{ "cells": [ { "cell_type": "markdown", "id": "37d2f74d-778b-4c53-94fc-2b58e9357fef", "metadata": {}, "source": [ "(DQM)=\n", "# Introducción al modelo Discrete Quadratic Model\n", "```{index} Discrte Quadratic Model, DQM, offset\n", "```\n", "\n", "En módulos anteriores hemos estado desarrollando modelos donde la variables $ x_i $ erán dicotómicas, y en concreto para los problemas tipo QUBO podían tener valores 0 ó 1. En este apartado vamos a ampliar ese modelo a variables que puedan tomar valores discretos. La formulación matemática de estos modelos, se representa mediante la siguiente ecuación genérica:\n", "\n", "$$ H(d)=\\sum_{i}a_{i}(d_{i})+\\sum_{i,j}b_{i,j}(d_{i},d_{j})+c $$\n", "\n", "Donde $d_i$ son las variables discretas, tanto $a_i()$ como $b_{i,j}()$ son funciones de valores reales y c es una constante, también denominada *offset*.\n", "\n", "Entonces este tipo de problemas los podemos trasladar a problemas de tipo QUBO transformado las variables $d_i$ a otras variables de tipo binario que sólo toman valores 0 ó 1 mediante el método denominado one-hot encoding . Este método consiste en los siguiente: supongamos que tenemos un modelo DQM con N variables discretas, $d_i$, y cada una de esta variables discretas puede tener $n_i$ valores diferentes, entonces definimos la variable binaria (con valores 0 ó 1) $x_{i,u}$ con valor 1 cuando para uno de los valores de $d_i$ y cero en el resto de los casos, y además y por construcción se debe cumplir que \n", "\n", "$$ \\sum_{u=0}^{n_{i}-1}x_{i,u}=1\\:\\forall i=1,2,...N $$\n", "\n", "En estos casos la función objetivo se podría expresar de la siguiente manera:\n", "\n", "$$ E(X)=\\sum_{i=1}^{n}\\sum_{u=1}^{n_{i}}a_{i,u}x_{i,u}+\\sum_{i=1}^{N}\\sum_{j=i+1}^{N}\\sum_{u=1}^{n_{i}}\\sum_{j=1}^{n_{j}}b_{i,j,u,v}x_{i,u}x_{j,v}+c $$\n", "\n", "Esta última expresión es la que se emplea en las herramientas de Ocean para resolver este tipo de problemas. Para entender mejor este concepto, vamos a ver un ejemplo concreto. Supongamos que tenemos tres contenedores y 10 objetos que queremos introducir en esos contenedores. En este caso la variable $d_i$ valdría 1,2,3 indicando al contenedor que elegimos para el objeto i. Ahora definimos la variable dicotómica que puede tomar valores 0 ó 1 siguiente:\n", "\n", "$$x_{i,j}=\\begin{cases}\n", "1 & Si\\ objeto\\ j\\ est\\acute{a}\\ en\\ contendor\\ i\\\\\n", "0 & en\\ caso\\ contrario\n", "\\end{cases} $$\n", "\n", "Entonces en este caso, como cada objeto j debe estar en un solo contendor, se debe cumplir que $\\sum_{i=1}^{3}x_{ij}=1\\ \\forall j=1,2,..,10$,$" ] }, { "cell_type": "markdown", "id": "3c0342dd-a01d-4cda-beb6-231076603058", "metadata": {}, "source": [ "De esta manera si por ejemplo el producto 5 se decide que está en el contenedor 1, se tiene que $x_{1,5}=1$ y $x_{i,5}=0$ para i=2,3\n", "\n", "Para un correcto uso de estas herramientas, se aconseja al lector mirar los siguientes enlaces:\n", "\n", "* Documentación de la clase dimod.DiscreteQuadraticModel \n", "\n", "* Documentación de la clase LeapHybridDQMSampler ." ] }, { "cell_type": "markdown", "id": "7d75e9b2-12a8-4e88-8c08-da7ff75d7875", "metadata": {}, "source": [ "Otra manera de enfocar este problema sería la siguiente:\n", "\n", "Supongamos que tenemos N variables discretas (o N grupos de variables binarias). Cada variable $x_i$ tiene $C_i$ casos. Entonces la siguiente ecuación es la forma más general para expresar la energía para un modelo DQM (salvo una constante).\n", "\n", "$$\\Large H = \\sum_{i,k} a_{i,k} x_{i,k} + \\sum_{i,k,j,l} w_{i,k,j,l} x_{i,k} x_{j,l}$$\n", "\n", "\n", "$$\\Large i, j \\in \\left\\{0, 1, 2, ..., N - 1\\right\\}$$ \n", "\n", "$$\\Large k \\in \\left\\{0, 1, 2, ..., C_i - 1 \\right\\}$$\n", "$$\\Large l \\in \\left\\{0, 1, 2, ..., C_j - 1 \\right\\}$$\n", "\n", "El Hamiltoniano anterior está sujeto a la siguiente restricción:\n", "\n", "$$\\Large \\sum_{k=0}^{C_i - 1} x_{i,k} = 1 ~~~~~ \\forall i$$\n", "\n", "Por la propia definición del problema, el coeficiente $\\large w_{i,k,i,l}$ cuando $k\\neq l$ no tiene efecto sobre la energía puesto que:\n", "\n", "$$\\Large x_{i,k}x_{i, l} = 0 ~~~~~ k\\neq l $$\n", "\n", "\n", "El número de variable binarias es:\n", "\n", "\n", "$$ N_b = \\sum_i C_i $$\n", "\n", "Como es habitual en python, podemos obtener ayuda sobre esta clase de la siguiente manera:" ] }, { "cell_type": "code", "execution_count": 1, "id": "b019a160-050c-4fe2-ae76-bb24b61bad2c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[1;31mInit signature:\u001b[0m \u001b[0mDQM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mDocstring:\u001b[0m \n", "Encodes a discrete quadratic model.\n", "\n", "A discrete quadratic model is a polynomial over discrete variables with\n", "terms all of degree two or less.\n", "\n", "Examples:\n", "\n", " This example constructs a map coloring with Canadian provinces. To\n", " solve the problem we penalize adjacent provinces having the same color.\n", "\n", " >>> provinces = [\"AB\", \"BC\", \"ON\", \"MB\", \"NB\", \"NL\", \"NS\", \"NT\", \"NU\",\n", " ... \"PE\", \"QC\", \"SK\", \"YT\"]\n", " >>> borders = [(\"BC\", \"AB\"), (\"BC\", \"NT\"), (\"BC\", \"YT\"), (\"AB\", \"SK\"),\n", " ... (\"AB\", \"NT\"), (\"SK\", \"MB\"), (\"SK\", \"NT\"), (\"MB\", \"ON\"),\n", " ... (\"MB\", \"NU\"), (\"ON\", \"QC\"), (\"QC\", \"NB\"), (\"QC\", \"NL\"),\n", " ... (\"NB\", \"NS\"), (\"YT\", \"NT\"), (\"NT\", \"NU\")]\n", " >>> colors = [0, 1, 2, 3]\n", " ...\n", " >>> dqm = dimod.DiscreteQuadraticModel()\n", " >>> for p in provinces:\n", " ... _ = dqm.add_variable(4, label=p)\n", " >>> for p0, p1 in borders:\n", " ... dqm.set_quadratic(p0, p1, {(c, c): 1 for c in colors})\n", "\n", " The next examples show how to view and manipulate the model biases.\n", "\n", " >>> dqm = dimod.DiscreteQuadraticModel()\n", "\n", " Add the variables to the model\n", "\n", " >>> u = dqm.add_variable(5) # unlabeled variable with 5 cases\n", " >>> v = dqm.add_variable(3, label='v') # labeled variable with 3 cases\n", "\n", " The linear biases default to 0. They can be read by case or by batch.\n", "\n", " >>> dqm.get_linear_case(u, 1)\n", " 0.0\n", " >>> dqm.get_linear(u)\n", " array([0., 0., 0., 0., 0.])\n", " >>> dqm.get_linear(v)\n", " array([0., 0., 0.])\n", "\n", " The linear biases can be overwritten either by case or in a batch.\n", "\n", " >>> dqm.set_linear_case(u, 3, 17)\n", " >>> dqm.get_linear(u)\n", " array([ 0., 0., 0., 17., 0.])\n", " >>> dqm.set_linear(v, [0, -1, 3])\n", " >>> dqm.get_linear(v)\n", " array([ 0., -1., 3.])\n", "\n", " The quadratic biases can also be manipulated sparsely or densely.