quantum_graph_partitioning.ipynb

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   "source": [
    "# Quantum Graph Partitioning"
   ]
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  {
   "cell_type": "markdown",
   "id": "c0dd6b02-4c8b-4aed-93fa-78463b1a60e2",
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   "source": [
    "Dynex is the world’s only accessible neuromorphic quantum computing cloud for solving real-world problems, at scale.. This example demonstrates how to use the Dynex SDK to use Pyton to compute on the Dynex Platform with Python."
   ]
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   "source": [
    "import dynex\n",
    "import dimod\n",
    "from qubovert.problems import GraphPartitioning"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "The goal of the Graph Partitioning problem is to partition the verticies of a graph into two equal subsets such that the number of edges (or the total weight of the edges) connecting the two subsets is minimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87a5132a-a36e-4d0f-bbc0-c13cc4da44cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = {(\"a\", \"b\"), (\"a\", \"c\"), (\"c\", \"d\"),\n",
    "        (\"b\", \"c\"), (\"e\", \"f\"), (\"d\", \"e\")}\n",
    "problem = GraphPartitioning(edges)\n",
    "Q = problem.to_qubo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d55a452-c115-482d-b6a5-c3f84da4088d",
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   "outputs": [],
   "source": [
    "bqm = dimod.BinaryQuadraticModel.from_qubo(Q.Q, Q.offset)"
   ]
  },
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     "text": [
      "[DYNEX] PRECISION SET TO 0.001\n",
      "[DYNEX] SAMPLER INITIALISED\n",
      "[DYNEX|TESTNET] *** WAITING FOR READS ***\n",
      "╭────────────┬──────────┬─────────────────┬─────────────┬───────────┬───────────────────────────┬────────────┬─────────┬────────────────╮\n",
      "│   DYNEXJOB │   QUBITS │   QUANTUM GATES │   BLOCK FEE │ ELAPSED   │ WORKERS READ              │ CIRCUITS   │ STEPS   │ GROUND STATE   │\n",
      "├────────────┼──────────┼─────────────────┼─────────────┼───────────┼───────────────────────────┼────────────┼─────────┼────────────────┤\n",
      "│         -1 │       14 │              29 │           0 │           │ *** WAITING FOR READS *** │            │         │                │\n",
      "╰────────────┴──────────┴─────────────────┴─────────────┴───────────┴───────────────────────────┴────────────┴─────────┴────────────────╯\n",
      "\n",
      "[DYNEX] FINISHED READ AFTER 0.30 SECONDS\n",
      "[DYNEX] SAMPLESET READY\n",
      "   0  1  2  3  4  5 energy num_oc.\n",
      "0  1  0  0  1  1  0    1.0       1\n",
      "['BINARY', 1 rows, 1 samples, 6 variables]\n"
     ]
    }
   ],
   "source": [
    "sampleset = dynex.sample(bqm, mainnet=False, num_reads=1024, annealing_time=200);\n",
    "print(sampleset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "977fe637-057c-4993-a427-caa0b3ed52ab",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal solution: ({'c', 'b', 'a'}, {'e', 'd', 'f'}) valid? True\n"
     ]
    }
   ],
   "source": [
    "solution = problem.convert_solution(sampleset.first.sample)\n",
    "print('Optimal solution:',solution,'valid?',problem.is_solution_valid(solution))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8053b159-7735-4fe2-a3b1-01a68aea21ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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