grover.ipynb

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      "###Computational Hardness\n",
      "\n",
      "A \"decision problem\" can be modelled as :\n",
      "\n",
      "<h4 align=\"center\">$f: \\{0,1\\}^n \\mapsto \\{0,1\\}$</h4>\n",
      "\n",
      "A resource independent way of quantifying the hardness of a problem is to analyse how the size of a circuit scales with the input size $n$. The above problem scales exponentially ($2^n$) with input size $n$.\n",
      "\n",
      "We say that a decision problem is in $P$ if the algorithm (or circuit family) can be described as a polynomial function of input size $n$. As a matter of fact, most of the functions described by the above mapping are not in $P$. For such functions, the only way to determine $f(x)$ is to search a table for the solution out of exponentially many possible entries. This table can be considered as the digital equivalent of a haystack, and the solution to the problem is a needle which we are interested in finding. Hence finding a solution to an NP-complete [a] problem can be seen as a search problem represented by the following Boolen satisfiability [b].\n",
      "\n",
      "<h4 align=\"center\">$\\boxed{f(x_1, x_2, x_3, ..., x_n) = (x_1 \\lor x_2' \\lor x_3) \\land (x_2 \\lor x_5' \\lor x_6) \\land ... }$</h4>\n",
      "\n",
      "The satisfiability problem is to find a configuration $x_1, x_2, x_3, ..., x_n$ such that $f(x_1, x_2, x_3, ..., x_n) = 1$. It is clear that the problem requires us to look up a haystack of $2^n$ entires and find a needle. In fact there are thousands of NP-complete problems which are computationally equivalent to satisfiability. The problems in P are the ones which have enough structure in them to be computationally easy to solve.\n",
      "\n",
      "<hr>\n",
      "[a] NP stands for \"nondeterministic polynomial time\". If a decision problem is in NP, it may not be possible to determine the solution in polynomial time, but easy to verify the correctness of the solution once we find it. For example, the prime factorization problem is in NP. A problem is NP-Complete if any problem in NP is polynomially redicible to it. Example, CIRCUIT-SAT. It can also be shown that CIRCUIT-SAT problem is reducible to Hamiltonian Path problem making the latter NP-Complete.\n",
      "\n",
      "[b] The Boolean satisfiability is a part of Cook's Theorem (1971), which states that every problem in NP is polynomially (in input/circuit family) reducible to CIRCUIT-SAT."
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      "# Setup the matplotlib graphics library and configure it to show \n",
      "# figures inline in the notebook\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Import Numpy arrays\n",
      "import numpy as np\n",
      "\n",
      "# Setup Ipython to load local images\n",
      "from IPython.display import Image\n",
      "\n",
      "import pprint"
     ],
     "language": "python",
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     "prompt_number": 10
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      "###Quantum Fourier Transform\n",
      "\n",
      "Quantum Fourier Transform (QFT) or Fourier Sampling is the quantum analogue of the Discrete Fourier Transform (DFT) [10]. Most Quantum Algorithms derive their exponential speed by virtue of QFT. QFT lets us define a state in a new computational basis. From a Physics point of view, the relationship between the two bases is analogous to that between the momentum and position bases of a particle. The mathematical representation of DFT is:\n",
      "\n",
      "<div align=\"center\">$\\boxed{y_k = \\frac{1}{\\sqrt{N}} \\sum\\limits_{j=0}^{N-1} x_j e^\\frac{2 \\pi ijk}{N}}$</div>\n",
      "Let us consider an initial state vector:\n",
      "<div align=\"center\">$\\left| \\psi \\right\\rangle = \\sum\\limits_{j=0}^{N-1} a_j \\left| j \\right\\rangle = \\left( \\begin{array}{ccc}\n",
      "a_0 \\\\\n",
      "a_1 \\\\\n",
      "a_2 \\\\\n",
      "\\vdots   \\\\\n",
      "a_N-1 \\end{array} \\right) $</div>\n",
      "\n",
      "The Quantum Fourier Transform is applied on the above state as:\n",
      "<div align=\"center\">$F\\left| \\psi \\right\\rangle = \\sum\\limits_{j=0}^{N-1} b_k \\left| j \\right\\rangle$</div>\n",
      "\n",
      "\n",
      "<div align=\"center\">where $b_k = \\frac{1}{\\sqrt{N}} \\sum\\limits_{j=0}^{N-1} e^\\frac{2 \\pi ijk}{N}$</div>\n",
      "\n",
      "It is clear that Discrete Fourier Transform (DFT) is performed on the amplitudes of the quantum state, through a linear transformation. The $F$ operator can be written in outer product notation as:\n",
      "<div align=\"center\">$\\boxed{F = \\frac{1}{\\sqrt{N}} \\sum\\limits_{j,k =0}^{N-1} e^\\frac{2 \\pi ijk}{N} \\left| k \\right\\rangle \\langle j|}$</div>\n",
      "\n",
      "The $F$ operator in matrix form is as follows:\n",
      "\n",
      "<div align=\"center\">$QFT_N = \\frac{1}{\\sqrt{N}} \\left( \\begin{array}{ccc}\n",
      "1 & 1 & 1 & 1 & \\cdots & 1 \\\\\n",
      "1 & \\omega & \\omega^2 & \\omega^3 & \\cdots & \\omega^{N-1} \\\\\n",
      "1 & \\omega^2 & \\omega^4 & \\omega^6 & \\cdots & \\omega^{2(N-1)} \\\\\n",
      "1 & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
      "1 & \\omega^{N-1} & \\omega^{2(N-1)} & \\omega^{3(N-1)} & \\cdots & \\omega^{(N-1)^2} \\\\\n",
      "\\end{array} \\right)$</div>\n",
      "\n",
      "<div align=\"center\">where $\\omega = e^\\frac{i 2 \\pi}{N} = cos \\frac{2N}{\\pi} + \\iota sin \\frac{2N}{\\pi}$</div>\n",
      "\n",
      "In general we see that the matrix for Quantum Fourier Transform has the form:\n",
      "<div align=\"center\">$QFT_N (i,j) = \\omega^{ij}$</div>\n",
      "\n",
      "Two-Qubit QFT in matrix form:\n",
      "\n",
      "<h4 align=\"center\">$F = \\frac{1}{2} \\left( \\begin{array}{ccc}\n",
      "1 & 1 & 1 & 1 \\\\\n",
      "1 & \\omega & \\omega^2 & \\omega^3 \\\\\n",
      "1 & \\omega^2 & \\omega^4 & \\omega^6 \\\\\n",
      "1 & \\omega^3 & \\omega^6 & \\omega^9 \\end{array} \\right) $\n",
      "$= \\frac{1}{2} \\left( \\begin{array}{ccc}\n",
      "1 & 1 & 1 & 1 \\\\\n",
      "1 & i & -1 & -i \\\\\n",
      "1 & -1 & 1 & -i \\\\\n",
      "1 & -i & -1 & i \\end{array} \\right)\n",
      "$</h4>\n",
      "\n",
      "Note that the QFT matrix is a unitary operator since $F F^\\dagger = F^\\dagger F = I$"
     ]
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      "####Complexity:\n",
      "<div align=\"center\">$QFT_N = \\frac{1}{\\sqrt{N}} \\left( \\begin{array}{ccc}\n",
      "1 & 1 & 1 & 1 & \\cdots & 1 \\\\\n",
      "1 & \\omega & \\omega^2 & \\omega^3 & \\cdots & \\omega^{N-1} \\\\\n",
      "1 & \\omega^2 & \\omega^4 & \\omega^6 & \\cdots & \\omega^{2(N-1)} \\\\\n",
      "1 & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
      "1 & \\omega^{N-1} & \\omega^{2(N-1)} & \\omega^{3(N-1)} & \\cdots & \\omega^{(N-1)^2} \\\\\n",
      "\\end{array} \\right)$ \n",
      "$\\left( \\begin{array}{ccc}\n",
      "\\alpha_0 \\\\\n",
      "\\alpha_1 \\\\\n",
      "\\alpha_2 \\\\\n",
      "\\vdots   \\\\\n",
      "\\alpha_N-1 \\end{array} \\right)$\n",
      "$= \\left( \\begin{array}{ccc}\n",
      "\\beta_0 \\\\\n",
      "\\beta_1 \\\\\n",
      "\\beta_2 \\\\\n",
      "\\vdots   \\\\\n",
      "\\beta_N-1 \\end{array} \\right)$</div>\n",
      "\n",
      "To calculate $\\beta_0$ time required = O(N)\n",
      "\n",
      "To calculate $\\beta_1$ time required = O(N)\n",
      "<br>\n",
      "$\\vdots$\n",
      "<br>\n",
      "To calculate $\\beta_{N-1}$ time required = O(N)\n",
      "\n",
      "Hence an ordinary algorithm (or a human) would take O($N^2$) steps to compute the Fourier Transform of a state.\n",
      "\n",
      "The Fast Fourier Transform (FFT) algorithm (or [Cooley-Tuckey algorithm]) is a classical algorithm which can compute the DFT of a vector in O($N \\log {N}$) steps.\n",
      "\n",
      "The Quantum Fourier Transform (QFT) takes O($n^2$) steps at maximum.\n",
      "\n",
      "<h4 align=\"center\">$N=2^n$</h4>\n",
      "\n",
      "<h4 align=\"center\">$\\implies n = \\log{N}$</h4>\n",
      "\n",
      "Hence the computational complexity of performing a QFT is:\n",
      "<h4 align=\"center\">$O(\\log^2{N})$</h4>\n",
      "\n",
      "<div align=\"center\">or $O(2\\log{N}) = O(\\log{N})$</div>\n",
      "<br>\n",
      "Note that this is an exponential speedup as compared to the classical FFT [9] or FFTW [11] algorithms.\n",
      "<br>\n",
      "\n",
      "<!--\n",
      "###Translations [may not be correct]\n",
      "\n",
      "Let us consider a unitary operator $S$ which changes the value of $x$ by 1. Here the operator $S$ is a generator of the translation. We want to analyse the effect of this shift operator on the Fourier Transform of $\\left| x_j \\right\\rangle$. \n",
      "\n",
      "<div align=\"center\">$S\\left| x_j \\right\\rangle = \\left| x_{(j+1)mod N} \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\left| \\tilde{x_k} \\right\\rangle = \\frac{1}{\\sqrt{N}} \\sum\\limits_{j=0}^{N-1} e^\\frac{\\iota2 \\pi jk}{N} \\left| x_{j} \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{1}{\\sqrt{N}} \\sum\\limits_{j+1=0}^{j+1=N-1} e^\\frac{\\iota2 \\pi k(j+1)}{N} \\left| x_{j+1} \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{1}{\\sqrt{N}} \\sum\\limits_{j+1=0}^{j+1=N-1} e^{\\frac{\\iota 2 \\pi kj}{N}} e^{\\frac{\\iota2 \\pi k}{N}} \\left| x_{j+1} \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{e^{\\frac{-\\iota 2 \\pi k}{N}}}{\\sqrt{N}}\\sum\\limits_{j+1=0}^{j+1=N-1} e^{\\frac{\\iota 2 \\pi kj}{N}} e^{\\frac{\\iota4 \\pi k}{N}} \\left| x_{j+1} \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{e^{\\frac{-\\iota 2 \\pi k}{N}}}{\\sqrt{N}}\\sum\\limits_{j+1=0}^{j+1=N-1} e^{\\frac{\\iota 2 \\pi kj}{N}} \\left| x_{j+1} \\right\\rangle \\hspace{10 mm}(\\because e^{\\iota 2 \\pi k} = 1)$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{e^{\\frac{-\\iota 2 \\pi k}{N}}}{\\sqrt{N}}\\sum\\limits_{s=0}^{s=N-1} e^{\\frac{\\iota 2 \\pi k(s-1)}{N}} \\left| x_s \\right\\rangle \\hspace{10 mm}(s = j+1)$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{e^{\\frac{-\\iota 2 \\pi k}{N}}}{\\sqrt{N}}\\sum\\limits_{s=0}^{s=N-1} e^{\\frac{\\iota 2 \\pi ks}{N}} e^{\\frac{-\\iota 2 \\pi k}{N}}\\left| x_s \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = \\frac{e^{\\frac{-\\iota 2 \\pi k}{N}}}{\\sqrt{N}}\\sum\\limits_{j=0}^{j=N-1} e^{\\frac{\\iota 2 \\pi kj}{N}}\\left| x_j \\right\\rangle  \\hspace{10 mm}(\\because e^{\\iota 2 \\pi k} = 1)$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S\\left| \\tilde{x_k} \\right\\rangle = e^{\\frac{-\\iota 2 \\pi k}{N}} \\left| \\tilde{x_k} \\right\\rangle$</div>\n",
      "-->"
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      "\n",
      "####Product Representation of Quantum Fourier Transform:\n",
      "<div align=\"center\">$\\left| j \\right\\rangle \\rightarrow \\frac{1}{\\sqrt N} \\sum\\limits_{k=0}^{k=N-1} e^{\\frac{\\iota 2 \\pi kj}{N}}\\left| k \\right\\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\left| j_1 \\cdots j_n \\right\\rangle \\rightarrow \\frac{1}{2^{n/2}} \\sum\\limits_{k=0}^{k=2^{n/2}-1} e^{\\frac{\\iota 2 \\pi j}{2^{n/2}}}\\left| k_1 \\cdots k_n \\right\\rangle$</div>\n",
      "<br>\n",
      "\n",
      "We know, any binary fraction can be written as $\\sum\\limits_{l=1}^{n} k_l 2^{-l}$\n",
      "<br>\n",
      "\n",
      "<div align=\"center\">$\\Rightarrow \\left| j_1 \\cdots j_n \\right\\rangle \\rightarrow \\frac{1}{2^{n/2}} \\sum\\limits_{k=0}^{k=2^{n/2}-1} e^{\\iota 2 \\pi j \\sum\\limits_{l=1}^{n} k_l 2^{-l}} \\left| k_1 \\cdots k_n \\right\\rangle$</div>\n",
      "<br>\n",
      "\n",
      "<div align=\"center\">$= \\frac{1}{2^{n/2}} \\sum\\limits_{k=0}^{k=2^{n/2}-1} \\bigotimes_{l=1}^{n} e^{\\iota 2 \\pi j k_l 2^{-l}} \\left| k_l \\right\\rangle \\hspace{10 mm} (\\because \\left| k_1 \\cdots k_n \\right\\rangle = \\left| k_1 \\right\\rangle \\bigotimes \\left| k_2 \\right\\rangle \\bigotimes \\cdots \\bigotimes \\left| k_n \\right\\rangle)$</div>\n",
      "<br>\n",
      "\n",
      "We can break the $2^n$ terms in the summation and replace it by $n$ summations over an $n$-bit binary string.\n",
      "<br>\n",
      "\n",
      "<div align=\"center\">$\\Rightarrow \\left| j_1 \\cdots j_n \\right\\rangle \\rightarrow \\frac{1}{2^{n/2}} \\sum\\limits_{k_1=0}^{1} \\cdots \\sum\\limits_{k_n=0}^{1} \\bigotimes_{l=1}^{n} e^{\\iota 2 \\pi j k_l 2^{-l}} \\left| k_l \\right\\rangle$</div>\n",
      "<br>\n",
      "\n",
      "$n$ summation terms can be grouped to one by tensoring it $n$ times.\n",
      "<br>\n",
      "\n",
      "<div align=\"center\">$\\Rightarrow \\left| j_1 \\cdots j_n \\right\\rangle \\rightarrow \\frac{1}{2^{n/2}} \\bigotimes_{l=1}^{n} \\Bigg[ \\sum\\limits_{k_l=0}^{1} e^{\\iota 2 \\pi j k_l 2^{-l}} \\left| k_l \\right\\rangle \\Bigg]$</div>\n",
      "<br>\n",
      "\n",
      "<div align=\"center\">$\\Rightarrow \\left| j_1 \\cdots j_n \\right\\rangle \\rightarrow \\frac{1}{2^{n/2}} \\bigotimes_{l=1}^{n} \\Bigg[ \\left| 0 \\right\\rangle + e^{\\iota 2 \\pi j 2^{-l}} \\left| 1 \\right\\rangle \\Bigg]$</div>\n",
      "<br>\n",
      "\n",
      "We can represent $j2^{-l}$ as a binary fraction. Thus the resulting equation is:\n",
      "<br>\n",
      "\n",
      "<div align=\"center\">$\\boxed{\\left| j \\right\\rangle \\rightarrow \\frac{1}{2^{n/2}} \\Big[ (\\left| 0 \\right\\rangle + e^{\\iota 2 \\pi 0.j_n} \\left| 1 \\right\\rangle) \\bigotimes (\\left| 0 \\right\\rangle + e^{\\iota 2 \\pi 0.j_{n-1}j_n} \\left| 1 \\right\\rangle) \\cdots \\bigotimes (\\left| 0 \\right\\rangle + e^{\\iota 2 \\pi 0.j_1 j_2 \\cdots j_n} \\left| 1 \\right\\rangle) \\Big]}$</div>\n",
      "<br>"
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      "###Grover's algorithm: Overview\n",
      "\n",
      "**Problem Definition:**<br>\n",
      "In mathematical terms, the problem of unstructured search can be defined by the following quantum oracle.\n",
      "\n",
      "We are given a database with $N$ records labelled as $0, 1, 2, \\cdots, N-1$. We assume $N=2^n$ for the simple reason of storing each index in $n$ bits. Rather than dealing with list itself, we focus on the *index* of the list. The oracle is thus the following binary function with an $n$-bit binary string input.\n",
      "<br>\n",
      "<div align=\"center\">$f:\\{0,1\\}^n \\rightarrow \\{0,1\\}$</div>\n",
      "<br>\n",
      "<div align=\"center\">$f(x) = \\begin{cases}\n",
      "  1 & \\text{for $x = x^*$} \\\\\n",
      "  0 & \\text{otherwise}\n",
      "\\end{cases}$</div>\n",
      "<br>\n",
      "The above oracle $f$ is a blackbox function, which can be queried as many times as required. Each query to the oracle has a computational overhead. The goal is to find the target label $x^*$ with as the minimum number of queries to the oracle.\n",
      "\n",
      "**Example: **<br>\n",
      "There are many problems which can be rephrased as a search problem. One practical example is the random brute force attack on a Data Encryption Standard (DES) encrypted ciphertext. Assume that the key is a 56-bit number.\n",
      "\n",
      "P (plaintext) = \"BlowUpHRI\"\n",
      "<br>\n",
      "C (ciphertext) = \"9423180a9fcaa78db8b4d7a3b8f9a9b67e075dc5\" (say)\n",
      "<br>\n",
      "$K^*$ is the 56-bit key used in the DES encryption.\n",
      "<br><br>\n",
      "To crack the above ciphertext, we have to perform a brute force with each of the $N = 2^{56}$ keys until we find $K^*$ such that:\n",
      "<div align=\"center\">$\\boxed{DES(C, K^*) = P}$</div>\n",
      "<br>\n",
      "\n",
      "The oracle for the above problem would be:\n",
      "<div align=\"center\">$f(K) = \\begin{cases}\n",
      "  1 & \\text{for $K = K^*$} \\\\\n",
      "  0 & \\text{otherwise}\n",
      "\\end{cases}$</div>\n",
      "<br>\n",
      "\n",
      "**Classical algorithms:**<br>\n",
      "$O(N)$ expcected time with linear search.<br>\n",
      "$O(\\frac{N}{2})$ expected time with random brute force.<br>\n",
      "$O(\\log{N})$ with binary search for databases with structure (sorted).\n",
      "\n",
      "\n",
      "**Quantum algorithm:**<br>\n",
      "Claim is that quantum algorithm for unstructured search will take time $O(\\sqrt{N})$. To search a desired element in an unstructured database, a quantum mechanical algorithm will take at least $\\Omega(\\sqrt{N})$ steps [5]. Hence the number of steps required by Grover's algorithm is within a constant factor of the fastest possible quantum mechanical algorithm.\n",
      "\n",
      "**Worst case:**<br>\n",
      "There is only $x$ such that $f(x) = 1$\n",
      "\n",
      "\n"
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      "###Grover's Algorithm: Steps\n",
      "\n",
      "**Phase Inversion:**<br>\n",
      "The initial state of the system is a uniform superposition of all states. The phase inversion step inverts the sign of the amplitude of the needle ($x^*$).\n",
      "<br><br>\n",
      "<div align=\"center\">$\\boxed{\\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\xrightarrow{U_{phase}} \\sum\\limits_{x \\neq x^*} \\alpha_x \\left| x \\right\\rangle - \\alpha_{x^{*}} \\left| x \\right\\rangle}$</div>\n",
      "<br>\n",
      "The oracle function $f$ can be implemented as an ouput register of a unitary transformation $U_{phase}$ as:\n",
      "<br><br>\n",
      "<div align=\"center\">$\\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\left| 0 \\right\\rangle \\rightarrow \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\left| 0 \\oplus f(x) \\right\\rangle = \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\left| f(x) \\right\\rangle$</div>\n",
      "<br>\n",
      "We can use a small trick to implement phase inversion in the uniform superposition by using the state $\\left| - \\right\\rangle$ as the answer bit.\n",
      "<br><br>\n",
      "<div align=\"center\">$\\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\left| - \\right\\rangle \\rightarrow \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\left| 0 \\oplus f(x) \\right\\rangle = \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\left| f(x) \\right\\rangle$</div>\n",
      "\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\Big( \\frac {\\left| 0 \\right\\rangle - \\left| 1 \\right\\rangle}{\\sqrt 2} \\Big) \\xrightarrow{U_{phase}} \\sum\\limits_{x} \\frac{\\alpha_x}{\\sqrt 2} \\Big( \\left| x \\right\\rangle \\left| 0 \\oplus f(x) \\right\\rangle - \\left| x \\right\\rangle \\left| 1 \\oplus f(x) \\right\\rangle \\Big)$</div>\n",
      "\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\Big( \\frac {\\left| 0 \\right\\rangle - \\left| 1 \\right\\rangle}{\\sqrt 2} \\Big) \\xrightarrow{U_{phase}} \\sum\\limits_{x} \\frac{\\alpha_x}{\\sqrt 2} \\Big( \\left| x \\right\\rangle \\left| f(x) \\right\\rangle - \\left| x \\right\\rangle \\left| \\overline{f(x)} \\right\\rangle \\Big)$</div>\n",
      "\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\Big( \\frac {\\left| 0 \\right\\rangle - \\left| 1 \\right\\rangle}{\\sqrt 2} \\Big) \\xrightarrow{U_{phase}} \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle (-1)^{f(x)} \\Big( \\frac {\\left| 0 \\right\\rangle - \\left| 1 \\right\\rangle}{\\sqrt 2} \\Big)$</div>\n",
      "<br>\n",
      "It is evident that the oracle qubit $\\left| - \\right\\rangle$ is always unchanged and hence can be ommitted from the above equation.\n",
      "<br><br>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{\\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle \\xrightarrow{U_{phase}} \\sum\\limits_{x} \\alpha_x \\left| x \\right\\rangle (-1)^{f(x)}}$</div>\n",
      "<br><br>\n",
      "The above operation can be represented by a unitary operator as:\n",
      "<div align=\"center\">$\\boxed{I_t = I - 2|t\\rangle\\langle t|}$</div>"
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      "**Reflection about mean:**\n",
      "<br>\n",
      "Though phase inversion logically separates the search element from the rest of the haystack, it doesn't help in increasing the probability of the outcome corresponding to the \"needle\" when a measurement is performed. To solve this problem, we do a reflection about mean.\n",
      "<br><br>\n",
      "Before defining an operator to perform the above transformation, let us consider an operator $S$ which does a reflection about $|0^n \\rangle$. The unitary transformation describing this reflection can be represented as:\n",
      "<br>\n",
      "<div align=\"center\">$\\boxed{S=2|0^n \\rangle \\langle 0^n| - I}$</div><br>\n",
      "<div align=\"center\">$S|x \\rangle = (2|0^n \\rangle \\langle 0^n| - I) |x \\rangle$</div>\n",
      "<br>\n",
      "Assuming a single qubit system, we can construct the $S$ operator as:\n",
      "<div align=\"center\">$\\Rightarrow S|x \\rangle = \\Bigg( 2 \n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 \\\\\n",
      "0 \\end{array} \\right]\n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 & 0 \\\\\n",
      "\\end{array} \\right] -\n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 & 0 \\\\\n",
      "0 & 1 \\\\\n",
      "\\end{array} \\right]\n",
      "\\Bigg) |x \\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S|x \\rangle = \\Bigg( 2 \n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 & 0\\\\\n",
      "0 & 0 \\end{array} \\right]-\n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 & 0 \\\\\n",
      "0 & 1 \\\\\n",
      "\\end{array} \\right]\n",
      "\\Bigg) |x \\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow S|x \\rangle =\n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 & 0\\\\\n",
      "0 & -1 \\end{array} \\right]|x \\rangle$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{S|x \\rangle = \\sigma_z|x \\rangle}$</div>\n",
      "<br>\n",
      "Another fancy way of writing the unitary operator $|S \\rangle$ for reflection about $|0^n \\rangle$ is:\n",
      "<br>\n",
      "<div align=\"center\">$\\boxed{|x \\rangle \\rightarrow -(-1)^{\\delta_{x,0}} |x \\rangle}$</div><br>\n",
      "where, $\\delta_{x,0}$ is the Kronecker Delta function for orthonormality condition.\n",
      "<div align=\"center\">$|0 \\rangle \\rightarrow -(-1)^{\\delta_{0,0}} |0 \\rangle = |0 \\rangle$</div>\n",
      "<div align=\"center\">$|1 \\rangle \\rightarrow -(-1)^{\\delta_{1,0}} |0 \\rangle = -|1 \\rangle$</div>\n",
      "<br>\n",
      "If the initial superposition is of the form $\\sum\\limits_{x} \\alpha_x | x \\rangle$, the mean of the superposition is given by:\n",
      "<div align=\"center\">$\\boxed{\\mu = \\frac{\\sum\\limits_{x}^{N-1} \\alpha_x}{N}}$</div>\n",
      "<br>\n",
      "In general, the reflection about mean $\\mu$ can be carried out by first mapping $|\\psi \\rangle$ to $|0^n \\rangle$ by applying $H^{\\otimes n}$ or the Quantum Fourier Transform (QFT), then applying the $S$ operator defined above, and then mapping $|0^n \\rangle$ back to $|\\psi \\rangle$ by applying $H^{\\otimes n}$ or the Quantum Fourier Transform (QFT) again.\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{D = H^{\\otimes n} S H^{\\otimes n}}$</div>\n",
      "<br>\n",
      "Details on the Diffusion operator is given in the next section."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Grover's Algorithm: Implementation\n",
      "\n",
      "**Algorithm:**\n",
      "\n",
      " 1. Start the state in $| \\psi \\rangle \\sum\\limits_{x} \\frac{1}{\\sqrt N} | x \\rangle$.\n",
      " 2. Invert the phase of the search element by applying the oracle $f$.\n",
      " 3. Apply the Quantum Fourier Transform ($H^{\\otimes n}$).\n",
      " 4. Apply conditional phase shift about $| 0^n \\rangle$.\n",
      " 5. Apply inverse Quantum Fourier Transform ($H^{\\otimes n}$).\n",
      " 6. Repeat steps 2 to 5 for $O(\\sqrt N)$ steps.\n",
      " \n",
      "The steps 3-5 can be combined into a single unitary transformation $I_s$ such that the net Grover's operator becomes $G=I_s I_t$:\n",
      "<br>\n",
      "<div align=\"center\">$D = H^{\\otimes n} \\big[ 2|0^n \\rangle \\langle 0^n| - I \\big] H^{\\otimes n}$</div>\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{I_s: D = 2|\\psi \\rangle \\langle \\psi| - I}$</div>\n",
      "<br>\n",
      "The above operator is also known as *Diffusion operator*, which can be represented in matrix form as:\n",
      "<br><br>\n",
      "<div align=\"center\">$D = H^{\\otimes n} \\Bigg(2\n",
      "\\left[ \\begin{array}{ccc}\n",
      "1 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\end{array}\n",
      "\\right] - I \\Bigg)| H^{\\otimes n}$</div>\n",
      "<br><br>\n",
      "<div align=\"center\">$D = H^{\\otimes n} \\Bigg(\n",
      "\\left[ \\begin{array}{ccc}\n",
      "2 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\end{array}\n",
      "\\right] - I \\Bigg) H^{\\otimes n}$</div>\n",
      "<br><br>\n",
      "<div align=\"center\">$D = H^{\\otimes n}\n",
      "\\left[ \\begin{array}{ccc}\n",
      "2 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\end{array}\n",
      "\\right]\n",
      "H^{\\otimes n} - H^{\\otimes n}IH^{\\otimes n}$</div>\n",
      "<br>\n",
      "<div align=\"center\">$D = \\frac{1}{\\sqrt{N}} \\left( \\begin{array}{ccc}\n",
      "1 & 1 & 1 & 1 & \\cdots & 1 \\\\\n",
      "1 & \\omega & \\omega^2 & \\omega^3 & \\cdots & \\omega^{N-1} \\\\\n",
      "1 & \\omega^2 & \\omega^4 & \\omega^6 & \\cdots & \\omega^{2(N-1)} \\\\\n",
      "1 & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
      "1 & \\omega^{N-1} & \\omega^{2(N-1)} & \\omega^{3(N-1)} & \\cdots & \\omega^{(N-1)^2} \\\\\n",
      "\\end{array} \\right)\n",
      "\\left[ \\begin{array}{ccc}\n",
      "2 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "0 & 0 & 0 & \\cdots & 0\\\\\n",
      "\\end{array}\n",
      "\\right]\n",
      "H^{\\otimes n} - I$</div>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div align=\"center\">$D = \\left[ \\begin{array}{ccc}\n",
      "\\frac{2}{\\sqrt N} & 0 & 0 & \\cdots & 0\\\\\n",
      "\\frac{2}{\\sqrt N} & 0 & 0 & \\cdots & 0\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "\\frac{2}{\\sqrt N} & 0 & 0 & \\cdots & 0\\\\\n",
      "\\end{array}\n",
      "\\right]\n",
      "H^{\\otimes n} - I$</div>\n",
      "<div align=\"center\">$D = \\left[ \\begin{array}{ccc}\n",
      "\\frac{2}{\\sqrt N} & 0 & 0 & \\cdots & 0\\\\\n",
      "\\frac{2}{\\sqrt N} & 0 & 0 & \\cdots & 0\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "\\frac{2}{\\sqrt N} & 0 & 0 & \\cdots & 0\\\\\n",
      "\\end{array}\n",
      "\\right]\n",
      "\\frac{1}{\\sqrt{N}} \\left( \\begin{array}{ccc}\n",
      "1 & 1 & 1 & 1 & \\cdots & 1 \\\\\n",
      "1 & \\omega & \\omega^2 & \\omega^3 & \\cdots & \\omega^{N-1} \\\\\n",
      "1 & \\omega^2 & \\omega^4 & \\omega^6 & \\cdots & \\omega^{2(N-1)} \\\\\n",
      "1 & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
      "1 & \\omega^{N-1} & \\omega^{2(N-1)} & \\omega^{3(N-1)} & \\cdots & \\omega^{(N-1)^2} \\\\\n",
      "\\end{array} \\right)- I$</div>\n",
      "<br>\n",
      "<div align=\"center\">$D = \\left[ \\begin{array}{ccc}\n",
      "\\frac{2}{N} & \\frac{2}{N} & \\frac{2}{N} & \\cdots & \\frac{2}{N}\\\\\n",
      "\\frac{2}{N} & \\frac{2}{N} & \\frac{2}{N} & \\cdots & \\frac{2}{N}\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "\\frac{2}{N} & \\frac{2}{N} & \\frac{2}{N} & \\cdots & \\frac{2}{N}\\\\\n",
      "\\end{array}\n",
      "\\right] - I$</div>\n",
      "<br>\n",
      "<div align=\"center\">$I_s: D = \\left[ \\begin{array}{ccc}\n",
      "\\big( \\frac{2}{N}-1 \\big) & \\frac{2}{N} & \\frac{2}{N} & \\cdots & \\frac{2}{N}\\\\\n",
      "\\frac{2}{N} & \\big( \\frac{2}{N}-1 \\big) & \\frac{2}{N} & \\cdots & \\frac{2}{N}\\\\\n",
      "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
      "\\frac{2}{N} & \\frac{2}{N} & \\frac{2}{N} & \\cdots & \\big( \\frac{2}{N}-1 \\big) \\\\\n",
      "\\end{array} \\right]$</div>\n",
      "<br>\n",
      "The above operator $D$ is a unitary transformation and hence can act on a Ket vector to give another Ket vector.\n",
      "<br>\n",
      "<div align=\"center\">D $\\left( \\begin{array}{ccc}\n",
      "\\alpha_0 \\\\\n",
      "\\alpha_1 \\\\\n",
      "\\alpha_2 \\\\\n",
      "\\vdots   \\\\\n",
      "\\alpha_N-1 \\end{array} \\right)$\n",
      "$= \\left( \\begin{array}{ccc}\n",
      "\\beta_0 \\\\\n",
      "\\beta_1 \\\\\n",
      "\\beta_2 \\\\\n",
      "\\vdots   \\\\\n",
      "\\beta_N-1 \\end{array} \\right)$</div>\n",
      "<br>\n",
      "It is easy to check that $\\beta_i = \\frac{-2}{N} \\sum\\limits_{j} \\alpha_j + \\alpha_i = -2\\mu + \\alpha_i$. Thus the Diffusion operator $D$ can flip the amplitiudes of the superposition about the mean $\\mu$.\n",
      "<div align=\"center\">$\\alpha_x \\xrightarrow{U_{mean}} 2\\mu - \\alpha_x = \\mu + (\\mu - \\alpha_x)$</div>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{\\sum\\limits_{x} \\alpha_x | x \\rangle \\xrightarrow{U_{mean}} \\sum\\limits_{x} (2\\mu - \\alpha_x) | x \\rangle}$</div>\n",
      "**Alternative algorithm (using Diffusion operator):**\n",
      "\n",
      " 1. Start the state in $| \\psi \\rangle \\sum\\limits_{x} \\frac{1}{\\sqrt N} | x \\rangle$.\n",
      " 2. Invert the phase of the search element by applying the oracle $f$.\n",
      " 3. Invert about mean using the Diffusion operator $D$.\n",
      " 4. Repeat steps 2 and 3 for $O(\\sqrt N)$ steps."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Helper functions implemented in Python\n",
      "def U_phase(psi, e):\n",
      "    \"\"\"\n",
      "    Unitary transformation to invert the phase of the search element\n",
      "    \n",
      "    Parameters                | Returns\n",
      "    --------------------------|----------------------------------\n",
      "    psi : ndarray             | psi : ndarray\n",
      "          Quantum state       |       State after phase inversion\n",
      "    e   : int                 | \n",
      "          Search element      |\n",
      "    \"\"\" \n",
      "    if psi.shape[0] >= e:\n",
      "        psi[e] *= -1\n",
      "    return psi\n",
      "\n",
      "def QFT(psi, N):\n",
      "    \"\"\"\n",
      "    Quantum Fourier Transform operator in N dimensions\n",
      "    \n",
      "    Parameters                | Returns\n",
      "    --------------------------|---------------------------\n",
      "    psi : ndarray             | QFT : ndarray\n",
      "          Quantum state       |       QFT unitary operator\n",
      "    N   : int                 | \n",
      "          Number of dimensions|\n",
      "          \n",
      "    Internals\n",
      "    ---------\n",
      "    Makes use of the DFT method of NumPy. QFT is a unitary operator\n",
      "    so the QFT matrix is its own inverse.\n",
      "    \"\"\"\n",
      "    return np.fft.fftn(psi)/N**0.5\n",
      "    \n",
      "def Diffusion(N):\n",
      "    \"\"\"\n",
      "    Diffusion operator D for Grover's iterations\n",
      "    \n",
      "    Parameters                | Returns\n",
      "    --------------------------|------------------\n",
      "    N   : int                 | D   : ndarray\n",
      "          Number of dimensions|       Unitary operator D\n",
      "    \"\"\"    \n",
      "    return np.ones((N, N)) * 2/N - np.identity(N)\n",
      "\n",
      "def __iter(N):\n",
      "    \"\"\"\n",
      "    Number of iterations to run the Grover's operators\n",
      "    \n",
      "    Parameters                | Returns\n",
      "    --------------------------|------------------\n",
      "    k  : int                  | N  :  int\n",
      "         Number of dimensions |       Number of Grover iterations\n",
      "    \"\"\"\n",
      "    return int(np.floor(np.pi * (N**0.5) /4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Grover's Algorithm: Examples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=4\n",
      "x=2\n",
      "\n",
      "psi = np.ones((N, 1))/N**0.5\n",
      "psi = U_phase(psi, x)\n",
      "psi = QFT(psi, N) # QFT on state psi\n",
      "\n",
      "si = np.zeros((N, 1))\n",
      "si[0][0] = 1 # The |0> state\n",
      "si_dag = np.conjugate(si.T) # The <0| state\n",
      "S = 2 * np.kron(si, si_dag) - np.identity(N) # S = (2|0><0|-I)\n",
      "\n",
      "psi = np.dot(S, psi) # Conditional phase shift operation\n",
      "psi = QFT(psi, N) # Inverse QFT on state psi\n",
      "\n",
      "psi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([[ 0.+0.j],\n",
        "       [ 0.+0.j],\n",
        "       [ 1.+0.j],\n",
        "       [ 0.+0.j]])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 256\n",
      "x = 55\n",
      "D = Diffusion(N)\n",
      "k = __iter(N) # Estimate least number of iterations required\n",
      "probability_amplitude_list = [1/(N**0.5)]\n",
      "psi = np.ones((N, 1))/N**0.5\n",
      "\n",
      "for c in xrange(k):\n",
      "    if c==k-1:\n",
      "        fig = plt.figure(figsize=(5, 5))\n",
      "        ax1 = fig.add_subplot(311)\n",
      "        plt.bar(np.arange(N), psi.T[0])\n",
      "        ax1.set_title(\"Superposition in iteration: %s\"%(c+1))\n",
      "    \n",
      "        psi = U_phase(psi, x) # Conditional phase shift\n",
      "        ax2 = fig.add_subplot(312)\n",
      "        ax2.set_title(\"Conditional phase shift\")\n",
      "        plt.bar(np.arange(N), psi.T[0])\n",
      "    \n",
      "        psi = np.dot(D, psi) # Reflection about mean\n",
      "        ax3 = fig.add_subplot(313)\n",
      "        ax3.set_title(\"Reflection about mean\")\n",
      "        plt.bar(np.arange(N), psi.T[0])\n",
      "        \n",
      "        fig.text(0.5, 0.01, 'Database entries', ha='center', va='center')\n",
      "        fig.text(0.01, 0.5, 'Probability amplitude', ha='center', va='center', rotation='vertical')\n",
      "        fig.tight_layout()\n",
      "        probability_amplitude_list.append(psi[x][0])\n",
      "        \n",
      "    else:\n",
      "        psi = U_phase(psi, x) # Conditional phase shift\n",