\n", "\n", " >>> dqm.set_quadratic(u, v, {(0, 2): 1.5})\n", " >>> dqm.get_quadratic(u, v)\n", " {(0, 2): 1.5}\n", " >>> dqm.get_quadratic(u, v, array=True) # as a NumPy array\n", " array([[0. , 0. , 1.5],\n", " [0. , 0. , 0. ],\n", " [0. , 0. , 0. ],\n", " [0. , 0. , 0. ],\n", " [0. , 0. , 0. ]])\n", " >>> dqm.set_quadratic_case(u, 2, v, 1, -3)\n", " >>> dqm.get_quadratic(u, v, array=True)\n", " array([[ 0. , 0. , 1.5],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , -3. , 0. ],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. ]])\n", " >>> dqm.get_quadratic(u, v) # doctest:+SKIP\n", " {(0, 2): 1.5, (2, 1): -3.0}\n", "\u001b[1;31mFile:\u001b[0m c:\\users\\francisco\\desktop\\dwaveocean\\ocean\\lib\\site-packages\\dimod\\discrete\\discrete_quadratic_model.py\n", "\u001b[1;31mType:\u001b[0m type\n", "\u001b[1;31mSubclasses:\u001b[0m CaseLabelDQM" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dimod import DQM\n", "\n", "DQM?" ] }, { "cell_type": "code", "execution_count": null, "id": "7d57aabb-2a69-4a76-b186-8e3300f0dde9", "metadata": {}, "outputs": [], "source": [ "```{index} ExactDQMSolver, LeapHybridDQMSampler\n", "```\n", "Para resolver este problema podemos trabajar con los dos solver que se muestran a continuación" ] }, { "cell_type": "code", "execution_count": 2, "id": "211db135-cdf2-4188-aeb7-5960be9c1f18", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[1;31mInit signature:\u001b[0m \u001b[0mExactDQMSolver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mDocstring:\u001b[0m \n", "A simple exact solver for testing and debugging code using your local CPU.\n", "\n", "Notes:\n", " This solver calculates the energy for every possible\n", " combination of variable cases. If variable :math:`i` has\n", " :math:`k_i` cases, this results in :math:`k_1 * k_2 * ... * k_n` cases,\n", " which grows exponentially for constant :math:`k_i` in the\n", " number of variables.\n", "\u001b[1;31mFile:\u001b[0m c:\\users\\francisco\\desktop\\dwaveocean\\ocean\\lib\\site-packages\\dimod\\reference\\samplers\\exact_solver.py\n", "\u001b[1;31mType:\u001b[0m type\n", "\u001b[1;31mSubclasses:\u001b[0m " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dimod import ExactDQMSolver\n", "\n", "ExactDQMSolver?" ] }, { "cell_type": "code", "execution_count": 3, "id": "e053a323-fa80-4b28-a494-9fd1f3b6309f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[1;31mInit signature:\u001b[0m \u001b[0mLeapHybridDQMSampler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mDocstring:\u001b[0m \n", "A class for using Leap's cloud-based hybrid DQM solvers.\n", "\n", "Leap’s quantum-classical hybrid DQM solvers are intended to solve arbitrary\n", "application problems formulated as **discrete** quadratic models (DQM).\n", "\n", "You can configure your :term:`solver` selection and usage by setting parameters,\n", "hierarchically, in a configuration file, as environment variables, or\n", "explicitly as input arguments, as described in\n", "`D-Wave Cloud Client `_.\n", "\n", ":ref:`dwave-cloud-client `'s\n", ":meth:`~dwave.cloud.client.Client.get_solvers` method filters solvers you have\n", "access to by `solver properties `_\n", "``category=hybrid`` and ``supported_problem_type=dqm``. By default, online\n", "hybrid DQM solvers are returned ordered by latest ``version``.