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        "description": "![wildqat](MDR_Wildqat.png)\n\nWildqat Python SDK\n--------\nPython Framework for QUBO \n\nVersion\n--------\n1.1.6\n\nInstall\n--------------------\n\n```\n$ pip3 install wildqat\n```\n\nor\n\n```\n$ git clone https://github.com/mdrft/Wildqat.git\n$ python setup.py install\n```\n\nExample\n-------\n\n```python\nfrom wildqat import *\na = opt()\na.qubo = [[4,-4,-4],[0,4,-4],[0,0,4]]\na.run() #=> [1, 1, 1]\nprint(a.E[-1]) #=>[0.0]\n```\n\nParameters\n-------\nSome parameters for simualtion is adjustable\n```python\n#for sa\na.Ts  = 10    #default 5\na.R   = 0.99  #default 0.95\na.ite = 10000 #default 1000\n```\n\nEnergy Function\n-------\nEnergy function of the calculation is stored in attribute E as an array.\n```python\n\nprint(a.E[-1]) #=>[0.0]\n\n#if you want to check the time evolution\na.plot()\n```\n\nSampling\n-------\nSampling and counter function with number of shots.\n```python\n\nresult = a.run(shots=100,sampler=\"fast\")\n\nprint(result)\n\n[[0, 1, 0],\n [0, 0, 1],\n [0, 1, 0],\n [0, 0, 1],\n [0, 1, 0],\n ...\n \n counter(result) # => Counter({'001': 37, '010': 25, '100': 38})\n\n```\n\nUniversal Gate Model Operator\n-------\nWith blueqat, you can easily simulate combinatorial optimization problem on Universal Gate Model\nlink:<a href=\"https://github.com/mdrft/Blueqat\">Blueqat</a>\n```python\nfrom wildqat import *\nfrom blueqat import vqe\n\nqubo = pauli(sel(4,1)) # =>  0.5*Z[0]*Z[1] + 1.0*I - Z[2] - Z[0] + 0.5*Z[0]*Z[2] - Z[3] + 0.5*Z[0]*Z[3] - Z[1] + 0.5*Z[1]*Z[2] + 0.5*Z[1]*Z[3] + 0.5*Z[2]*Z[3]\nstep = 4\nresult = vqe.Vqe(vqe.QaoaAnsatz(qubo,step)).run()\nprint(result.most_common(5))\n\n# => (((0, 0, 1, 0), 0.24650337773427797), ((1, 0, 0, 0), 0.24650337773427794), ((0, 0, 0, 1), 0.24650337773427788), ((0, 1, 0, 0), 0.24650337773427783), ((0, 0, 0, 0), 0.0034271782738342416))\n```\n\nConnection to D-Wave cloud\n-------\nDirect connection to D-Wave machine with apitoken  \nhttps://github.com/dwavesystems/dwave-cloud-client is required\n```python\nfrom wildqat import *\na = opt()\na.dwavetoken = \"YOUR TOKEN HERE\"\na.qubo = [[0,0,0,0,-4],[0,2,0,0,-4],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,4]] \na.dw()\n\n# => [1,1,-1,1,1,0,0,0,0,0,0]\n```\n\n\n\nFunctions\n-------\n\nsel(N,K,array)  \nAutomatically create QUBO which select K qubits from N qubits\n```python\nprint(sel(5,2))\n#=>\n[[-3  2  2  2  2]\n [ 0 -3  2  2  2]\n [ 0  0 -3  2  2]\n [ 0  0  0 -3  2]\n [ 0  0  0  0 -3]]\n```\n\nif you set array on the 3rd params, the result likely to choose the nth qubit in the array\n```python\nprint(sel(5,2,[0,2]))\n#=>\n[[-3.5  2.   2.   2.   2. ]\n [ 0.  -3.   2.   2.   2. ]\n [ 0.   0.  -3.5  2.   2. ]\n [ 0.   0.   0.  -3.   2. ]\n [ 0.   0.   0.   0.  -3. ]]\n```\n\nnet(arr,N)  \nAutomatically create QUBO which has value 1 for all connectivity defined by array of edges and graph size N\n```python\nprint(net([[0,1],[1,2]],4))\n#=>\n[[0. 1. 0. 0.]\n [0. 0. 1. 0.]\n [0. 0. 0. 0.]\n [0. 0. 0. 0.]]\n```\nthis create 4*4 QUBO and put value 1 on connection between 0th and 1st qubit, 1st and 2nd qubit  \n\nzeros(N)\nCreate QUBO with all element value as 0  \n```python\nprint(zeros(3))\n#=>\n[[0. 0. 0.]\n [0. 0. 0.]\n [0. 0. 0.]]\n```\n\ndiag(list)\nCreate QUBO with diag from list  \n```python\nprint(diag([1,2,1]))\n#=>\n[[1 0 0]\n [0 2 0]\n [0 0 1]]\n```\n\nrands(N)\nCreate QUBO with random number  \n```python\nprint(rands(2))\n#=>\n[[0.89903411 0.68839641]\n [0.         0.28554602]]\n```\n\ndbms(list,weight)\nCreate QUBO on DBM or RBM model\n```python\nprint(dbms([2,2]))\n#=>\n[[0.60181446 0.         0.41019491 0.17743251]\n [0.         0.61166332 0.87964297 0.46509678]\n [0.         0.         0.29579843 0.        ]\n [0.         0.         0.         0.96743087]]\n```\n\nDocument\n----------\nEnglish  \nhttps://wildqat.readthedocs.io/en/latest/\n\n\u65e5\u672c\u8a9e  \nhttps://wildqat.readthedocs.io/ja/latest/\n\nTutorial\n----------\nEnglish  \nhttps://github.com/mdrft/Wildqat/tree/master/examples_en  \n\n\u65e5\u672c\u8a9e  \nhttps://github.com/mdrft/Wildqat/tree/master/examples_ja\n\nAuthors\n----------\n[Yuichiro Minato](https://github.com/minatoyuichiro)(MDR), [Asa Eagle](https://github.com/Morning777)(MDR), [Satoshi Takezawa](https://github.com/takebozu)(TerraSky), [Seiya Sugo](https://github.com/seiya-sugo)(TerraSky)\n\nDisclaimer\n----------\nCopyright 2018 The Wildqat Developers.\n\n",
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