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‎pr-2542/_sources/api/default_ops.rst.txt‎

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@@ -650,7 +650,7 @@ defined by the qudit level that represents the qumode. If it is applied to a qum
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where the number of photons is already at the maximum value, the operation has no
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effect.
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:math:`U|0\rangle → |1\rangle, U|1\rangle → |2\rangle, U|2\rangle → |3\rangle, \cdots, U|d\rangle → |d\rangle`
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:math:`C|0\rangle → |1\rangle, C|1\rangle → |2\rangle, C|2\rangle → |3\rangle, \cdots, C|d\rangle → |d\rangle`
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where :math:`d` is the qudit level.
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.. tab:: Python
@@ -674,7 +674,7 @@ This operation reduces the number of photons in a qumode up to a minimum value o
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0 representing the vacuum state. If it is applied to a qumode where the number of
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photons is already at the minimum value 0, the operation has no effect.
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:math:`U|0\rangle → |0\rangle, U|1\rangle → |0\rangle, U|2\rangle → |1\rangle, \cdots, U|d\rangle → |d-1\rangle`
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:math:`A|0\rangle → |0\rangle, A|1\rangle → |0\rangle, A|2\rangle → |1\rangle, \cdots, A|d\rangle → |d-1\rangle`
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where :math:`d` is the qudit level.
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.. tab:: Python

‎pr-2542/_sources/examples/python/executing_photonic_kernels.ipynb.txt‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Executing Quantum Photonic Circuits \n",
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"\n",
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"In CUDA-Q, there are 2 ways in which one can execute quantum photonic kernels: \n",
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"\n",
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"1. `sample`: yields measurement counts \n",
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"3. `get_state`: yields the quantum statevector of the computation \n",
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"\n",
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"## Sample\n",
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"\n",
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"Quantum states collapse upon measurement and hence need to be sampled many times to gather statistics. The CUDA-Q `sample` call enables this: \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cudaq\n",
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"import numpy as np\n",
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"\n",
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"qumode_count = 2\n",
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"\n",
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"# Define the simulation target.\n",
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"cudaq.set_target(\"orca-photonics\")\n",
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"\n",
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"# Define a quantum kernel function.\n",
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"\n",
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"\n",
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"@cudaq.kernel\n",
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"def kernel(qumode_count: int):\n",
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" level = qumode_count + 1\n",
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" qumodes = [qudit(level) for _ in range(qumode_count)]\n",
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"\n",
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" # Apply the create gate to the qumodes.\n",
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" for i in range(qumode_count):\n",
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" create(qumodes[i]) # |00⟩ -> |11⟩\n",
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"\n",
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" # Apply the beam_splitter gate to the qumodes.\n",
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" beam_splitter(qumodes[0], qumodes[1], np.pi / 6)\n",
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"\n",
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" # measure all qumodes\n",
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" mz(qumodes)\n",
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"\n",
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"\n",
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"result = cudaq.sample(kernel, qumode_count, shots_count=1000)\n",
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"\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## Get state\n",
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"\n",
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"The `get_state` function gives us access to the quantum statevector of the computation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cudaq\n",
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"import numpy as np\n",
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"\n",
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"qumode_count = 2\n",
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"\n",
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"# Define the simulation target.\n",
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"cudaq.set_target(\"orca-photonics\")\n",
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"\n",
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"# Define a quantum kernel function.\n",
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"\n",
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"\n",
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"@cudaq.kernel\n",
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"def kernel(qumode_count: int):\n",
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" level = qumode_count + 1\n",
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" qumodes = [qudit(level) for _ in range(qumode_count)]\n",
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"\n",
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" # Apply the create gate to the qumodes.\n",
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" for i in range(qumode_count):\n",
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" create(qumodes[i]) # |00⟩ -> |11⟩\n",
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"\n",
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" # Apply the beam_splitter gate to the qumodes.\n",
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" beam_splitter(qumodes[0], qumodes[1], np.pi / 6)\n",
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"\n",
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" # measure some of all qumodes if need to be measured\n",
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" # mz(qumodes)\n",
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"\n",
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"\n",
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"# Compute the statevector of the kernel\n",
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"result = cudaq.get_state(kernel, qumode_count)\n",
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"\n",
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"print(np.array(result))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The statevector generated by the `get_state` command follows little-endian convention for associating numbers with their digit string representations, which places the least significant digit on the right. That is, for the example of a 2-qumode system of level 3 (in which possible states are 0, 1, and 2), we have the following translation between integers and digit string:\n",
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"$$\\begin{matrix} \n",
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"\\text{Integer} & \\text{digit string representation}\\\\\n",
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"& \\text{least significant bit on right}\\\\\n",
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"0 = \\textcolor{blue}{0}*3^1 + \\textcolor{red}{0}*3^0 & \\textcolor{blue}{0}\\textcolor{red}{0} \\\\\n",
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"1 = \\textcolor{blue}{0}*3^1 + \\textcolor{red}{1}*3^0 & \\textcolor{blue}{0}\\textcolor{red}{1}\\\\\n",
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"2 = \\textcolor{blue}{0}*3^1 + \\textcolor{red}{2}*3^0 & \\textcolor{blue}{0}\\textcolor{red}{2}\\\\\n",
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"3 = \\textcolor{blue}{1}*3^1 + \\textcolor{red}{0}*3^0 & \\textcolor{blue}{1}\\textcolor{red}{0} \\\\\n",
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"4 = \\textcolor{blue}{1}*3^1 + \\textcolor{red}{1}*3^0 & \\textcolor{blue}{1}\\textcolor{red}{1} \\\\\n",
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"5 = \\textcolor{blue}{1}*3^1 + \\textcolor{red}{2}*3^0 & \\textcolor{blue}{1}\\textcolor{red}{2} \\\\\n",
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"6 = \\textcolor{blue}{2}*3^1 + \\textcolor{red}{0}*3^0 & \\textcolor{blue}{2}\\textcolor{red}{0} \\\\\n",
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"7 = \\textcolor{blue}{2}*3^1 + \\textcolor{red}{1}*3^0 & \\textcolor{blue}{2}\\textcolor{red}{1} \\\\\n",
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"8 = \\textcolor{blue}{2}*3^1 + \\textcolor{red}{2}*3^0 & \\textcolor{blue}{2}\\textcolor{red}{2} \n",
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"\\end{matrix}\n",
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"$$\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## Parallelization Techniques\n",
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"\n",
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"The most intensive task in the computation is the execution of the quantum photonic kernel hence each execution function: `sample`, and `get_state` can be parallelized given access to multiple quantum processing units (multi-QPU). We emulate each QPU with a CPU."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(cudaq.__version__)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎pr-2542/_sources/using/backends/simulators.rst.txt‎

