{ "cells": [ { "cell_type": "markdown", "id": "5677b81c", "metadata": {}, "source": [ "# SOQCS Example 7: Boson sampling example." ] }, { "cell_type": "markdown", "id": "1060dc45", "metadata": {}, "source": [ "<p style='text-align: justify;'>In this example a random circuit of four channels is generated and two photons are created, one in each of the first two channels. An exact calculation of the output is then carried out using one of the SOQCS cores and also an approximated calculation by sampling using Clifford A algorithm [1]. Both results are printed for comparison.</p>\n", "<br>\n", "<br>\n", "[1] Peter Clifford and Raphael Clifford. <it>The Classical Complexity of Boson Sampling</it>, <b>Proceedings of the 2018 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)</b> pages 146:155. " ] }, { "cell_type": "code", "execution_count": 1, "id": "5ec5a71c", "metadata": {}, "outputs": [], "source": [ "import soqcs # Import SOQCS\n", "import pandas # Import PANDAS for plotting\n", "from collections import Counter # Import collections" ] }, { "cell_type": "markdown", "id": "f2ba3e87", "metadata": {}, "source": [ "## Build a random circuit" ] }, { "cell_type": "markdown", "id": "d73a1752", "metadata": {}, "source": [ "A circuit of four channels is created. With the gate random_circuit() a random matrix is assigned to the circuit." ] }, { "cell_type": "code", "execution_count": 2, "id": "d1a2a4eb", "metadata": {}, "outputs": [], "source": [ "example = soqcs.qodev(2,4)\n", "example.add_photons(1,0)\n", "example.add_photons(1,1)\n", "example.random_circuit()\n", "example.detector(0)\n", "example.detector(1)\n", "example.detector(2)\n", "example.detector(3)" ] }, { "cell_type": "markdown", "id": "05d28f89", "metadata": {}, "source": [ "## Obtain a list of samples" ] }, { "cell_type": "markdown", "id": "ff866ab3", "metadata": {}, "source": [ "Create a simulator:" ] }, { "cell_type": "code", "execution_count": 3, "id": "4c685007", "metadata": {}, "outputs": [], "source": [ "sim = soqcs.simulator() " ] }, { "cell_type": "markdown", "id": "16e9048d", "metadata": {}, "source": [ "A few samples are obtained." ] }, { "cell_type": "code", "execution_count": 4, "id": "a8f521f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['1100', '0101', '1100', '0101', '1100']\n" ] } ], "source": [ "sample=sim.get_sample(example,N=5,method=0)\n", "print(sample)" ] }, { "cell_type": "markdown", "id": "d0b49f13", "metadata": {}, "source": [ "We can run the simulator for a few more and use the resulting list to build an histogram using pandas (or any other tool)" ] }, { "cell_type": "code", "execution_count": 5, "id": "7b9979e6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate list of samples\n", "sample=sim.get_sample(example,10000,0)\n", "# Create and lot the histogram.\n", "count = Counter(sample)\n", "count=dict(sorted(count.items()))\n", "df = pandas.DataFrame.from_dict(count, orient='index')\n", "df.plot(kind='bar')" ] }, { "cell_type": "markdown", "id": "02c26d17", "metadata": {}, "source": [ "## Perform a comparison between sampled and exact results" ] }, { "cell_type": "markdown", "id": "2f41acd3", "metadata": {}, "source": [ "Asking to SOQCS to directly generate and plot the probability distribution of the samples is faster. Below we perform,\n", "\n", " - An exact simulation.\n", " - A sampling procedure" ] }, { "cell_type": "code", "execution_count": 6, "id": "1132fded", "metadata": {}, "outputs": [], "source": [ "apdexact=sim.run(example) # Run a simulation\n", "apdsample=sim.sample(example,1000000) # Run a sampling procedure with one million samples." ] }, { "cell_type": "markdown", "id": "f861503c", "metadata": {}, "source": [ "Exact calculation plot:" ] }, { "cell_type": "code", "execution_count": 7, "id": "15070f8e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 560x350 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "apdexact.show(dpi=70)" ] }, { "cell_type": "markdown", "id": "1bed18b1", "metadata": {}, "source": [ "Sampling result plot:" ] }, { "cell_type": "code", "execution_count": 8, "id": "af674b0e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 560x350 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "apdsample.show(dpi=70)" ] }, { "cell_type": "markdown", "id": "1ee62073", "metadata": {}, "source": [ "**THIS CODE IS PART OF SOQCS** <br>\n", "\n", "**Copyright:** <br>\n", "Copyright © 2023 National University of Ireland Maynooth, Maynooth University. All rights reserved.\n", "The contents and use of this document and the related code are subject to the licence terms detailed in <a href=\"./assets/LICENCE.TXT\">LICENCE.txt</a>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }