/python/edge_detection.md b/pr-4328/applications/python/edge_detection.md
@@ -2115,7 +2115,7 @@ each pixel with its position
 
 <div>
 
-![c1fa5e2db8b94f01a530f831d2c08bc2](../../_images/pixels-img.png){.no-scaled-link
+![1fb6913d47004518994124a77418199e](../../_images/pixels-img.png){.no-scaled-link
 style="width: 200px;"}
 
 </div>
diff --git a/pr-4328/applications/python/grovers.html b/pr-4328/applications/python/grovers.html
@@ -1259,7 +1259,7 @@ <h3>Good and Bad States<a class="headerlink" href="#Good-and-Bad-States" title="
 <p>Let us now examine the geometric picture behind our current discussion. We’ll consider the ambient Hilbert space to be spanned by the standard basis vectors <span class="math notranslate nohighlight">\(|0\rangle, |1\rangle, \dots, |N-1\rangle\)</span>, where the full dimension is <span class="math notranslate nohighlight">\(N = 2^n\)</span>. Since the uniform superposition state <span class="math notranslate nohighlight">\(|\xi\rangle\)</span> can be expressed as a linear combination of the states <span class="math notranslate nohighlight">\(|G\rangle\)</span> and <span class="math notranslate nohighlight">\(|B\rangle\)</span> with real coefficients, all three states <span class="math notranslate nohighlight">\(|\xi\rangle, |G\rangle,\)</span> and <span class="math notranslate nohighlight">\(|B\rangle\)</span>
 reside in a two-dimensional real subspace of the ambient Hilbert space, which we can visualize as a 2D plane as in the image below. Since, <span class="math notranslate nohighlight">\(|G\rangle\)</span> and <span class="math notranslate nohighlight">\(|B\rangle\)</span> are orthogonal, we can imagine them graphed as unit vectors in the positive <span class="math notranslate nohighlight">\(y\)</span> and positive <span class="math notranslate nohighlight">\(x\)</span> directions, respectively. From our previous expression, <span class="math notranslate nohighlight">\(\ket{\xi} = \sin(\theta) |G\rangle + \cos(\theta) |B\rangle,\)</span> we see that the state <span class="math notranslate nohighlight">\(|\xi\rangle\)</span> forms an angle <span class="math notranslate nohighlight">\(\theta\)</span> with
 <span class="math notranslate nohighlight">\(|B\rangle\)</span>.</p>
-<div style="text-align: center;"><p><img alt="300652ddf02a4e99ad9d42c53d419434" src="../../_images/grovers-2D-plane.png" /></p>
+<div style="text-align: center;"><p><img alt="aafd7b5455ca41cea53370edf8d81eda" src="../../_images/grovers-2D-plane.png" /></p>
 </div><p>Given that the number of marked states <span class="math notranslate nohighlight">\(t\)</span> is typically small compared to <span class="math notranslate nohighlight">\(N\)</span>, it follows that <span class="math notranslate nohighlight">\(\theta = \arcsin\left(\sqrt{\frac{t}{N}}\right)\)</span> is a small angle. This assumption is reasonable, as otherwise, a sufficient number of independent queries to the oracle would likely yield a solution through classical search methods.</p>
 </section>
 <section id="Step-2:-Oracle-application">
@@ -1374,9 +1374,9 @@ <h4>Completing Step 3<a class="headerlink" href="#Completing-Step-3" title="Perm
 <div class="math notranslate nohighlight">
 \[\mathcal{G} = r_\xi \circ r_B = H^{\otimes n} \big( 2|0^{\otimes n} \rangle \langle 0^{\otimes n}| - \text{Id} \big) H^{\otimes n} \mathcal{O}_f.\]</div>
 <p>The circuit diagram below puts together steps 1 through 3:</p>
-<div style="text-align: center;"><p><img alt="11bfefc755f64691b4c783bb8388803a" src="../../_images/grovers-steps1-3.png" /></p>
+<div style="text-align: center;"><p><img alt="8024ea2827f14baaa06a901b6d8daafe" src="../../_images/grovers-steps1-3.png" /></p>
 </div><p>Running this circuit initializes <span class="math notranslate nohighlight">\(\ket{\xi}\)</span> and performs a rotation by <span class="math notranslate nohighlight">\(2\theta\)</span> (twice the angle between <span class="math notranslate nohighlight">\(|\xi\rangle\)</span> and <span class="math notranslate nohighlight">\(|B\rangle\)</span>) in the direction from <span class="math notranslate nohighlight">\(|B\rangle\)</span> to <span class="math notranslate nohighlight">\(|G\rangle\)</span>.</p>
-<div style="text-align: center;"><p><img alt="5455ff6cf70947a4b9b8b2bcdb963a6e" src="../../_images/grovers-full-rotation.png" /></p>
+<div style="text-align: center;"><p><img alt="0b08b2e7057c438184521e9af7dc6412" src="../../_images/grovers-full-rotation.png" /></p>
 </div><p>Let’s verify that the state resulting from one iteration of Grover’s algorithm brings us closer to the good state, <span class="math notranslate nohighlight">\(\ket{G}\)</span>. In particular, notice that the amplitudes of <code class="docutils literal notranslate"><span class="pre">1001</span></code> and <code class="docutils literal notranslate"><span class="pre">1111</span></code> in the resulting state have been amplified compared to the equal superposition of states.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
diff --git a/pr-4328/applications/python/grovers.md b/pr-4328/applications/python/grovers.md
@@ -2307,7 +2307,7 @@ can imagine them graphed as unit vectors in the positive [\\(y\\)]{.math
 [\\(\|B\\rangle\\)]{.math .notranslate .nohighlight}.
 