\n", "\n", "The default specification for filtering and ordering solvers by features is\n", "available as :attr:`.default_solver` property. Explicitly specifying a\n", "solver in a configuration file, an environment variable, or keyword\n", "arguments overrides this specification. See the example in :class:`.LeapHybridSampler`\n", "on how to extend it instead.\n", "\n", "Args:\n", " **config:\n", " Keyword arguments passed to :meth:`dwave.cloud.client.Client.from_config`.\n", "\n", "Examples:\n", " This example solves a small, illustrative problem: a game of\n", " rock-paper-scissors. The DQM has two variables representing two hands,\n", " with cases for rock, paper, scissors. Quadratic biases are set to\n", " produce a lower value of the DQM for cases of variable ``my_hand``\n", " interacting with cases of variable ``their_hand`` such that the former\n", " wins over the latter; for example, the interaction of ``rock-scissors`` is\n", " set to -1 while ``scissors-rock`` is set to +1.\n", "\n", " >>> import dimod\n", " >>> from dwave.system import LeapHybridDQMSampler\n", " ...\n", " >>> cases = [\"rock\", \"paper\", \"scissors\"]\n", " >>> win = {\"rock\": \"scissors\", \"paper\": \"rock\", \"scissors\": \"paper\"}\n", " ...\n", " >>> dqm = dimod.DiscreteQuadraticModel()\n", " >>> dqm.add_variable(3, label='my_hand')\n", " 'my_hand'\n", " >>> dqm.add_variable(3, label='their_hand')\n", " 'their_hand'\n", " >>> for my_idx, my_case in enumerate(cases):\n", " ... for their_idx, their_case in enumerate(cases):\n", " ... if win[my_case] == their_case:\n", " ... dqm.set_quadratic('my_hand', 'their_hand',\n", " ... {(my_idx, their_idx): -1})\n", " ... if win[their_case] == my_case:\n", " ... dqm.set_quadratic('my_hand', 'their_hand',\n", " ... {(my_idx, their_idx): 1})\n", " ...\n", " >>> dqm_sampler = LeapHybridDQMSampler() # doctest: +SKIP\n", " ...\n", " >>> sampleset = dqm_sampler.sample_dqm(dqm) # doctest: +SKIP\n", " >>> print(\"{} beats {}\".format(cases[sampleset.first.sample['my_hand']],\n", " ... cases[sampleset.first.sample['their_hand']])) # doctest: +SKIP\n", " rock beats scissors\n", "\u001b[1;31mFile:\u001b[0m c:\\users\\francisco\\desktop\\dwaveocean\\ocean\\lib\\site-packages\\dwave\\system\\samplers\\leap_hybrid_sampler.py\n", "\u001b[1;31mType:\u001b[0m type\n", "\u001b[1;31mSubclasses:\u001b[0m " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dwave.system import LeapHybridDQMSampler\n", "\n", "LeapHybridDQMSampler?" ] }, { "cell_type": "code", "execution_count": null, "id": "11f5c87b-c9fd-402d-b20f-d66acab48e56", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "81be2e0f-1bd8-40ff-9c36-2e38b5b5bb7d", "metadata": {}, "source": [ "A continuación se procede a mostrar una serie de ejemplos que ayudan a comprender cómo utilizar esta clase." ] }, { "cell_type": "markdown", "id": "8fdfb84e-d860-41b3-84e7-07f27fe3d5aa", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b9d299f4-4e68-41ca-af84-6e91555148ef", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ad0b53fa-e06e-4ccb-af9d-5ab4f5b97dcf", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "33cb3cae-0e72-405b-9986-9493a359b386", "metadata": {}, "source": [ "(MapColoring)=\n", "## Ejemplo 1. Map Coloring.