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can be slower than executing Stim a single time and generating all the shots
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from that single execution.
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Photonics Simulators
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==================================
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The :code:`orca-photonics` target provides a state vector simulator with
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the :code:`Q++` library.
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The :code:`orca-photonics` target supports supports a double precision simulator that can run in multiple CPUs.
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OpenMP CPU-only
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++++++++++++++++++++++++++++++++++
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.. _qpp-cpu-photonics-backend:
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This target provides a state vector simulator based on the CPU-only, OpenMP threaded `Q++ <https://github.com/softwareqinc/qpp>`_ library.
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To execute a program on the :code:`orca-photonics` target, use the following commands:
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.. tab:: Python
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.. code:: bash
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python3 program.py [...] --target orca-photonics
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The target can also be defined in the application code by calling
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.. code:: python
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cudaq.set_target('orca-photonics')
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If a target is set in the application code, this target will override the :code:`--target` command line flag given during program invocation.
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.. tab:: C++
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.. code:: bash
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nvq++ --library-mode --target orca-photonics program.cpp [...] -o program.x
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Fermioniq
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==================================
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‎pr-2542/_sources/using/examples/examples.rst.txt‎

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Introduction <introduction.rst>
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Building Kernels <building_kernels.rst>
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Quantum Operations <quantum_operations.rst>
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Photonic Operations <photonic_operations.rst>
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Measuring Kernels <../../examples/python/measuring_kernels.ipynb>
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Visualizing Kernels <../../examples/python/visualization.ipynb>
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Executing Kernels <../../examples/python/executing_kernels.ipynb>
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Executing Photonic Kernels <../../examples/python/executing_photonic_kernels.ipynb>
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Computing Expectation Values <expectation_values.rst>
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Multi-Control Synthesis <multi_control.rst>
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Multi-GPU Workflows <multi_gpu_workflows.rst>

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