 ::: {style="text-align: center;"}
-![300652ddf02a4e99ad9d42c53d419434](../../_images/grovers-2D-plane.png)
+![aafd7b5455ca41cea53370edf8d81eda](../../_images/grovers-2D-plane.png)
 :::
 
 Given that the number of marked states [\\(t\\)]{.math .notranslate
@@ -2522,7 +2522,7 @@ which we will denote by [\\(\\mathcal{G}\\)]{.math .notranslate
 The circuit diagram below puts together steps 1 through 3:
 
 ::: {style="text-align: center;"}
-![11bfefc755f64691b4c783bb8388803a](../../_images/grovers-steps1-3.png)
+![8024ea2827f14baaa06a901b6d8daafe](../../_images/grovers-steps1-3.png)
 :::
 
 Running this circuit initializes [\\(\\ket{\\xi}\\)]{.math .notranslate
@@ -2534,7 +2534,7 @@ from [\\(\|B\\rangle\\)]{.math .notranslate .nohighlight} to
 [\\(\|G\\rangle\\)]{.math .notranslate .nohighlight}.
 
 ::: {style="text-align: center;"}
-![5455ff6cf70947a4b9b8b2bcdb963a6e](../../_images/grovers-full-rotation.png)
+![0b08b2e7057c438184521e9af7dc6412](../../_images/grovers-full-rotation.png)
 :::
 
 Let's verify that the state resulting from one iteration of Grover's
diff --git a/pr-4328/applications/python/qm_mm_pe.html b/pr-4328/applications/python/qm_mm_pe.html
@@ -1177,7 +1177,7 @@ <h2>Key concepts:<a class="headerlink" href="#Key-concepts:" title="Permalink to
 </section>
 <section id="PE-VQE-SCF-Algorithm-Steps">
 <h2>PE-VQE-SCF Algorithm Steps<a class="headerlink" href="#PE-VQE-SCF-Algorithm-Steps" title="Permalink to this heading">¶</a></h2>
-<div><p><img alt="4bafdb58d00442bf94e6273b0e9a7f86" class="no-scaled-link" src="../../_images/qm-mm-pe.png" style="width: 600px;" /></p>
+<div><p><img alt="48fe8a67a26b4185b52b9439034277e7" class="no-scaled-link" src="../../_images/qm-mm-pe.png" style="width: 600px;" /></p>
 </div><section id="Step-1:-Initialize-(Classical-pre-processing)">
 <h3>Step 1: Initialize (Classical pre-processing)<a class="headerlink" href="#Step-1:-Initialize-(Classical-pre-processing)" title="Permalink to this heading">¶</a></h3>
 <ul class="simple">
diff --git a/pr-4328/applications/python/qm_mm_pe.md b/pr-4328/applications/python/qm_mm_pe.md
@@ -2073,7 +2073,7 @@ mutual polarization.
 
 <div>
 
-![4bafdb58d00442bf94e6273b0e9a7f86](../../_images/qm-mm-pe.png){.no-scaled-link
+![48fe8a67a26b4185b52b9439034277e7](../../_images/qm-mm-pe.png){.no-scaled-link
 style="width: 600px;"}
 
 </div>
diff --git a/pr-4328/applications/python/quantum_fourier_transform.html b/pr-4328/applications/python/quantum_fourier_transform.html
@@ -1414,7 +1414,7 @@ <h2>Quantum Fourier Transform revisited<a class="headerlink" href="#Quantum-Four
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-CUDA-Q Version  (https://github.com/NVIDIA/cuda-quantum 39cfb7279a82217b613548220ff68fe13d3543d5)
+CUDA-Q Version  (https://github.com/NVIDIA/cuda-quantum 7ab08ec4343c00165e6d8d0f84877c6e3396faeb)
 </pre></div></div>
 </div>
 </section>
diff --git a/pr-4328/applications/python/quantum_fourier_transform.ipynb b/pr-4328/applications/python/quantum_fourier_transform.ipynb
diff --git a/pr-4328/applications/python/quantum_fourier_transform.md b/pr-4328/applications/python/quantum_fourier_transform.md
diff --git a/pr-4328/applications/python/skqd.html b/pr-4328/applications/python/skqd.html
diff --git a/pr-4328/applications/python/skqd.md b/pr-4328/applications/python/skqd.md
diff --git a/pr-4328/examples/python/performance_optimizations.html b/pr-4328/examples/python/performance_optimizations.html
diff --git a/pr-4328/examples/python/performance_optimizations.md b/pr-4328/examples/python/performance_optimizations.md
diff --git a/pr-4328/searchindex.js b/pr-4328/searchindex.js
diff --git a/pr-4328/sphinx/applications/python/divisive_clustering_coresets.ipynb b/pr-4328/sphinx/applications/python/divisive_clustering_coresets.ipynb