\n", "```{index} Problema Map Coloring\r\n", "```\n", "\n", "Este es uno de los ejemplos típicos de utilización de Ocean. Se trata de los siguiente: Dado un mapa (en este caso el de Estados Unidos), y cuatro colores, se pretende utilizar estos cuatro colores para colorear todos los Estados, pero de tal manera que dos Estados adyacentes no pueden tener el mismo color.\n", "\n", "Para poder resolver este problema, una de las cosas que debemos tener es información de para cada Estado, los que son adyacentes al mismo. Esta información la obtenemos de este enlace . Cogiendo los datos que alli se pueden ver, se ha construido el fichero denominado *usa.adj* el cual nos va a servir para contruir la red base para reolver el problema.\n", "\n", "A continuación se muestra un ejemplo del contenido de este fichero:\n", "\n", "```{admonition} Ejemplo del contenido del fichero usa.adj\r", "# Author Gregg Lind\r\n", "# License: Public Domain. I would love to hear about any projects you use if it for though!\r\n", " \r\n", "AK,HI\r\n", "AL,MS,TN,GA,FL\r\n", "AR,MO,TN,MS,LA,TX,OK\r\n", "AZ,CA,NV,UT,CO,NM\r\n", "CA,OR,NV,AZ\r\n", "CO,WY,NE,KS,OK,NM,AZ,UT\r\n", "CT,NY,MA,RI\r\n", "DC,MD,VA\r\n", "DE,MD,PA,NJ\r\n", "FL,AL,GA\r\n", "GA,FL,AL,TN,NC\n", ",SC\r\n", "HI,AKt\r\n", "```" ] }, { "cell_type": "markdown", "id": "64e3445a-cfe8-4efc-bb5c-c0db42c87819", "metadata": {}, "source": [ "Así pues con la información anterior, procedemos a leer el fichero y construir los nodos y los arcos de la red con la que vamos a trabajar para resolver el problema planteado." ] }, { "cell_type": "code", "execution_count": 2, "id": "b2bf1985-e2e9-4d92-a114-2a11bb938f41", "metadata": {}, "outputs": [], "source": [ "import networkx as nx\n", "# Leemos el ficheros\n", "G = nx.read_adjlist('datos/usa.adj', delimiter = ',') " ] }, { "cell_type": "code", "execution_count": 3, "id": "5012639a-8f86-4911-b67c-9d1918ba4e09", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NodeView(('', 'AK', 'HI', 'AL', 'MS', 'TN', 'GA', 'FL', 'AR', 'MO', 'LA', 'TX', 'OK', 'AZ', 'CA', 'NV', 'UT', 'CO', 'NM', 'OR', 'WY', 'NE', 'KS', 'CT', 'NY', 'MA', 'RI', 'DC', 'MD', 'VA', 'DE', 'PA', 'NJ', 'NC', 'SC', 'IA', 'MN', 'WI', 'IL', 'SD', 'ID', 'MT', 'WA', 'IN', 'KY', 'MI', 'OH', 'WV', 'NH', 'VT', 'ME', 'ND'))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Veamos los nodos de la red\n", "G.nodes()" ] }, { "cell_type": "code", "execution_count": 4, "id": "30559b0b-3ff7-43e1-8042-5ed6093b073c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "EdgeView([('AK', 'HI'), ('AL', 'MS'), ('AL', 'TN'), ('AL', 'GA'), ('AL', 'FL'), ('MS', 'AR'), ('MS', 'LA'), ('MS', 'TN'), ('TN', 'AR'), ('TN', 'GA'), ('TN', 'KY'), ('TN', 'MO'), ('TN', 'NC'), ('TN', 'VA'), ('GA', 'FL'), ('GA', 'NC'), ('GA', 'SC'), ('AR', 'MO'), ('AR', 'LA'), ('AR', 'TX'), ('AR', 'OK'), ('MO', 'IA'), ('MO', 'IL'), ('MO', 'KS'), ('MO', 'KY'), ('MO', 'OK'), ('MO', 'NE'), ('LA', 'TX'), ('TX', 'NM'), ('TX', 'OK'), ('OK', 'CO'), ('OK', 'KS'), ('OK', 'NM'), ('AZ', 'CA'), ('AZ', 'NV'), ('AZ', 'UT'), ('AZ', 'CO'), ('AZ', 'NM'), ('CA', 'OR'), ('CA', 'NV'), ('NV', 'ID'), ('NV', 'UT'), ('NV', 'OR'), ('UT', 'CO'), ('UT', 'ID'), ('UT', 'NM'), ('UT', 'WY'), ('CO', 'WY'), ('CO', 'NE'), ('CO', 'KS'), ('CO', 'NM'), ('OR', 'ID'), ('OR', 'WA'), ('WY', 'ID'), ('WY', 'MT'), ('WY', 'NE'), ('WY', 'SD'), ('NE', 'IA'), ('NE', 'KS'), ('NE', 'SD'), ('CT', 