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pr-4365/api/languages/python_api.html

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<dl class="py attribute">
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<dt class="sig sig-object py" id="cudaq.State.from_data">
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<span class="sig-name descname"><span class="pre">from_data</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;nanobind.nb_func</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x60902c0&gt;</span></em><a class="headerlink" href="#cudaq.State.from_data" title="Permalink to this definition">¶</a></dt>
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<span class="sig-name descname"><span class="pre">from_data</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;nanobind.nb_func</span> <span class="pre">object&gt;</span></em><a class="headerlink" href="#cudaq.State.from_data" title="Permalink to this definition">¶</a></dt>
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pr-4365/api/languages/python_api.md

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: Print the state to the console.
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[[from_data]{.pre}]{.sig-name .descname}*[ ]{.w}[[=]{.pre}]{.p}[ ]{.w}[\<nanobind.nb_func]{.pre} [object]{.pre} [at]{.pre} [0x60902c0\>]{.pre}*[¶](#cudaq.State.from_data "Permalink to this definition"){.headerlink}
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[[from_data]{.pre}]{.sig-name .descname}*[ ]{.w}[[=]{.pre}]{.p}[ ]{.w}[\<nanobind.nb_func]{.pre} [object\>]{.pre}*[¶](#cudaq.State.from_data "Permalink to this definition"){.headerlink}
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pr-4365/applications/python/adapt_qaoa.html

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parameter</p>
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<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>
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<p>Below is a schematic representation of the ADAPT-QAOA algorithm explained above.</p>
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<div><p><img alt="2d0bafe083e74ab3809efcf3b002db59" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 1000px;" /></p>
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<div><p><img alt="bc0cf50bb60f4197ab27884d068c794e" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 1000px;" /></p>
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</div><div class="nbinput nblast docutils container">
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
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</pre></div>

pr-4365/applications/python/adapt_qaoa.md

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pr-4365/applications/python/adapt_vqe.html

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<p>7- Perform a VQE experiment to re-optimize all parameters in the ansatz.</p>
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<p>8- go to step 4</p>
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<p>Below is a Schematic depiction of the ADAPT-VQE algorithm</p>
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<div><p><img alt="e33c4507141d43008c2ceba4074ee495" class="no-scaled-link" src="../../_images/adapt-vqe.png" style="width: 800px;" /></p>
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<div><p><img alt="9915eb0a0c5444ef837b605387f1f7b4" class="no-scaled-link" src="../../_images/adapt-vqe.png" style="width: 800px;" /></p>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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pr-4365/applications/python/adapt_vqe.md

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pr-4365/applications/python/deutsch_algorithm.html

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</section>
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<section id="Quantum-oracles">
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<h2>Quantum oracles<a class="headerlink" href="#Quantum-oracles" title="Permalink to this heading"></a></h2>
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<p><img alt="f167e39f06b0473fbfe94941bfe94ab8" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
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<p><img alt="bedb13be7c514c04b4bc27d2c1d6067e" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
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<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>
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<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>
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<h2>Deutsch’s Algorithm:<a class="headerlink" href="#Deutsch's-Algorithm:" title="Permalink to this heading"></a></h2>
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<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>
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<p>We step through the circuit diagram below and follow the math after the application of each gate.</p>
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<p><img alt="9d683b5b2c554eb8b6003e3e09c990ce" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
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<div class="math notranslate nohighlight">
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\[\ket{\psi_0} = \ket{01}
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pr-4365/applications/python/deutsch_algorithm.md

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::: {#Quantum-oracles .section}
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## Quantum oracles[](#Quantum-oracles "Permalink to this heading"){.headerlink}
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pr-4365/applications/python/edge_detection.html

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<h2>Quantum Probability Image Encoding (QPIE):<a class="headerlink" href="#Quantum-Probability-Image-Encoding-(QPIE):" title="Permalink to this heading"></a></h2>
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<p>Lets take as an example a classical 2x2 image (4 pixels). We can label each pixel with its position</p>
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<div><p><img alt="5da4d496eceb41c0a462c4cd4be44968" class="no-scaled-link" src="../../_images/pixels-img.png" style="width: 200px;" /></p>
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<div><p><img alt="a67959bb5f3d4e2b9797096940bdb047" class="no-scaled-link" src="../../_images/pixels-img.png" style="width: 200px;" /></p>
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\[c_i = \frac{I_{yx}}{\sqrt(\sum I^2_{yx})}\]</div>

pr-4365/applications/python/edge_detection.md

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