'NY'), ('CT', 'MA'), ('CT', 'RI'), ('NY', 'MA'), ('NY', 'NJ'), ('NY', 'PA'), ('NY', 'VT'), ('MA', 'RI'), ('MA', 'NH'), ('MA', 'VT'), ('DC', 'MD'), ('DC', 'VA'), ('MD', 'DE'), ('MD', 'VA'), ('MD', 'WV'), ('MD', 'PA'), ('VA', 'KY'), ('VA', 'NC'), ('VA', 'WV'), ('DE', 'PA'), ('DE', 'NJ'), ('PA', 'NJ'), ('PA', 'OH'), ('PA', 'WV'), ('NC', 'SC'), ('IA', 'MN'), ('IA', 'WI'), ('IA', 'IL'), ('IA', 'SD'), ('MN', 'WI'), ('MN', 'SD'), ('MN', 'ND'), ('WI', 'IL'), ('WI', 'MI'), ('IL', 'IN'), ('IL', 'KY'), ('SD', 'MT'), ('SD', 'ND'), ('ID', 'MT'), ('ID', 'WA'), ('MT', 'ND'), ('IN', 'MI'), ('IN', 'OH'), ('IN', 'KY'), ('KY', 'OH'), ('KY', 'WV'), ('MI', 'OH'), ('OH', 'WV'), ('NH', 'ME'), ('NH', 'VT')])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sacamos los arcos de la red\n", "G.edges()" ] }, { "cell_type": "markdown", "id": "82f2d51a-0fc2-413a-8086-706dc74ed9ba", "metadata": {}, "source": [ "Para tener un trabajo más cómodo y más descriptivo, procedemos a denominar a los nodos por *states* y a los arcos por *borders*." ] }, { "cell_type": "code", "execution_count": 5, "id": "65941dca-c533-44cb-8e1f-d095ad3cb563", "metadata": {}, "outputs": [], "source": [ "\n", "states = G.nodes \n", "borders = G.edges " ] }, { "cell_type": "markdown", "id": "90b3f13a-26f3-4eb4-8c36-5e6197ae035e", "metadata": {}, "source": [ "Ahora ya tenemos todos los ingredientes para construir el modelo que resuelva el problema planteado. Los pasos son los que damos en el código que sigue y para una mejor comprensión del mismo a continuación detallamos los pasos que se dan:\n", "\n", "1.- Se definen los cuatro colores con los que se va a colorear el mapa. Estos colores son genéricos y los identificamos con los números: 0,1,2,3.\n", "\n", "2.- Se procede a crear una instancia de * DiscreteQuadraticModel()*\n", "\n", "3. Dado que cualquier Estado del mapa se puede colorear con uno de los cuatro colores, se representa cada estado con una variable discreta que puede tener cuatro casos (las variables binarias pueden tener dos valores; las variables discretas pueden tener algún número arbitrario de casos).\n", "\n", "4. Para cada par de Estados que comparten frontera, establezca un sesgo cuadrático (denominado bias) de 1 \n", " entre los casos idénticos de las variables de 0 \r\n", " entre todos los casos diferentes (por defecto, el sesgo cuadrático es cero). Este modelo de penalización añade un valor d1 \r\n", " a las soluciones del DQM para cada par de estados vecinos con el mismo co (es decir estamos penalizando los estados que tienen alguna fronmtera en común)lor. Las soluciones óptimas son las que tienen el menor número de estados vecinoslator" ] }, { "cell_type": "code", "execution_count": 8, "id": "4d961317-e8d6-4568-85b2-6e303b08a454", "metadata": {}, "outputs": [], "source": [ "import dimod\n", "# Incorporamos los colores que se van a utilizar : 1\n", "colors = [0, 1, 2, 3]\n", "# Obtenemos el objeto DiscreteQuadraticModel(): 2\n", "dqm = dimod.DiscreteQuadraticModel()\n", "# Cada estado se representa con una variable discreta que puede tomar cuatro valores y como etiqueta tiene el nombre del estado correspondiente : 3\n", "for state in states: \n", " dqm.add_variable(4, label=state)\n", "\n", "# Damos una penalización de 1 para cada par de Estados que tienen alguna frontera en común (estos Estados se localizan \n", "# por estar en la variables borders ): 4\n", "for state0, state1 in borders: \n", " dqm.set_quadratic(state0, state1, {(color, color): 1 for color in colors})" ] }, { "cell_type": "markdown", "id": "a5d8afaf-8bd1-4c64-a1cd-f12b2fbf071d", "metadata": {}, "source": [ "Obtengamos a continuación alguna información del objeto creado. Veremos sólo algunos casos, para ampliar esta información, el lector puede consultar los métodos y propiedades de esta clase en la documetación oficial y obtener más información.\n", "\n", "Comenzamos viendo la estructura de adyacencia de las variables" ] }, { "cell_type": "code", "execution_count": 9, "id": "a67d57e4-c402-493d-9d13-7e0371dcb4ff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'': {}, 'AK': {'H': 'I'}, 'HI': {'A': 'K'}, 'AL': {'M': 'S', 'T': 'N', 'G': 'A', 'F': 'L'}, 'MS': {'A': 'R', 'T': 'N', 'L': 'A'}, 'TN': {'A': 'R', 'M': 'O', 'G': 'A', 'V': 'A', 'N': 'C', 'K': 'Y'}, 'GA': {'A': 'L', 'T': 'N', 'F': 'L', 'N': 'C', 'S': 'C'}, 'FL': {'A': 'L', 'G': 'A'}, 'AR': {'M': 'O', 'T': 'X', 'L': 'A', 'O': 'K'}, 'MO': {'T': 'N', 'A': 'R', 'O': 'K', 'N': 'E', 'K': 'Y', 'I': 'L'}, 'LA': {'M': 'S', 'A': 'R', 'T': 'X'}, 'TX': {'A': 'R', 'L': 'A', 'O': 'K', 'N': 'M'}, 'OK': {'A': 'R', 'M': 'O', 'T': 'X', 'C': 'O', 'N': 'M', 'K': 'S'}, 'AZ': {'C': 'O', 'N': 'M', 'U': 'T'}, 'CA': {'A': 'Z', 'N': 'V', 'O': 'R'}, 'NV': {'A': 'Z', 'C': 'A', 'U': 'T', 'O': 'R', 'I': 'D'}, 'UT': {'A': 'Z', 'N': 'M', 'C': 'O', 'W': 'Y', 'I': 'D'}, 'CO': {'O': 'K', 'A': 'Z', 'U': 'T', 'N': 'E', 'W': 'Y', 'K': 'S'}, 'NM': {'T': 'X', 'O': 'K', 'A': 'Z', 'U': 'T', 'C': 'O'}, 'OR': {'C': 'A', 'N': 'V', 'I': 'D', 'W': 'A'}, 'WY': {'U': 'T', 'C': 'O', 'N': 'E', 'S': 'D', 'I': 'D', 'M': 'T'}, 'NE': {'M': 'O', 'C': 'O', 'W': 'Y', 'K': 'S', 'I': 'A', 'S': 'D'}, 'KS': {'M': 'O', 'O': 'K', 'C': 'O', 'N': 'E'}, 'CT': {'N': 'Y', 'M': 'A', 'R': 'I'}, 'NY': {'C': 'T', 'M': 'A', 'P': 'A', 'N': 'J', 'V': 'T'}, 'MA': {'C': 'T', 'N': 'H', 'R': 'I', 'V': 'T'}, 'RI': {'C': 'T', 'M': 'A'}, 'DC': {'M': 'D', 'V': 'A'}, 'MD': {'D': 'E', 'V': 'A', 'P': 'A', 'W': 'V'}, 'VA': {'T': 'N', 'D': 'C', 'M': 'D', 'N': 'C', 'K': 'Y', 'W': 'V'}, 'DE': {'M': 'D', 'P': 'A', 'N': 'J'}, 'PA': {'N': 'J', 'M': 'D', 'D': 'E', 'O': 'H', 'W': 'V'}, 'NJ': {'N': 'Y', 'D': 'E', 'P': 'A'}, 'NC': {'T': 'N', 'G': 'A', 'V': 'A', 'S': 'C'}, 'SC': {'G': 'A', 'N': 'C'}, 'IA': {'M': 'N', 'N': 'E', 'W': 'I', 'I': 'L', 'S': 'D'}, 'MN': {'I': 'A', 'W': 'I', 'S': 'D', 'N': 'D'}, 'WI': {'I': 'L', 'M': 'I'}, 'IL': {'M': 'O', 'I': 'N', 'W': 'I', 'K': 'Y'}, 'SD': {'W': 'Y', 'N': 'D', 'I': 'A', 'M': 'T'}, 'ID': {'N': 'V', 'U': 'T', 'O': 'R', 'W': 'A', 'M': 'T'}, 'MT': {'W': 'Y', 'S': 'D', 'I': 'D', 'N': 'D'}, 'WA': {'O': 'R', 'I': 'D'}, 'IN': {'I': 'L', 'K': 'Y', 'M': 'I', 'O': 'H'}, 'KY': {'T': 'N', 'M': 'O', 'V': 'A', 'I': 'N', 'O': 'H', 'W': 'V'}, 'MI': {'W': 'I', 'I': 'N', 'O': 'H'}, 'OH': {'P': 'A', 'I': 'N', 'K': 'Y', 'M': 'I', 'W': 'V'}, 'WV': {'M': 'D', 'V': 'A', 'P': 'A', 'K': 'Y', 'O': 'H'}, 'NH': {'M': 'E', 'V': 'T'}, 'VT': {'N': 'H', 'M': 'A'}, 'ME': {'N': 'H'}, 'ND': {'M': 'T', 'S': 'D'}}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dqm.adj" ] }, { "cell_type": "markdown", "id": "ad845f7e-f483-4eb0-abde-179ab534c03d", "metadata": {}, "source": [ "También podemos consultar el nombre de las variables discretas que se han creado." ] }, { "cell_type": "code", "execution_count": 10, "id": "fda127ca-796f-484a-8027-d5b6494cc71d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Variables(['', 'AK', 'HI', 'AL', 'MS', 'TN', 'GA', 'FL', 'AR', 'MO', 'LA', 'TX', 'OK', 'AZ', 'CA', 'NV', 'UT', 'CO', 'NM', 'OR', 'WY', 'NE', 'KS', 'CT', 'NY', 'MA', 'RI', 'DC', 'MD', 'VA', 'DE', 'PA', 'NJ', 'NC', 'SC', 'IA', 'MN', 'WI', 'IL', 'SD', 'ID', 'MT', 'WA', 'IN', 'KY', 'MI', 'OH', 'WV', 'NH', 'VT', 'ME', 'ND'])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dqm.variables" ] }, { "cell_type": "markdown", "id": "8ce240fc-87b7-4334-bae9-e1344a076298", "metadata": {}, "source": [ "El servicio de nube cuántica de D-Wave proporciona solucionadores híbridos basados en la nube a los que se pueden enviar BQM y DQM arbitrarios. Estos solucionadores, que implementan algoritmos clásicos de última generación junto con la asignación inteligente de la unidad de procesamiento cuántico (QPU) a las partes del problema donde más se beneficia, están diseñados para dar cabida incluso a problemas muy grandes. Los solucionadores de Leap pueden liberarle de la carga de cualquier desarrollo actual y futuro y de la optimización de los algoritmos híbridos que mejor resuelvan su problema.\r\n", "Utilizaremos en este caso la clase LeapHybridDQMSampler para obtener las soluciones que estamos buscandoor" ] }, { "cell_type": "code", "execution_count": 11, "id": "bfa7894d-4d3a-4811-91a8-4e8f820ed222", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Energy: 0.0\n", "Solution: {'': 2, 'AK': 0, 'HI': 1, 'AL': 0, 'MS': 2, 'TN': 1, 'GA': 2, 'FL': 3, 'AR': 0, 'MO': 2, 'LA': 1, 'TX': 3, 'OK': 1, 'AZ': 1, 'CA': 2, 'NV': 0, 'UT': 3, 'CO': 2, 'NM': 0, 'OR': 3, 'WY': 1, 'NE': 3, 'KS': 0, 'CT': 2, 'NY': 1, 'MA': 0, 'RI': 3, 'DC': 3, 'MD': 0, 'VA': 2, 'DE': 1, 'PA': 2, 'NJ': 0, 'NC': 3, 'SC': 1, 'IA': 0, 'MN': 3, 'WI': 2, 'IL': 3, 'SD': 2, 'ID': 2, 'MT': 0, 'WA': 0, 'IN': 2, 'KY': 0, 'MI': 3, 'OH': 1, 'WV': 3, 'NH': 2, 'VT': 3, 'ME': 3, 'ND': 1}\n" ] } ], "source": [ "from dwave.system import LeapHybridDQMSampler\n", "sampleset = LeapHybridDQMSampler().sample_dqm(dqm,\n", " label='SDK Examples - Map Coloring DQM') \n", "print(\"Energy: {}\\nSolution: {}\".format(\n", " sampleset.first.energy, sampleset.first.sample)) " ] }, { "cell_type": "markdown", "id": "020c49a7-6f11-452e-be47-107b5aaf020b", "metadata": {}, "source": [ "**NOTA**. El valor de energía cero anterior significa que esta primera (mejor) solución encontrada no ha acumulado penalizaciones, lo que significa que no hay pares de estados vecinos con el mismo color." ] }, { "cell_type": "markdown", "id": "cf1e86ad-d41d-4458-ac27-82e5f0aa3c4b", "metadata": {}, "source": [ "Procedemos ahora a dibujar la mejor solución encontrada" ] }, { "cell_type": "code", "execution_count": 14, "id": "4b0adad4-6289-4852-89c7-8406dd49fd19", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "node_list = [list(G.nodes)[x:x+10] for x in range(0, 52, 10)] \n", "node_list[4].append('ND') \n", "nx.draw(G, pos=nx.shell_layout(G, nlist = node_list), with_labels=True,\n", " node_color=list(sampleset.first.sample.values()), node_size=400,\n", " cmap=plt.cm.rainbow) \n", "plt.show() " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 5 }