      "        psi = np.dot(D, psi) # Reflection about mean\n",
      "        probability_amplitude_list.append(psi[x][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5J8RiMUaMGIEVK1YAePyPKDY2FjU1Ndyt5ctoW9PS0sL777+P3Nxc3LhxAzk5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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1554b35b10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(\"plots.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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mvr6etrY2nE4nXl5eBAcH4+Pjg8vlorOzk/r6epxOpzLropQDF+bp6UlKSgpF\nRUV8+OGHtLS0oNFoGDFihLKwJsCyZcs4ffo0L7zwAhERESQkJBAUFNSvsuH06dOsXLmS3t5exo8f\nz5IlSwgPD8ftdrN06VJWrVrFq6++SkdHB1FRUcyaNUtZtyYjI+OC16i4uJiysjLUajXTp09n7ty5\nBAYG4u/vj91u55133mHFihWEhYVxww03cN1119HW1kZgYCA5OTlKK/XPfvYz0tLSaGpqYsSIEcpz\nae7cuVRVVfHee++RkZGh9D7oq/CZOHEibrebt99+m9zcXIYOHcpdd91FcnIyra2tREVFKeUAnGsh\nGjVq1EXXyYmIiMBut2MwGHC73f2eiX0VTCkpKRf8vFqtJi4ujvDwcFQqFSEhIfz4xz9m1apVfPrp\np/zmN79h4sSJaLVacnJy+Nvf/oZarSYzM5Pk5GR0Oh0RERH9Ws9DQkJYsmQJTz31FI8//jixsbFM\nmjQJh8OhXI+f/OQnvPDCCzz//PPcfPPNREVFER8fr6yls2TJEjw9PcnJycFutzN16lSWLl2Kp6cn\nXl5eJCYmKu9zarWakJAQkpOTlbK2b4zrZysbL1QxplaraWho4Nlnn1W2Z2Rk8Otf/5qkpCS5mQcI\nlfsKXCSib9D0+++/T1FREf7+/sycOZMlS5YoN2Rf4ffmm2/y0UcfKbUMv/71r/H19eWxxx5Tpvd1\nuVwUFxfzyCOPcNNNN33jRRrFl6uvryc5OZkjR44QHx9/SVo5tm3bxkMPPcT69eu/ctO3EGJwlAVO\np5M33niD9evX09vby9ChQ7nvvvuIiYlRKgHsdjsvvvgimzdvpqenh7CwMBYsWMCsWbOwWCx8+umn\nvPPOO9jtdpKSkrj//vsvWJstLp3bb7+dmJgYHnjggX4v/v8XEydO5O6772bBggVSYy/EIHFFtuhY\nrVa2bt2KwWDg3XffZffu3XzyySeMHj1aWVPE6XRSXV3NG2+8wcaNGzGZTCxYsICDBw8yefJkHn/8\n8X4v34899hgRERHf6cq9Qggh/m/a2tp46qmn+Oijj4iMjOTuu+/m0KFDSlfKPtdddx0LFiwgODiY\nl19+mb179zJy5EhOnjzJ3r17uf/++0lNTWXNmjWsXLmSxx57TBJXCCG+Z+or8aRra2txuVzEx8dj\nMpmIiYkhKiqq39STNpuN8vJyhg0bRmBgIFqtltmzZ7Nnz55+C0a53W7Wr19PYmIiSUlJF+wGJC5h\nhlWrL3n3QJ1Od9EBiUKIwatvLFx6ejrBwcGEyn2KAAAgAElEQVTodDquuuoqSkpK+o0H0el0JCcn\nEx4eriyEaDAYsFqtNDY20tXVxbhx4wgMDGTEiBHs27dPEvdbZjKZ8PLyuqRjF/uu6/cxHlII8e24\nIlt0urq6cLlcStO0h4cHOp2O9vZ2ZR+Hw0Fra6syUBDODRgvKyvrN2iuo6OD7du3c8sttygLO4pv\nT0hIyHkztPxfTZ48mW3btkniCnGFcbvdNDQ0EBgYqLzc+vv7c+zYsfNmRuzbv6Ojg/LyclwuFzEx\nMezfvx+bzaYsQqvX65W1m8S357PjJi6VjRs3SsIKMchckVXYffPYf7YG3+VynTfPulqtPq+W3263\n95vCds2aNaSkpJCQkHDBQe5CCCEu7/Lg8y3EfWumfJ7D4eCtt96iubmZuXPnYjAYzisn3G73BYMk\nIYQQ370r8s3c29sbtVqtzPHeN5/9Z2ex0mq1+Pv791u/pKmpieDgYKVQs1gsbNiwgTvuuOOiA09X\nrlzJo48+SnNzs+Q2IcSAptPpGDlyJHl5eYMmyAkJCaGlpUUJbFpbW/H19T1vFiq73c5rr71GSUkJ\nCxcuZOTIkUqa6PV6ZdHfnp6ei053PmHCBI4fP/6NFi8UQojLSXBwMH/605+46aabJNC53PRNQ1tR\nUUFXVxdVVVVUV1czbdo02trasNvtBAYGkpSUxJNPPklLSwve3t5s2LCB+++/X5lzfe3atSQlJZGQ\nkHDRxQdbWlq47rrr+POf/zzg0+3w4cPU1dUxe/bsQZEPSkpKOH78OAsXLhzw51JZWUlBQQFLly4d\n8OdSVVXFjh07lPUuBrrGxkbWrl3Lj3/84wF/LqdOneKGG24YNGWBRqNh5MiRHDlyhKamJnQ6HVu3\nbmXmzJnodDrq6+vx9fVFrVbz73//m5MnT7Jo0SKys7OVVqDQ0FB8fHzYs2cPQ4cOVdZeuZCysjLy\n8/O/cGr4geKf//wnS5cuVaawH8heeukl5s2bpyxePNC9+eabTJo0aVDM/Pfee+8xevToQbNY5/r1\n64mLiyMzM3PAn8uDDz5IW1vbZf87r8hAx2AwcN111/H2228zb948goODmT9/PkFBQbz55pvU1tby\nxBNPEBMTw+23387ixYtxuVxkZ2czceJEpfbugw8+UKa4vNjgRbfbrazTM9D5+flhsVgGxbnAub74\nJpNpUJxPa2vroDmXrq6uQXMugLK2ymA4n/b29n4ruQ+W58Bvf/tb7r77bmw2G6NGjSI7O5tjx46R\nk5PDj370I3x9fdm5cyeHDx+moKAADw8P4uLiWLZsGePGjaOpqYm//vWv2Gw20tLSeOihhy6aFwIC\nAgZFXvD29iYwMLDfONaBfi6D5ZljMpkGTT4zmUz4+/sPmmvj4+MzaM5Hr9czEFaouSIDnb7FEn/x\ni19gNpuVWbcMBgM33XQTdrsdlUqFt7c3S5cu5dprr1UyqMlkQqVSodfree655wgICFAGoQohhBhY\nZYFarWb+/PlMnDgRl8ulLCobGBjIsGHD8PPzQ6PR8Mwzz2Cz2ZSuy3q9Hl9fX4xGIzNmzGDMmDG4\nXC48PT0JDg6WxBVCCAl0vt9INCQkpN8KwUC/2im1Wo2Pj0+/tRQ++/+io6OvqDTrm351sPD39x80\nzeG+vr4kJycPinMxmUwMHTp00OQzT09P0tPTpbS5jIMdb29vvL29+23//LYvWkz4Qp8f7DIyMpRu\n3APdsGHDBlWFZUpKCiaTaVCcS1JS0kXHvA1E8fHxg6IVVAIdMSiFh4efFxgOZEFBQYPmARoQEDBo\nVvL28/Nj+PDhgyafGY3Gi47ZEGKgGjdu3HnBwWOP/YG9e/fxs589wIwZ0wfMuYwZM2bQPD8BRowY\nMWiC0MEUUAMMHTp0UFUYS6BzGTt79iyffPIJe/fuJSwsjJkzZ5Kdnd1vH5vNxr59+3jnnXdwOp1M\nmzaNhQsXKhMPHDhwgHXr1lFXV8f48eOZPXv2oBhkejGD6WED51r1LjaJxECj0+kGzcNTq9UOmtpI\nODfg/UKtwkIMZBfK00ePHmHLls384AeLB/y5DGSDqXVxsLWUDqaAeqC4ItfRcTgc5OXlUVFRwdy5\ncwkODmbr1q3U19cr+7hcLhobG3nhhReYPn06CxYsYO3atVRUVOBwOKioqOCtt94iJiaGBQsWkJWV\nJS8zQgghrlhOpwOn04Hb7ZLEEEJcFq7IFp2mpibOnj1LeHg4M2fO5MiRI3z88cccO3aMsLAwAHp7\ne6moqKCnp4fZs2ej1WrJy8tj9+7dxMXFkZOTQ1RUFNOnTycqKuq8hUU/a8uWbdxyy61f+rt0Og1z\n5sz+wmNdzKlTVRQWFg26GZG+Do1GzeTJE7/RdKdtbW3s2LHrik+/zMx04uPjv/ZnXS4XH330Cb29\ndsB9xaWdWq0iISGezMyMr/W5WbNmyWQmQgghhAQ6l05raytOp5Pg4GD0ej3+/v74+vpy5swZZZ/e\n3l5qa2uJjo7GYDDgcrlITU2ltLQUh8PBzp07SU5OZuXKlWi1WqZMmcLIkSMv2MxaXl5KeXnZl/wq\nFRqNiqNHj1x0quov0tzcwpkzNVd0TZpKpWLfvj3fqGm4u7ub0tLyKz79oqOjvtG0l263m8LCY/8v\nUHRfkWkXHBz0hQPWL2Ty5MkS6AghhBAS6Fw6vb29qFQqZUyDRqNBpVJhtVqVfVwuFz09Pf1eQgwG\nA2azGZfLRUNDA1OmTCEsLIyWlhZycnIwGAyMHTv2Yq+CX/aqiNN5blFO8c243W5KS0slIf4P6Vdd\nXU11dbUkxjdIu8bGRhobG7/W5+x2uySeEEIIIYHOpePh4YFKpVJeMhwOBw6Ho19LgFqtxsvLC5vN\npmyzWCx4enqiUqlQqVRMnDiRsWPH0tHRwQMPPEB1dTUjR45Eq5XJ7IQQA193dzenTp1Sxi+ePXtW\nEkUIIcSAcUVORhAUFIRaraa+vp6enh6am5tpaWkhOjoaq9WK2WzGw8ODqKgoTp48SXd3N06nk8OH\nD5OcnIxOpyM+Pp7a2lqsVisajQa9Xv+NxtYIIcTlrK9ip+8/IYQQYqC4IpseAgICGDJkCAUFBbz2\n2mu0tLRgMpmIjY1l3759tLe3M2/ePBISEoiIiGDlypXo9Xo6OzvJzs5Gr9ezePFiDh48SENDAy6X\ni8jISOLj46U1RwgxaHh5eZGWlkZaWhoAFRUVkihCCCEk0LmsT1qrJTs7G5fLRWFhIYGBgUyaNIng\n4GClBUetVhMcHMwdd9zBxo0bcblcLFmyhKSkJLRaLTNnzqSnp4fKyko0Gg1z584dNCvTCyGEEEII\nIYHOABUaGsrChQtZuHBhv+2zZs1S/q3X6xk7duwFJxjw8vJi2bJlkoOEEGIAc7vd1NXVcfToUex2\nO9HR0QwdOvSCs+FZLBbKysrw8vIiJSUFgK6uLkpLSzl9+jR6vR6VSoWfnx+TJ0+WxBVCCAl0hBBC\niO8nyLHb7bz++uvK8gJarZaf/OQnDBkypF9X5JqaGvbv309OTg7jx49XAp2GhgbWrFnDnj17SE9P\nR61WExMTI4GOEEJIoPP9sVgs1NTU0NTUhKenJ1FRUYSGhvbbx+l00tjYSGVlJW63m4iICOLi4tBo\nNDQ0NFBeXo7L5UKlUqFWq0lJSflGa5AIIYT4fgKd+vp63n//fdavX09YWBg///nPOXz4MGFhYfj5\n+Sn7VlZWkpeXx6lTpxg/fny/4wQHB7N06VLuuusuSVQhhJBA5/vlcrkoKipi7dq1nDlzBi8vL7Kz\ns1m6dKnSXcHtdtPZ2clbb73FoUOHUKlUhIeH8/DDDxMQEMCuXbt49tlnCQgIwGQyodfrufvuuyXQ\nEUKIAVQWHD9+nJSUFPz9/dFqtYwfP56Kigo6Ozv7BTpTp04lIyODFStW4Hb3XxfNZrPR2NjIwYMH\nMZlMREdHy0KwQgghgc73o7u7m507d2I0Gvn3v//N3r17+fjjjykvLyczMxM415pTVVXF2rVr+fDD\nDzEajSxbtowDBw5w9dVXAzBhwgTuuusuEhISJCcJIcQA43a7aWlpwd/fX5k628fHh66uLhwOxwX3\n/3yQ4+HhgV6v59ChQ5SXlxMREcGiRYuYMmWKJLAQQnzPrsiFX+rq6nA6ncTGxuLt7U1UVBQREREc\nO3ZM2cdms1FZWUlqaqpS03fttdeyf/9+ent7gXODUKuqqjh9+jSdnZ04nU7JUUIIMYBotdp+wYvT\n6TwvmPkikZGRPPDAA3zyySe8+OKLpKen8/TTT190/5aWFpqammhqaqKrq0vKDSHEgGC1Wvs9v2w2\n28B4xl+JF8tsNuN2uzEajcC52dV0Oh2dnZ3KPg6Hg/b2dvz9/ZVtAQEBlJSU4HQ68fLyorKykkcf\nfRR/f38WLFjA7NmzCQkJkbtBCCEGAJVKRUREBA0NDTgcDmUGtsDAQHQ6HQ6HA7Va/YWLQbvdblwu\nF263Gx8fH9LS0njzzTcvun9OTo7SJW7YsGFkZWXh6+srF0MIcVmrrKxk//79yrtyZWUl2dnZEuhc\njj5fcPUVVJ+vWVOr1f1WAv/sPtdccw3Tp09HrVaze/dunnnmGUJDQ/tNTy2EEAOZy+XCbrcrzz2r\n1Tqozk+j0TB8+HBOnTrFqVOnCAsLIz8/nwULFgAo20wmEz09PZjNZnp6erBarVgsFgwGA42NjdTU\n1BATE0NXVxeHDh1i1KhRF/3OO+64g6ioKMlcQogBJT09nfT0dOXvwsLCAfG7r8hAx2QyoVKplJYd\nm81GT08PISEhSpcFnU5HQEAAzc3NyraGhgZCQ0PRaDRKEOR2u8nKykKj0dDd3S13ghBi0Ghra2PX\nrl0UFRUB57pdDSYqlQqTycSTTz7JPffcg9lsZs6cOUycOJHDhw/z7rvvcvfddzN+/Hg2b97MW2+9\nxYEDB9BqtbS1tXH77bdjtVpZuXIleXl5mEwmRo8ezSOPPCKZRwghJND5fkRGRqJWqykvL6erq4tT\np05x+vRppk+fTmtrK3a7neDgYJKTkzly5AjNzc34+Piwfv16Hn74YWXgaUJCAv7+/uzevRuTyURg\nYKDkKCHEoBEYGMi8efOYN28eABUVFbz99tuD6hzVajUzZsxgxowZ/bbPnDmTmTNnKn9ff/31XH/9\n9Rc8xj/+8Q/JLEIIIYHO5UGv1zN37lxee+01pk6dSnR0NMuXLycgIIBXX32Vmpoann32WaKjo3nw\nwQeZOXMmTqeT+fPnM3HiRPR6PUeOHOHBBx/EbDYTGBjIPffc84XdFYQQQgghhBAS6HyrVCoVSUlJ\nPProozz88MOo1WoMBgNarZZ77rlHWQTUaDSyZMkS5s6dC5ybRtTT0xOVSsXy5ctZvHgxbrcblUqF\np6cner1ecpQQQgghhBAS6Hx/NBoNRqNRmXmtj5eXV7+AyMPDAw8Pj/M+bzAYMBgMkoOEEEIIIYS4\nDKklCYQQQgghhBAS6AwS1dXVPPfccyxatIj777+f3Nzc8/ax2Wxs3ryZxYsXs3DhQl5++WV6enr6\n7dPY2MhPf/pT3n333X7r8AghhBBCCCEk0PlO2e12tmzZQn19PQ888ACZmZls27aNs2fPKvu4XC7q\n6up4/vnn+clPfsKvfvUrPvroI0pKSnA4HMpxXnzxRXbv3k17e7uyXQghhBBCCPH9uiLH6DQ2NtLc\n3ExUVBQTJkzAy8uL+vp6ioqKiIyMBKCnp4eKigr0ej2TJk1Co9EwbNgwdu/ezZAhQ9Bqtbz//vvo\ndDqio6MvOI5HCCGEEEII8f24Ilt02tracDqdBAYGotVq8fX1xWQyUVtbq+zT29tLfX09kZGR6PV6\nZaa206dP43A4KCgooLCwkMmTJxMTE4NGo5HcJIQQQgghhAQ63x+73Y5KpUKrPdegpVarUalUWK1W\nZR+3201vb2+/mdU8PDzo7u6mtbWVVatWkZ2dTXp6usy+JoQQQgghxGXmiuy6ZjAYUKvV9Pb2KoGP\n3W7H29tb2Uej0eDt7U13d7eyraurCy8vLxoaGjh48CDd3d0cOHCA3bt3U1lZSUBAAFOmTMHHx0dy\nlhBiwDObzZSXl1NTUwPQr9VbCCGEkEDnMhQSEoJKpaK2thabzUZjYyNNTU2MHDkSi8WC0+nEw8OD\n6OhoKioqMJvNeHh4sG/fPiZNmkRoaCh33nmnMgPbgQMHCA0Nxc/PT2klEkKIgU6j0WAymQgODgY4\nb9bJwcDtdlNSUsKmTZuw2WxkZmYydepUTCbTefu2tbWxa9cu/Pz8mDRpEnCuoqy8vJwtW7Zgs9lI\nSEhg7ty50tIvhBAS6Hw/fH19SUtLIz8/n7///e90dXURHBxMVFQUO3fupLW1lWXLlhEfH09KSgor\nVqxAq9XicrmYMGECMTExxMfHK8crLCxkxIgRpKWl9VtwVAghBjJPT0+GDBnCkCFDAKioqBh0QY7N\nZmPFihUkJibi5+fHhg0bCA8PJyMjA71e3+85v3nzZnJzc5k9e7YS6FRXV7N161aam5vx9/enqKgI\nHx8fpk+fLhlICCG+Z1fkGB2tVsu4ceO46qqrMBgMJCcnM3PmTEJDQ/H09MRkMqFSqQgMDOS2227D\naDSi0+m4+eabSUhIOG/igWuvvZaMjAw8PT0lRwkhxADhcrmoqqqisLCQm2++mbvuuovg4GCOHj16\n3rpoKpUKvV6PTqfrt728vJzy8nKWLl3KbbfdRnp6OmvXrpXEFUKIy+Gd/0o98aCgIGbMmMGMGTP6\nbZ86daryb71eT2ZmJpmZmV94rFmzZklOEkKIARjolJWVER8fj8lkQqPRkJGRwYkTJ7BYLAQFBSn7\nZmRkEBkZSVtbG263W9ne3t6OxWJRlh2IiYmhrKxMElcIISTQEUIIIb4/nZ2dSis+gJeXlzJW8/Pc\nbne/IAfA6XTicDiUbm5arRaLxSIJK4QQEugIIYQQ3x9PT0/sdrvyd09PDxqNRgl8voxGo0Gj0eB0\nOlGpVDgcji9cV624uJjW1lYA/P39CQkJkQWnhRCXvdbWVhobG5UZi9va2iTQuZyZzWZOnz5NXV0d\nRqORuLg4IiIi+u3jcDior6+npKQEt9tNdHQ0SUlJaDQa6urqqKiowGazoVKpSEhIIDIyUgosIYQY\nINRqNXFxcZw5cwabzYbBYKCyspLQ0FB0Oh1WqxW9Xv+FgYvRaMTT05O6ujr8/f1pbGwkMjLyovuX\nl5crgU58fDz+/v5SbgghLnvt7e1UVFQoLdbt7e0S6FyuXC4XR48e5cMPP6SxsREPDw/GjBnDsmXL\nlFnT3G43HR0dvPHGG5SXl6NWq/Hx8eHhhx8mKCiI4uJiNm7cSGdnJxaLheHDh7N48WISEhLkbhBC\niAES6KSkpKDX68nLy8PPz4+SkhKWLVuGxWKhrKyMYcOGERoaSkNDAyUlJdTU1NDV1UVJSQnR0dHE\nxcURFRXFhx9+SGRkJEePHj1v7OdnzZs3j6ioKEl8IcSAkpCQ0O8d95NPPhkYz/kr8WJZLBZ2796N\nt7c3K1asYNGiRZSWlvYbQOpwOKiqqmLTpk387W9/44UXXqCmpoYDBw5gt9uJj4/n0Ucf5V//+hf3\n3nsv1dXVyqJ6QgghLn8qlQqj0ch///d/s2nTJl555RXGjh3L6NGjaW1tJT8/n8bGRgDKysrYvHkz\nbW1tnDlzhm3bttHW1kZKSgpTp07l+PHj5OTkYDQaWbJkiSSuEEJcBq7IFp26ujqcTiexsbEYjUYi\nIyMJCwvjxIkTjBgxAgCbzcbJkycZOnQofn5+uFwupk2bRkFBAZMnTyY+Ph6n08nJkyc5cuQIERER\n/WboEUIIMTCCnfHjxzN+/Ph+2ydMmMCECROUvydPnszkyZMveIzs7Gyys7MlMYUQ4jJzxbbouN1u\npZta39oIHR0dyj5Op5OOjg78/PyUbf7+/rS2tiqz8dhsNv72t7/xwQcfEBMTQ1hYmOQoIcSg4XA4\n6OzspKmpiaamJmVsiRBCCDEQXJEtOmq1GrX6/4/x3G43Tqez34BTlUp13gBUp9OJy+VS/jYajbzw\nwgsUFhby3HPPER4ezrXXXiu5SggxKHR1dbF//36Ki4sBaGpqkkQRQgghgc7lzMfHB5VKRVdXFy6X\ni+7ubmw2G+Hh4Uogo9VqCQwMpLm5GZfLhdvtpq6ujrCwMDQaDb29vWi1WlQqFSkpKVgsFrq6uiRH\nCSEGDX9//34LK1dUVPDKK69IwgghhBgQrsiuaxEREeh0OioqKmhubqayspKzZ88ydOhQmpqaOHv2\nLJ6eniQnJ3PixAlqa2vp6upi06ZNjB8/Hg8PD7Zv305NTQ3d3d3k5+fj7e2Nv7+/5CghhBBCCCEu\nA1dki46Hhwdz587lpZdeYurUqcTFxXHHHXcQEBDAq6++ypkzZ/jnP/9JdHQ0Dz74IDNmzMDpdLJs\n2TImTpyIXq+npKSEhx9+mM7OTkJCQvjFL37BmDFjJEcJIYQQQgghgc73JzExkT/+8Y88/vjjqFQq\ntFotarWahx56CLfbDZxbMXvRokXMnz//XGJptWi155Ls7rvv5s4778Ttdiuf/6JF5YQQQgghhBAS\n6Hzr1Go1er3+gtv79AUwfcHNZ+l0OnQ6neQgIYQQQgghLsf3fUkCIYQQQgghxGBzxbboVFVV8cEH\nH5Cbm0tkZCSLFy8+b2poq9VKbm4uL7zwAi6Xi+uvv54f/ehHeHh48MEHH7Bq1SpljM6Pf/xjxo4d\ne8FWIiGEEEIIIYQEOt86u93O5s2baWlp4fe//z3Hjx8nNzeXoUOHEhUVBYDL5aKuro7nn3+eRx55\nBG9vbx599FHGjRtHeno6QUFB/PznP8doNJKbm0teXh4BAQGkpqZKrhJCCCGEEEICne9eQ0MDbW1t\nxMTEMHLkSDQaDbW1tRw9elQJdHp6eigvL8dkMpGVlYVarWbUqFHs2rWL5ORkRo8ejcFgQKvV0tTU\nxEcffURHR4fkKCGEGEDcbjf79u3jlVdeobu7m8mTJ7NkyRICAgL67VdZWcmaNWs4evQoCQkJzJ8/\nn9GjR1NTU8Pq1avZunUrJpMJtVpNQkICjz/+uCSuEEJIoPPda2trw+l04u/vj1arxcfHB6PRSH19\nvbJPb28vDQ0NhIeHo9PpcLlcJCQkUFRUhMPhwNfXV9m3uLgYk8nUb5sQQojLP8jp7u7miSee4L/+\n67/w9/fnjTfeIC0tjaysLAwGg1Ie5OTk4HA4uPXWWzl8+DD5+fnExMRgs9mwWCykpKQwb948VCoV\n3t7eF/3ONWvWnBdEXUxJSRmHDx/B6XQNiPQ8dKgAUPHMM8/x/vtrLu3LilbFVVddRVhY6Df6vNls\nZsOGj3E4HJLxAbVaRWJiPOPGZX3jY+Tl7aC2tg6Xyy0JCuj1GhYuXPCNZuBNTk4mKytLElECnUvD\n6XQqM6qdu+HVqFQqbDZbvwLQbrcrBd25TKynu7tbmX4aYO/evRQWFjJ79myio6MlRwkhxADhcrko\nLS3FarUydepU/P39OXDgAEVFRaSkpCjP/9raWpqbmxk1ahRXX301KpWKAwcOUFZWRmhoKJ6engQE\nBDB58uQv/c4VK1ZccCbPC2lra6OxsalfmXOZh44AHDtWyLFjhZf0yCqViqKiQry8vL7R5+12OydP\nnsbtdknG/3/8/HzZsuXTb/z5mpqzmM0W5bpL8KimrKwUlUr1tT+7bNkyCXQk0Ll0DAYDarWa3t5e\n5QFot9sxmUzKPhqNBm9vb8xms7Kts7NT6ZoAcPjwYVatWsXYsWPJysrCaDRKjhJCDBpdXV2UlpZS\nXV0NQF1d3aALdE6dOkVUVBSenp5Kt7MjR45gtVqV/RobG9Hr9UovgKCgILRaLQ0NDYSGhtLc3ExB\nQQFVVVUMGTKE66+/npCQkAt+Z0VFxRWQcy79i6/b7Vbyobg02tvbaW9vl4S4ZM8TJ6Wlpd/os5/t\nUSQucQB6JZ50SEgIKpWKmpoarFYrDQ0NNDY2EhcXh8VioaOjAw8PD2JjYykvL6erqwun08mePXtI\nS0tDp9NRUFDAO++8Q3JyMjNmzFCOKYQQg4VOpyMwMJDY2FhiY2OJjIwcdOdotVr7tRJ4eHhgtVpx\nOp3Ktt7eXtRqtdISo9Vqcbvd9PT0EBQUxKxZs1i+fDnJyclUV1ezcuVKyTxCCHEZuCJbdHx9fcnI\nyGD79u385S9/wWq1Eh4eTlRUFLm5ubS0tPDDH/6QuLg4MjMzefrpp5WFQ7Ozs9Hr9f8fe3ceHNV1\nJ3r827vU2lv7vu8SYAFiN2bHrDYJIRjHsZNJnDJJZZnJJJmpmvFLZiaLK6lxnNiMndiOFzAxmEXs\ni0AIAUII7VJr37fW3t1q9d7vDz/dZwXiOHnxQ5jzqXIVbnVL99x7+p77O+d3zuGdd97h2rVrTE1N\nMTw8jFwuZ/v27WRlZYlaJQjCZ4KXlxeJiYkkJiZK987PGn9//xkpyZOTk3h5ec3Is/f29pbSmacD\nH7fbjUajISAggKVLl7J8+XIsFgvnz5/npZde4p//+Z9FBRIE4TOjp6eH1tZWabS7t7eXvLw8EejM\nRgqFgvz8fNRqNc3NzQQGBjJ37lzCw8MZGhpCqVQik8kIDg7mmWee4erVq3g8Hh555BHi4+Olz6en\np0vzewCxh44gCMJ9RC6Xk5qaSmtrK2azGY1GQ01NDdHR0ahUKoxGI97e3kRFRWG1WjEYDNhsNnp6\nerBarURHR2M2m7FYLISEhOB2u3G73R+7GIEgCML9SKlUotVqpekbn3SuoQh07hGdTsfq1atZvXr1\njNeXLl0q/VulUpGVlXXXUZo9e/aIWi8IgnCfBzrTI/dvv/02Xl5eDA0NsWXLFgwGAxcvXmTFihXE\nxMSQmZlJU1MT+/btY3BwkISEBFJSUhgaGuLy5cuYzWbcbjejo6N8/vOfFydXEITPlIiICCIiIqT/\nf+ONN+6P+7y4dIIgCMKDSCaT4eXlxaAM53EAACAASURBVN69e1EoFJhMJrZs2UJ2djYajUbquVSp\nVKxfv57c3FympqZIT0/nkUceITg4GG9vb3x9fTGZTDidTnJycti6das4uYIgCLOA8kEuvNFopLGx\nkY6ODnx8fMjIyCApKWnGe1wuF11dXdy6dQu3201KSgpz5sxBpVIB4HQ6KS0tJSQkhISEBDQajahV\ngiAI91Gwk5mZSWZm5ozX/3Q0Pyoqip07d97x+cjISL7whS+IEykIgiACndnD7XZTVVXFqVOnsNls\neDweWltb+fKXv4y/vz/w4XKWJpOJN998k4mJCZRKJVevXuWf/umfiImJwWg0cu3aNX7zm9+wY8cO\nQkNDRaAjCIIgCIIgCLPAA5u6ZjabqaioQK1W85Of/ISdO3fS2dmJXq+X3jM9mnP+/Hn+1//6X/z8\n5z/HYrFw69YtrFYrExMTlJaW0tPTI3ZbFgRBEARBEAQR6Nx7BoMBu91OXFwcPj4+hIeHExMTQ11d\nnfQeu91OS0sL6enpBAQEoFAoWLZsGTU1NUxNTZGQkMCPf/xj1q9fL1bZEQRBEARBEAQR6Nx70xvC\nTW8Up1KppOVEp7lcLiYmJmbsHeHv78/ExMSMzeQEQRAEQRAEQZhdHtg5OgqFArlcLm0S53a7cblc\n0p448OEkVZVKJb0HkFLUPvo+QRCEzyKn04nJZJI2iBscHBQnRRAEQRCBzmzn5+eHUqlkbGwMl8vF\n5OQkJpOJ5ORkXC4XbrcbpVJJeHg4/f39OJ1OFAoF3d3dREZG3rFR0keDIUEQhM8Cs9lMeXk5jY2N\nAAwNDYmTIgiCINw3HtjUtfDwcPz9/eno6KC5uRm9Xs/AwABZWVn09vbS3NyMWq0mIyODvr4+Ghoa\n6OrqoqSkhPnz56PVarHb7RgMBiYnJxkfH2d0dBSbzSZqlSAInwmBgYGsXbuWvXv3snfvXp588klx\nUgRBEIT7xgM7oqNWq1mzZg0Gg4F/+Id/ICoqiieeeILw8HAOHTpEU1MTL774ImFhYfzwhz9k7969\nOBwOHnvsMRYtWoSXlxd6vZ5f/vKXXL9+nfLycvR6PV/72td46KGHRM0SBEEQBEEQBBHo3BsJCQn8\n6Ec/4kc/+tGM1/fu3Sv9W6PRsGXLFrZs2XLH5zMyMnjttddELRIEQRAEQRCEWUYuToEgCIIgCIIg\nCCLQEQRBEARBEARBEIHO7NTW1saPf/xjli1bxu7duzl58uQd77FYLBw5coQVK1awdOlSfvWrX0mL\nDUxMTPC9732Phx9+mG3btlFUVITdbhc1ShAE4T7idru5ePEi69atY8mSJfzkJz+56+pydXV1/OM/\n/iPLli3jq1/9KleuXAHAZrNx/vx5tm7dysqVK/nBD36AyWQSJ1YQBEEEOveG3W7n9OnT2Gw23njj\nDbZv305RURGdnZ0zGr++vj5efPFFXnnlFQ4cOMCFCxeorq7G4XDw8ssvExkZyW9/+1t2797NlStX\npCVYBUEQhNnP4/EwOTnJj370I/7jP/6DgwcP0tTUREVFBRaLRXqfzWbjwIEDREdH84c//IH58+dz\n9epV+vr6qKqqoqioiC996Uv8+te/JjQ0lD/84Q/i5AqCIIhA597o7+/HbDaTkJBAcnIymZmZ6HQ6\nKisrpfdYrVaampqIiooiIyODmJgYVqxYwdWrV7HZbBQXF7No0SLS0tJYu3YtPT099Pb2iholCIJw\nn3C5XFRXVxMeHk5GRgaxsbEsXbqU2tpaxsfHpfd1dnbidrtJTEwkKSmJ3NxclEoler2e3t5eRkZG\nWLNmDRkZGeTl5XH58mVxcgVBEESgc29MTEzgdrsJCAhAoVDg6+uLVqvFYDBI73E4HBgMBiIiIlAq\nlchkMuLi4ujt7cXlcmEwGAgICECtVqPT6ZicnJR2DxcEQRBmP4/HQ3d3NxEREahUKmQyGZGRkQwP\nD8/YE21kZASlUomfnx9yuZyAgADkcjmjo6NMTU0xNTVFUFAQarUaf3//GW2JIAiCcO88kMtLu91u\nZDIZSuWHxZfJZHg8nhlzbDweDy6XC7VaLb2mUCiwWq14PB48Hg8ymQyZTIZCocDpdOJyuUSNEgRB\nuI84nc4Z93mlUondbsftdkuvuVwuFAoFCoVCajPcbjcOhwOPxwOAXC6XfiY2jhYEQRCBzj3j7e2N\nXC7HarUCH87ZcTqdBAYGzghq/P39Z0wqnZiYwN/fH7lcjre3N06nE7fbjd1uR6lUSoHTR/n4+ODv\n74/RaBS1TRCEGfbs2YNGo7lvjtdutxMeHv6ZOf8ymYygoCBMJpMUsBiNRrRa7Yz7ua+vLy6XS+oM\ns9lseDwevL29sdvtqFQq7HY7CoUCm82GVqu969+LiIjAZDLNCKIEQRBOnDhBW1vbfXXM3d3drFix\nQgQ6s1F4eDgymYyuri4sFgv9/f0MDAyQn5+PyWTC5XKh1WpJSEhAr9djNBrx8vKiuLiYLVu2oFKp\nyMrKoqGhgaSkJBobGwkMDESn093xtx599FFiY2PFimyCIHxmAoPPCrlcTlZWFnq9nvHxcTQaDWVl\nZeTk5KBUKhkdHcXHx4eYmBimpqbo6+tjamqKjo4OLBYL8fHxjIyM4OPjQ21tLfHx8TQ2NpKVlXXX\nv/fb3/4Wo9EoBVWCIAj3K41GQ25u7uxvtzwP4B3X5XJRVFTEuXPnpJ651NRUNmzYQGVlJUNDQ3zj\nG99geHiYl156CaPRKKWt/eAHPyA6Oprr169z7NgxnE4nVquVxYsXs2HDBiIiIkTtFwRBuA9Mpyz/\n/Oc/Z2xsDLlcjs1m4+tf/zqTk5OUlJSwefNm0tLSOH78ODdu3MDhcOByuVi4cCHbt2/HaDRy5swZ\nysvL0Wg0qNVqHnvsMZYsWSJOsCAIggh07o2JiQn0ej2dnZ34+fmRkZFBaGgozc3NTE5Osnz5cpxO\nJx0dHVRUVODxeEhOTmbu3LkolUpsNhulpaUYDAY0Gg0PPfQQkZGRUg63IAiCcH8EO93d3dy6dQuH\nw0FCQgK5ubkMDw/T3NxMdnY2ERERjIyMUFdXx8DAAEFBQWRmZhITE4PL5aKnp4eqqirsdjsREREs\nXLjwvkpJFARBEIGO8FcZHBzk+vXr6PV6QkJCWLJkCdnZ2fdVGYqKiigtLZVyz0NDQ1m4cCEqlYoL\nFy5gtVpJTk5m8+bNeHl5zbrjHxkZ4dKlSyQlJZGXl4fdbqeqqoqSkhJcLhfZ2dmsWrUKjUbD+Pg4\nRUVF1NXVodVqefTRR0lPT581ZRkdHeXKlStERkayaNEi+vv7KSoqor6+XppzlpSUxM6dOzGZTFy+\nfJmamho0Gg2bN28mPT0dmUx2z8vhcrm4evUqZWVl2Gw2IiMjWbp0KcnJyTQ3N9+1XtlsNgoLC6mo\nqEChUPDoo4+SmZmJSqWaFeW5desWJSUlWCwWwsLCWLx4MbGxsVy7do3r16/j4+ODTCYjMDCQb3zj\nG7jdbgoLCykvLwdgzZo15OTkzIrvUF1dHVeuXGFsbAwvLy9ycnJYuXIlAwMDnDhxApPJRGRkJDt3\n7pTmoVy6dIlbt27hcrlYtWoVubm5f3aOyoPI5XKh1+s5deoUDoeDefPmsXLlSnx8fO6bMkxOTvL6\n668zNjYmrUI6Z84cFi1axMWLF+ns7MTb25v169eTnJx81/mq95LD4aClpYXCwkL27t2Lx+PBYrFw\n8OBBBgcHCQgIYNu2bVJnZUVFBaWlpYyPj0vlDA4OnhVlcTqdtLe3c/LkSb7zne8AUF5ezokTJ1Aq\nlcjlclQqFTt37iQ+Pp7q6mpu3LjB6OgoWVlZLFmyhNDQ0FnRwWAymXj77bcZGxtDJpMxf/58Vq1a\nhcvloqCg4K71Sq/Xc+3aNQYHB0lNTWX58uWzIpPG4/HgcDh4/fXXGRkZAWDu3LmsXLmS7u5uzpw5\ng9lsRqVSoVQqWbt2LQ899BCtra1cu3aNnp4e4uPjWblyJdHR0bOirh08eJCOjg6cTiehoaFs3ryZ\noKAgCgsL0ev1qFQqHn74YXJyclCpVHR0dFBSUkJXVxcxMTE88sgjxMbG3vNyPJDLS///uBEVFxdT\nVlaGj48PPT09XLx4kdHR0fuqHJWVldTX10tLaAcEBDAwMMDFixcZHh5GqVRSXV0t7RA+m9y6dYt9\n+/bx6quvShu56vV6iouLMRqNuFwubt++TVlZGVNTU9TU1HDmzBmCgoJwOBy89tpruFyuWZFLX1FR\nwauvvsq+ffuor6+Xgriqqip6enrQ6XTodDr8/PywWq3U1dVRUFBAUFAQHo+Hl19+ecbqUPeS2+3G\nbDYjl8sJCgqSgpuampo76lVxcTEOh4Ompibef/99dDodarWa3/3ud5jN5llRnukNJ91uNzqdjp6e\nHk6fPk13dzc1NTXo9Xrp+gQGBuJ2u2lvb2f//v34+PigVqt55513GBoamhXlmZqawul0otPpMJvN\nXLp0iWvXrnHmzBl6e3vx8fGhoaGBc+fO4fF46Ojo4L333kOj0eDl5cXBgwfp6+sTc1D+pH689NJL\nKBQKgoKCOHr0KK2trTgcjvumHDabjaNHj2K326X67Ovry8WLF6mpqUGtVmMwGCgqKqK/v39WHfv4\n+DgffPABv/zlLzl8+LB0XY4dO4Zer8fPz4+uri4uXrzI2NgYAwMDnD59muHhYbRaLVevXuXWrVuz\nYgEJk8nE0aNHeeGFFzh48KD0emNjIzdv3kSj0aDT6QgKCkKlUjE4OMjZs2cZGBhAq9Vy8+ZNbty4\nMWsWw3A6nYyPjxMcHExAQADvvvsu7e3tnDlz5q71amhoiIsXL9LR0YGvry81NTVcvnx51pTH5XIx\nNjZGYGAggYGBHD58mMbGRpqamrh165Z0b9XpdHh5eTE6OkpxcTH19fX4+fnR3NzMmTNnZk15RkdH\nCQgIIDg4mNLSUm7evMmZM2e4ffs2Ho+H8fFxSkpKaGtrk/5dXV2Nn58fHR0dnDhxYlaU5YFcjOD/\nR+XQ6/VERkbyta99jWvXrlFUVERjY+N9l7edk5PDE088QVRUFABnzpyhubmZb3zjG0RFRXHu3DmO\nHj3K+vXrZ9Vxy2QyvLy8ZqQSVlZWMjY2xtNPP42vry/Hjx/nwoULJCcnU1VVRWRkJF//+tfp7e3l\nK1/5Cj09PcTFxc2Ksmg0GpRK5YwHSJ1OR3Z2Nk8++aT02tDQELdv3yYkJIRnn32WoaEhdu7cSXd3\nN4mJifd8VEcul5Ofn8+yZcvw9/fn4MGDVFVVce3atRn16uzZsxw9epTFixdTWlqKn58fzz77LGaz\nmc9//vN0dnbi6+t7z0d15HI5c+fOZe7cuQQFBXHmzBlOnz7NwMAAPj4+LFmyhGeffVZ6v91up7i4\nGLVazTPPPIPH4+GZZ56ho6ODsLCwe57ulJKSQkJCAjqdjuLiYg4fPoxer+f69et873vfIz09naKi\nIn7/+9+zbds2iouLUSgUfOlLX0KtVvPss8/S3t5OdHQ03t7eD3xb4HK5aG9vp6amhp/97Gf4+fnx\nL//yL9y+fZu4uLgZK33OdiqVih07djBv3jxpKe2nnnqKbdu2sX79erq6uvjd735Henr6rOjF/SiF\nQoG3t7d0//R4PLz55pv84Ac/YPny5dTW1vKb3/yG/Px8uru7MRqN7Nixgzlz5vDiiy/S1NTEokWL\nZsX1ksvldx0xTU9P56mnniIsLEx6rbCwkLGxMTZt2sSCBQv4n//5H5qamli+fPmsWFTEx8eHPXv2\nEB8fj8vl4uzZs3R0dHDgwAF27dp1R71SKpUYDAaWLVvGI488woEDB9Dr9YyOjhISEnLvH6iVSr74\nxS8SFxeHx+Ph2rVrdHZ24nQ6SUxMZOfOnTMm75eXl9PT00NeXh7bt2/nxIkTlJSUMDw8POM63iub\nNm0iOjoaj8eDXq9nbGyMK1eusGLFCrZt28b4+Dhvv/02NTU1WK1WOjs7mTNnDl/4whc4d+4cp0+f\nZmho6J6v1CkCnU/B4OAg8OFSohqNhvDwcPz9/eno6LjvAp2GhgYOHjxIfHw8OTk5jI2NMTk5SUpK\nCgqFgri4OJqammbdcc+fP5+4uDh6enqkxm1oaAiAuLg4ZDIZwcHBlJSUYDabGRgYIC8vT1pWPCYm\nhqamJmJjY+95cDBv3jxiYmKkejVtbGyM5uZmPB4PsbGx5OXlYbFY6O3tZe7cucjlcvz8/IiLi6O5\nuZm4uDjpAeVePnBMp03Y7XbsdjsymQyr1XrXemW322lubiYvLw+ZTCaVp6Ojg7S0tFkR6EyntDgc\nDmw2GzKZDK1Wi8VioaKigj/84Q+EhoayZMkSvL29qa2tZf78+SiVSjQaDbGxsdJqXvc60AkMDGRk\nZIQLFy5QXFxMaGgoYWFhGAwGsrKyUCqVZGdnU1tbi8fjoba2lrlz56JWq6XVyQYHB7FYLCLQ4cMe\n69bWVpKTk/H19UWhUDB37lwaGxuxWCz3VaDjdDo5fvw4VVVVxMfHs2TJEpqbm0lKSsLX15e0tDTG\nxsYYHx+fVccdGBjItm3biImJ4Yc//KEU6NTX15Obm4tarSY7O5uenh4mJydpb28nIiKC4OBgvLy8\nCA8PZ2RkhOHh4Xt+vfz8/Ni6dSuJiYncuHFjxs9aWlo4cOAAkZGRLFy4kJiYGDo6OggJCSE0NFQq\nS1dXFwaD4Z4HOtMdeAkJCXg8Hmw2GzabDR8fH9ra2qR6lZqaKtUrq9WKr68vkZGRaDQaQkND6erq\nYmBg4J4HOjKZDJVKRWJiIgAWiwW73Y63tzcWi4Wuri6OHj1KXV0dubm5JCcn09/fL7V3arWakJAQ\nvLy86O3tnRWBTnx8PHq9nsuXL2M2m0lNTeXIkSOEh4dLWT4Oh4OhoSECAgIASEhIQKVSERwcjI+P\nD93d3SLQ+SyyWq3Sl3i6J0wulzM5OXlflSM7O5vx8XGGhobo6uqSGoKPbrCnUCjum3I5HA7cbrf0\ncCyTyZiamsLlcmG1WqVesukH1fHx8VmbghMUFERGRgY1NTXU1dXR0NAg5ZNPTU1J+f8ymQxfX18m\nJiZmVVncbjf19fW0tbURExODVqudUa+USiUWiwW3283k5CS+vr4zegGnl4GfTeVpbW2lvr6e6Oho\nUlJSGBoawmAw0NjYSE1NDQMDA+zevRuTyYSvr68UQGu1Wul7NRvY7XYMBgPj4+PEx8czNTWFw+GQ\nro1arcZsNgMf7jnj4+MjBdBarVZKfxM+fKA2m834+flJr2m1Wsxm8321wbRGo2HdunWMj4/T3NxM\naWkpGo0Go9EozQtRq9XSnnT3g6mpKdRqtTT6b7VacblcTE5OolarpXZCo9FIbcRslZSURHZ2NgaD\ngY6ODrq6uti1axcWiwWlUimVRa1W43a7Z1VZPB4PTqeTY8eOERgYSHJyMi6XS6pXGo1GqldTU1NS\nQDH9bDUdVMwmLpeL06dP4+XlRVJSElarlaysLIxGI2VlZbS1tbFlyxbpOkw/K06Xefr+OhuYTCY6\nOzvx8vLC5XIxNTWFXC6XNlB2Op3Y7XZsNhtut1uaazq9t+RsKIsIdD4FKpUKmUwmNWROp1P64t5P\nVq1axapVq4APJxxfuHBBmhzocrmkMt4vK81N30Smr4vL5UIul0uN9HQDPd27NN0IzkaRkZHs2bMH\nhULByMgIf/zjHzl06BDz5s1DrVbPyP+3Wq2zYuL+R4OC5uZmzp49i1arZcOGDdy6dQuFQjGjXsnl\ncqnD4KPlsdls0oTo2VKejo4Ozp49i9Pp5HOf+xxhYWFs27aNxx9/HIvFwvnz5/npT3/KE088gZeX\n14zyTC/2MVvKM123cnJy+OCDD+jq6kKpVOJ0OlEoFDgcDuk7P71x8nQQbbfbpesm/N8U2o/uo2az\n2WbV9f4ktFot3/ve91AqlTgcDp5//nnOnz+P3W7H7Xbj8Xik++r9Uq7pAGb6QVsmk0n3G7fbLc0t\nmG4XZnM7t2DBAhYtWoRMJqOmpobvf//7LFmyBLVazdTU1IyyeDyeWVOW6eXdL126xMmTJ3n22Wel\nkbS71avpNnm6PC6XC7fbPavK43a7KSoq4uTJk+zatYuYmBi8vb2ZM2cOcrmc5uZmfvrTn1JZWUlA\nQMCMZ0WXyzXrnhUXLlzI/Pnz+fWvf82NGzekTtPp74jH40Emk0nPV9PfF7fbjdPpnBVlEYsRfAqC\ng4PxeDyMjIzgcDikdK/IyMj7pgxutxuLxYLNZpNSoHx9ffH29sbb25v+/n4sFguDg4OzZoWQv2T6\npjI8PIzRaMRkMhEREYFWqyUkJIS2tjap3H19fdIQ9Gxks9mkEQ9vb290Op3UmxIeHk5raytut5up\nqSlpNZfZ0BhMBzkFBQW43W42bdpESkoKPj4+d9SrmJgYVCqVlMbm8XiwWq309PQQHR09K4K36SDn\n1KlTjI2NsWXLFjIyMnA6ndKok1KpJDw8XAreUlJSaGxslHqJp9MU7vWqay6XC6PRiNFolB4ENRoN\nTqeToKAgenp6sNvtdHZ2Eh8fj0wmIyUlhZaWFiltr6+vj+DgYJG29n8oFApiY2Pp6uqSejxbWlqI\nioqalStV/rmHN5fLxcTEhNRhFBYWhsfjISoqCoPBIH0vfX19Z4y+zmZxcXHS/InpFC+NRkN0dDRj\nY2NMTExgt9sZGhpCqVTO6o1yJyYmpCAmLCwMlUolXR+j0cj4+DgOh4ORkRFkMtldNze/F/XKarVS\nVFTEwYMH2bFjBytWrECj0fzZehUeHo7VapWerUZHR3G5XLNmFTmn08nVq1c5cOAA69at45FHHsHX\n1xeTyYTdbsfj8aDT6aT5YiEhIXg8HgYHB6WFGaxW6z1fRW46ADUYDFIQFhAQgNVqRafTMTExgdls\nZnBwEKVSSWBgIDqdDrlczsDAgFSW2fLcK0Z0PgURERGEhYXR1dXFpUuX0Ov1AGRmZt43ZXC5XFRU\nVDAyMkJQUBDV1dXIZDI2btxIa2srBQUFREdHU11dzYYNG2bd8ff19dHQ0EB/fz/Nzc20tLSQmJjI\n0NAQJ0+exNfXl97eXpYtW0ZgYCAZGRkcO3aMoqIiBgcH0Wq1pKWlzYreyYGBARoaGujt7UWhUNDU\n1ITVaqW7uxtvb29MJhOtra0sX74cf39/cnJyOHDgAFeuXGF4eBgvLy/S09Pv+fwc+DDV6cCBA9TW\n1kqpMOXl5Xh5eRETEzOjXq1fvx4vLy8WLlzISy+9xJUrV5iYmEChUJCamjorAh2z2czRo0cpKipi\n9erVWCwWysrKUCgU9PX1SaOflZWVbNq0CYVCwYoVK/iv//oviouLsdlsuFwukpKS7nlwYLfbqa2t\npauri4iICFpbWzGZTKxdu5bGxkaOHj1KZmYmpaWlPP7448hkMlasWMHPfvYzSkpKcLvd2O12EhMT\n76ulkz/VBlaplOaSXbx4ET8/P/R6PV/96lfvq3NkNps5duwYKSkpuN1uqqur2bFjB0FBQdL+Q9Pz\nKmbbQgRWq5Xm5mZqamowGo2UlpaSkpLCjh07OH36NKOjo1RUVJCfn09ISAh+fn7cvHmTmzdv0t/f\nz8DAAPPmzZsVk91tNhstLS1UV1djNpu5fv06aWlp3LhxA5VKhZeXF/X19WRnZxMWFkZMTAxlZWWU\nlZUxPDxMd3c3aWlps2L+x/RCHf/2b//G4sWLCQkJobS0lNDQUFavXn3XeuXn58etW7e4desWVquV\ntrY2QkNDZ8XD9HTA8sMf/pC8vDyCg4OpqKggODiYnp4eqVOyo6MDnU5HcnIySUlJVFRUUFFRgUql\nklbpnA2dx5OTkxw/fpzExETkcjkVFRUsXryY0NBQOjo6OH/+PKOjo/j7+5Oenk58fDxBQUFUVlai\n1Wqpq6tDp9PNivuB4vnnn39eNEd//168kJAQenp6OHXqlBQgfHS1jfuhF6+0tJRTp05RWlqKXC5n\n7dq1rF27FrVaTWFhITU1NcTGxvLUU09J+fuzRWlpKefOnWNwcJDx8XHUajXz589HpVJRWFhIc3Mz\nWVlZPPbYY/j6+hIYGMjQ0BAFBQX09fXxrW99S+q1vtfKyso4e/Ys/f390oO+RqOhrKyMM2fO0N7e\nTkJCArt378bf35+goCDGx8c5evQoHR0dfOc73yEhIWFWBDoGg4Hy8nIGBgYYGBigvLyczs5OkpOT\nSU1NnVGvvvSlL0mjbZOTkxw+fJiGhgaee+450tLSZsWQ+PDwMJWVlXR3dzMyMkJ5ebk0mjYwMMDx\n48dpaGjAz8+P5557Dj8/P8LCwrDZbBw/fpy6ujqeeuopcnJy7vlCBG63m4aGBk6dOsW1a9cwm82s\nXr2azZs3ExISwsWLFykrKyMwMJDnnntOGj202+2cPHmSmpoadu3axUMPPXTfjFZ82qbTbRITE9m/\nfz9lZWU8/PDDrFu37r4Z+Zh+6HnvvfcoKiqiqqqKBQsW8Pjjj/PQQw9RUVFBUVERJpOJxx9/nKys\nrFmV5jU2NsaJEye4du0acrlcethfvXo1RUVFFBcX43Q6eeqpp4iNjSU4OBiZTMaNGze4fv06eXl5\nrFu3blYsHGEymThx4gTFxcXI5XI6OztJSUmhr6+PDz74gFu3bmE2m3niiSdIS0sjJCQEuVzOzZs3\nuXbtGtnZ2WzcuHFWjE45HA6am5u5ceMGCoWC27dvU15ejt1u57HHHqO+vv6OejW9bHZVVRVXrlwh\nLi6O7du3z4o9jqbnaV69ehW5XE5NTQ3l5eXS9gOFhYXSimobNmwgPz8fnU6HRqOhoaGBS5cuodPp\n2Llz56wIRKfnTV26dInbt2+TlpbG9u3byc/Pp6WlhcuXLzM0NMTatWtZtGgRAQEBeHt709jYSGFh\nIT4+PuzevfueL0QAYsNQQRAEQRAEQRA+g8QcHUEQBEEQBEEQRKAjCIIgCIIgCIIgAh1BEARBEARB\nEAQR6AiCIAiCIAiCIIhARxAERyzDiQAAIABJREFUQRAEQRAEQQQ6giAIgiAIgiCIQEcQBEEQBEEQ\nBEEEOoIgCIIgCIIgCCLQEQRBEARBEARBEIGOIAiCIAiCIAiCCHQEQRAEQRAEQXiAKO+Hg3Q4HLS3\nt1NUVIRWq+Vzn/scTU1NmEwmli1bJq6iIAjCA2JycpLCwkLa29vZtGkTGo2Gnp4eYmNjiYmJESdI\nEARBkCief/7552f7QTY0NPDHP/6R3t5e6urq2LZtG4ODg+zbt4/t27eLq/gAsdvtFBcX8/LLL/PG\nG29w/Phxbt68id1uJzIyErVa/Xf7W263m0OHDnHx4kWys7Pp7Ozk+eefJzk5mZCQEF5++WW6u7sJ\nDw/H29ubgYEB3nrrLRoaGpg7d+6neh6uXr3KyZMn0Wq1hIWF/dWfP3DgABUVFYSHh+Pr6zvrrnN3\ndzevvPIKMpmMuLi4O37e2NjIv/7rvzJnzhwCAwNpaWnhtdde49VXX6WpqQm9Xk9lZSVKpZLw8HDx\nxfkM+f3vf09HRwdFRUXMmTOHqKgojh49isfjIS0tTZygB0hrayuHDx/mtdde47333uPixYt0dnb+\nzffFj1NXV8eJEycYHx8nMDCQgoICCgoKWL58Oe3t7bzwwgvExMQQEhKCy+Wirq6Ob3/72zz66KN/\n13bpbv7zP/8Ts9lMdHQ0KpXqr/psc3Mzhw4dYmJigqSkpFl5nd99911qa2sJDg7Gz89vxs9GR0f5\n4IMPuHDhAkuWLMFkMnH58mVeeOEFjhw5gtls5sqVK7S1tZGZmYlcLhKZHjT3xRUfGBigt7eX/Px8\nTCYTKpWKgIAAWlpaxBV8gFgsFk6ePMlvfvMbwsLC2LBhA2vWrMHHx4fjx48zOjr6d/+ber2e8vJy\nHA4HQUFBrFu3Dp1OB8Dt27dpbGzEarUC4OPjQ15eHjk5OZ/6uejt7aWyspKRkZG/ufOgtraWycnJ\nWXmtTSYTpaWl9Pb23vXnOp2OjRs34u/vD8CRI0fo6Ohg4cKFLF68GJPJRFVVFUNDQ+KL8xlTUlJC\nbm4u3t7eOBwOAgMDGR8fZ2xsTJycB0htbS1vvPEGdXV1zJkzh/Xr15OZmUlHRwfFxcV/9783PDxM\ndXU1nZ2deHl5kZGRweLFiwGYmJigqKhIuh/LZDJCQ0PZvn37Xx14/C2uX79OW1sbDofjr/7s2NgY\nlZWVdHZ2ztprXVdXR0NDA2az+Y6feXl5kZ2dTX5+PgA9PT28//77BAQEsGnTJhITEykvL6ehoQGP\nxyO+OA+g+yJ1zePx4OXlRUREBHK5HIfDwdDQEF5eXuIKPiDcbjcDAwPs27ePlStXsnv3bsLCwvB4\nPPT29qLX6wkICMDlclFcXMy1a9cYGxsjMTGRtWvXkpKSwtjYGIWFhXR2dhIQEEBzczM6nY7169eT\nl5eH2+1mbGyMY8eOUVNTg0qlorGxUepBMhqNNDY2MnfuXM6dO0dDQwM1NTU0NDSwYMECFixYQE9P\njzRCMjU1RU1NDefPn2d0dJSEhAQ2btxIamoqFouFgwcP4nA4cDgctLS0EBAQwBe/+EWSk5NRqVQc\nOnSI27dvYzab8fHxYfHixSxdupTQ0NCPPVdOp5P29nZeeuklVq1axa1bt7BaraxYsYKNGzdK35vx\n8XEOHTqE0WgEYO3ateTn5+Pj48P169c5d+4co6OjqFQqMjMzWbt2LfHx8ZhMJq5fv05RUREmk4nQ\n0FA2bNjAggULkMlkXL58mZKSEkZHR4mPj2fr1q3Ex8ejUCju+F43Nzdz4cIFWltbUSqV5OTksGHD\nBuDDlNWGhgZ+8Ytf0NvbS0xMDF/5ylfQ6XQYjUb0ej2LFy/m1KlTHDlyhKmpKSYmJlAqldTW1lJW\nVkZ7ezvnzp1jzZo1rFu3TnyRPgM8Hg8RERFotVpkMhljY2M4nc476pfw2WW32ykoKMBgMPDYY4+x\ndOlSfHx8MBqNtLe34/F4cLvdDA4OcvbsWerr61Gr1eTn57N582bkcjnNzc28+eabzJ07l4aGBkwm\nE0uWLGH16tXodDocDgc3btyguLiY0dFRRkdHMZlMzJs3D7vdTn9/PwMDA/T393P8+HHa2tp44YUX\niIuLk9qc2tpaHnvsMSlQKigooKGhAbVazcMPP0x+fj6BgYHcuHGD2tpaHA4HIyMj0t/ZsGEDOp2O\nrq4u9u/fz8DAAB6Ph6ioKLZu3UpWVtYn6jQ6cOAANpsNuVxOV1cXkZGRrFmzhtzcXOl81tXV8ctf\n/pLe3l5iY2PZvXs3ERERuN1ufv3rX9Pd3Y3dbken07Fu3Try8vLQarU0NjZSWFhIS0sLSqWSuXPn\n8vDDDxMdHU13dzeXLl2ivr4elUpFfn4+69evv+PZbToAKSgooLS0lMnJSUJCQti0aRNZWVnIZDKG\nh4f54IMPMJlMyOVyVq5cybJly7Db7fT29jIxMUF3dzf79+/n7NmzZGRk4HQ6WbhwIdevX6empoaB\ngQGSkpLYu3fvrMxkED4d98WITmxsLNHR0Rw+fJj29nZeeeUV3n77bbZu3Squ4APUsDU1NdHR0cGX\nv/xlaYherVaTmJjIhg0b8Pf3p7S0VHrojYyMpK6ujoKCAjo6OjCZTFy9epUTJ05gMpkIDw+nra2N\nt956i6mpKYxGI0eOHKGwsJDY2Fji4uJwuVzSMQwNDVFQUMDw8DDBwcH4+PgQFhZGamqq9N7r169T\nUVGB2+2mpqaG9957D6PRSFRUFN3d3ezbt4/R0VEcDgeXLl3inXfewW63k5SURF1dHQcPHpRGphQK\nBREREaSmphIQEMCZM2coKSnB7Xb/xaCwv7+fN998k4aGBqKiovD392ffvn3U1dVJvX4dHR0YjUYi\nIiIwGAwUFBTQ2Ngo9UgGBgaSkpIinccDBw5gtVrR6/W8/vrr+Pj4kJCQAEBLSwsej4eSkhL279+P\nQqEgNjaW6upqjh49Sl9f3x3H2dfXx9mzZ6muriYqKoqgoCDGxsakXtGpqSn0ej1arZbY2FjOnTtH\nQUEBDoeDwcFBjh49yvj4OCEhIajVahISEsjOziYmJgadTodOpyM+Pp7U1FSCg4PFl+gzYu3atVy+\nfJnGxkZOnjzJr371K9RqNampqeLkPCD6+/upq6sjLS2NFStWEBgYiEqlIjg4mPnz5zN//nyGh4c5\ndeoURUVFUmA8nYo8/TveeOMNqqqqCAsLw8vLS0o9djqd3Lx5k4KCAsbHx4mNjcXf319qD6ampqiq\nquLKlStoNBpCQ0Px9vYmISGBjIwMgoOD6evrkzqzXC4Xv//976moqCA4OBgvLy/++Mc/UlpaitVq\npbm5mWPHjlFRUYG/vz8ej4eLFy9KI1MymQxvb2+SkpJITk7GZDLxi1/8AqfT+RdHKaxWKxcuXOD8\n+fPYbDZCQkKoq6vj6NGjDA4OSsFQZ2cnKpWK0NBQrl27xqFDh/5vj7hSSXx8POnp6SiVSl577TWp\n/di/fz/Nzc1ERkYSGBgoBYU9PT2cOHGC8vJyIiIiUCgUnDlzhkuXLt31OG/fvs0777yDVqslLi4O\nu92OwWDAZrPh8Xjo6urCaDQSHh6OwWDg1KlT1NfXMzk5SUVFBSUlJXh5eREcHIy3tzdz5swhPT2d\nkJAQAgMDCQ0NJTMzk4SEBNEp8oC5L0Z04uLiWL9+PVeuXEGlUmG1WsnJyWHbtm3iCj4gbDYbPT09\nhIaGEh0dfWfE/n/ybo8fP45SqeTzn/88KSkpnDlzhtOnT5OYmEheXh5yuZz4+Hi2b99OWFgYR44c\n4c0338RgMGC32zl48CC7d+9m9+7dyGQyOjo6MBgMd/y9+fPnExcXR0pKCk899RRRUVG0trZKjZLZ\nbKa8vJz+/n7+/d//ndjYWK5fv86LL75IaWkpS5cuRSaTERUVxY4dO4iKikKj0fD++++ze/duwsPD\nSUpKwmazMTAwwODgICUlJURGRn7ikQm1Ws2aNWuYM2cOZrOZ0tJSrl69SkpKCvBh+teaNWtYtGgR\nly5dYv/+/bS1tZGXl0dERAQJCQn09PQwOjpKdXU1Go2GL3/5y3R3d1NdXc23v/1t5s2bx/DwMEaj\nEZlMxptvvkl4eDhPPPEE4eHhHD9+nEOHDrFo0SJiY2NnHJ/BYKChoQEfHx927dqFVqtldHSUgIAA\nhoaGUKlUpKWlsXv3bmnC+blz59i5c+eM35Ofn09MTAx5eXns2bOH8PBwJiYmcDgcbNu2jdWrV4sv\n0GfIpk2buHr1KqtXr0aj0aDValm0aBHp6eni5DwgBgcH8Xg8REZG3jFnQyaTIZPJ6Orq4vLlyyxf\nvpwnn3yS8fFxXnnlFV5//XXpHqpUKlmxYgUrVqzAaDSya9cu2tramDNnDmfOnMHpdLJnzx6ys7O5\ndOkSx44du+NYdDodS5YsITIykp07d7J06VKcTidFRUXSe3p6eigoKOBb3/oWGzZswGq18pOf/ISy\nsjKp3vr5+ZGfn8/nPvc5Ojo6ePPNNykrK2P79u34+vqSkZFBV1cXg4ODtLW1ce7cOcbHxz9xJ05q\naio7duwgKCiIQ4cOcePGDfR6Pd7e3mg0GmJjY9m1axcymQyn08mZM2f45je/iUwmIzc3l/b2doaG\nhujt7eXcuXPs3r0brVbLzZs3Wbx4Mbt27cLb25vx8XH8/f0pKyujpqaGRx55hM2bN9Pf389bb71F\nQUEBjz766Ixj83g81NbW0tLSwne/+11yc3MxGAwoFAo0Gg0AISEhrFq1iiVLlnDx4kVOnDiBXq8n\nIiJC+j2hoaHk5+dTUFDAF77wBRYtWsTExARZWVnExsbyzW9+E6VSKb5AItCZPSYnJ6W0mvDwcDZt\n2sTIyAhKpZKwsDBRYR/ECvsXrnltbS3btm0jLi5OmjNz4sQJent7ycvLQ6VSER0dTXJyMgCRkZEo\nFApGR0ex2+10dHSwefNmvL29cbvd0kP3X2tsbAyDwUBERAQZGRkAJCcnEx8fT2VlJUuXLkWhUJCe\nnk50dDRKpZLU1FRMJhNOp1MaYRkaGsLf35/AwEC0Wi1TU1PY7fZPdAwqlYqFCxcil8ulHq729nbp\n8/Hx8cTHx+Pj40NMTAxyuZypqSnGx8cpLi7m+vXrBAQESL1kw8PDOBwOoqKiyMrK4tChQ1y+fJm4\nuDhWr16NTCbj6tWr5Ofn89Zbb6FSqTAYDNTW1krf448KDQ0lNTWV27dv8+qrrxIREUFubi4xMTEM\nDQ3h7e1NTk4OwcHB2O120tPTqa+vF3nWD/DD7fRoZn5+vtQREBgYiJ+fn5hk/IBRKBTIZLKPvQcP\nDAywfPlyfHx8UCgUrFixgnfffVd6j1arZd68efj6+uLr60tgYCBmsxmj0UhLSwuLFy8mIyMDLy8v\ntFrt35wu39railwulxZPAcjNzaWxsRGDwYBMJiMsLIzExESCgoIwmUwEBQUxMjIipUAfPnyYwMBA\n6X5tt9sxmUyfONBJSEggPj4emUxGYmIiNTU19Pb2kpKSgq+vL6mpqYSHh2MymUhMTOTkyZO43W66\nu7t555138Pf3x8/Pj6ioKGQyGZOTk3h5ebFgwQK6u7ule/hDDz1EeHg43d3dVFZWotVqaW9vl7IB\nJiYm7jg2mUxGRkYGCQkJvP/++xQVFREfH8/DDz+MSqVCJpNJ7ZWvr6+0ANDd5uwIwn0V6DQ3N1NY\nWIjdbsdqteLr64tCocDtdmOxWPDz8+M73/mOuIoPALVaTXR0NENDQwwMDMzoxYEP07VkMtmffQj+\nc6/LZDLkcjkul0v675OukPNxjez03/vT93zcMU7X7emArbS0lM2bN7N161ZCQ0MZGhr6f3rIdzqd\nUqPxp+dkeijf7XbT1dXFjRs38PLy4plnniEuLo5jx47x6quvolQqyc7O5tlnn6WsrIy+vj5u375N\nf38/3/3ud3G5XFKwpFAoUCqVbNmyhfj4+DuOJyoqis2bN+Pv709LSwu3bt2irq4OjUZz1/xphUIx\nI5VQeLAcO3aMyclJxsfH8fb2Rq1WI5PJsFqtuFwu1q9fL01IFj7bpldUGxwcxGw2z7hfeDyeGf99\n9N4rk8k+NvVXoVDg8XhwuVw4HA4UCsUnSnOabkf+nOn26ePajz93rGNjY1y/fp2+vj6ee+45UlNT\nMRqN7Nu3729uD9xuNx6PZ0bH4UfbrOl77XQ6dmlpKf/93//NQw89hL+/P0eOHMHj8eDv78+ePXu4\ndu0aLS0t3Lx5k+bmZtxuN263WwrGps9hYmLiXVfRhA+zJL761a9SWVnJwMCAlNZ3tykKcrn8L17L\nT9pWCyLQuefcbjednZ00NDTw6KOPIpPJpBz9kpISEeg8IDQaDWlpaURERPDGG2/wxBNPEBkZCXw4\n10Ov17NkyRIyMjJobW2lt7cXjUZDbW0tcrlceu+fI5fLCQgIICgoiLKyMlatWsXY2BhdXV1/9maq\n1WoxGo0YjUZ0Oh1Op1P6WWBgIMHBwTQ1NdHa2kpkZCSdnZ10d3d/otSziYkJtFot6enpBAcH09nZ\nicFg+KsmULrdbkZHRwkKCqKvr4/q6mr27NnzF3slp1dim26UxsbGpLQ8h8PBxMQEMTExLF26FLPZ\nzE9/+lNOnjzJt7/9bRYsWEBubi579uwhKioKq9XK0NDQXRdQGB0dRSaTsWXLFry9vSksLOTtt9+m\nrq6ORYsWfWyA+pd4eXlhs9kYHx/HarUik8mkFAjh/jQ9wfz06dMsW7aMqKgo5HI5FouFyspKUlJS\nRKDzgIiMjCQ1NZX6+nouX77M0qVL8fPzw2QySYsRBAQEEBYWxs2bN0lMTMRoNFJaWsr8+fP/4u/3\n9vYmPDycvr4+ent7CQsLo7+//8+u7KdQKFCr1fT392O32+8YdU9MTMTpdKLX64mOjsZms9HQ0IBO\npyM4OPhj73N2u52pqSmioqJIT0/H6XRSUVHxV58zi8WCxWLB4XDQ3NyMw+EgKSnpY4MFj8fDyMgI\nISEh5ObmotVqqampwWKxAB8uaAMfppP6+flx8uRJDh8+TGNjI5GRkSxcuJBHH32UNWvWoFarGR8f\nlz77p1pbW8nIyGDVqlVMTk7y/e9/n6qqKh5++OH/p7ZAJpPh4+MjXRubzYa3t7cYARaBzuwwb948\n5s2bx9mzZ1EoFHz/+9+Xet9bWlr4/ve/L67gA0IulxMREcHXv/513nnnHVwuF9HR0chkMvr7++np\n6SE7O5utW7dy8OBBjh8/Tnh4OFVVVSQnJ8/Y1+ZuvTsymYzg4GA2btzIwYMHGRgYwGq10tLSQlRU\n1F0/m5OTI03anD9/Pn5+ftLP/f39mTdvHvX19bz11lvExsbS0dFBREQES5cu/bM37unPp6en4+Pj\nQ2FhIV1dXQwNDdHe3v5X7XNgs9k4duwYGo2Grq4ufH19Wb58Od7e3h/bKMTExBAREUFtbS379+9n\namqK8vJy6Xd2dnZSWFhIUlISKpUKp9PJqlWrkMvlPP300+zfv59Dhw4REhIiNSzTDeFHDQwMcOXK\nFSwWC8HBwQwODhITE/N3mWuRkJCAUqnk0qVLWK1WsrOzP/W9jYRP17PPPgvAjRs3eOqpp5g3bx4y\nmQyLxcILL7wgJhg/YB1fW7duZf/+/Zw8eZLW1lYCAgIwm8309vaSmJjI1q1bWbZsGUVFRVIWSEND\nA08//fQnam8eeeQRLl26xHvvvUdkZOTHLufv5+dHZmYmJ0+eZHJyUrqHTd/PExISWL16NZcuXWJg\nYACbzcbo6Chr1qyZ0b786b14elGY1NRUKisreffdd1EoFFRVVd01KPk41dXVHDp0CLPZTENDA2lp\naWRkZEjpwHcbcVIoFMyfP5/333+fgwcPEhgYSFtbm5SKPDIywoULF3C5XISGhtLV1UV8fDxJSUnE\nxcXR1tbGxYsXMRgMqFQqpqamCA4Olhax+eixV1VVodfrpf3w5HI5ubm50hYCnySguRuVSkVeXh5v\nvPEG7777LiEhIWzcuPFj20Hhs+W+2DC0p6dHWhnF5XIxNjZGc3Mz169f58knn/ybfqfZbKapqYmq\nqioGBwdRKBR3PIg5nU76+vq4efMmra2t0l4q072IVVVVNDQ00N7eDnzYwy/mDX16pldWCg8Pp7a2\nloqKCtrb29Fqtaxfv560tDSSk5NRq9U0NDTQ1NREYmIi27dvJy0tDYfDIa3aMmfOHKkeWCwWFi1a\nRGRkJImJiTQ1NVFZWYlcLicrK4v4+HgWLFiA0+lkYmKCxYsXo9PpCAsLY2RkhLq6OuRyObGxsSiV\nSqKjo8nOziYkJITg4GBqampobGwkPDycp59+mvj4eFwuFwaDgfj4eLKzs5HL5UxOTjIxMcGKFStI\nTk5GoVBQV1dHS0sL0dHRhIWFkZOTQ3Z2NlNTU8jlcjIzM+8YLXG5XPT09PDOO++QlpZGWVkZHo+H\nvXv3SqvmDA4OEhISQkZGBr6+vlitVkwmE+np6cyZMwetVkt3dzc1NTVoNBrpvC9fvhyZTEZ9fT21\ntbV0dnaSkpLC1772NXx8fEhKSkKr1VJbWyvtYxMTE0NWVtYdDdb0kuHV1dXo9Xrkcjnr1q1j5cqV\n2O12xsbGyM3NJTY2Fo/Hw/j4ODKZjBUrVmC326Vz5e/vT1dXF8nJySQnJ+Pl5UVgYCAej4empib6\n+/tnzMsS7m8XLlyQJlBPTk7S399PSUkJsbGxf9MeVh6Ph8HBQcrKymhubmZqagp/f/+73sstFguN\njY2YzWaCgoKk75vBYKC8vJzW1laMRiPBwcEi8PqURUREEB8fj8Viobq6mvr6esxmM+np6Tz88MPE\nx8cTHR3NxMQEZWVljI+Ps2HDBh577DFpjsnY2Bjr1q2THno7OjrIzMwkMTGR5ORkbDYbNTU1GAwG\nwsLCSEtLIzMzk5iYGCllLj8/H41GQ3BwsDShPiEhgejoaMbGxti4cSMajYacnBz6+vqoqKjAaDSy\ndetWli9fjr+/P+Pj46jVatLS0ggPD8fhcGAymQgMDGTp0qXodDomJia4efMmRqORBQsWoFAo2LJl\nCz4+PnR2dpKVlSVtTfCndfbUqVNMTEwwOfm/2bvv8KjKtPHj3+mTMskkmfReCKRBCBBKAkivIgIi\n9rKsr33XdfWVn7u6u2LZV9fd1VUXXV5R7ChWqtI0EqmhBEIgJCEhPaRPps/5/cGV8zoGkEQUEp7P\ndXlJJmeeTDlznrmfct9mqqqqGDZsGFdddRUmk4nOzk6sVisJCQnEx8fjdrvp6OjA4XAwZcoUIiIi\ncDqd5OfnU1VVxeDBg9HpdIwfP56oqCiqq6spLCykqKgIlUrFtGnTGDVqFCEhIYSFhdHS0sKePXso\nKSlBoVCQmpp6xgE7l8vFwYMHOXjwICdOnCAjI4O5c+cSGhpKbW2t/PobDAY6Ozux2WzExsYSExND\nR0cH/v7+DBs2TN7bnZ2dLX8OIyIiqKqqYu/evahUKkaMGCFm+C8jCqkP7Ow9ceIEn3zyCaWlpSQl\nJWG326moqCAhIYHf/OY3PW7P7XaTn58v5+HX6XQMHz5cziLS1fk1NTXx6quvcuzYMZRKJX5+fjzy\nyCOYTCa2bt3KZ599htlsljdKL1y4UM5oJQgXi91u57vvvuPaa6+lqqpKTNEL/cratWv56quvMJlM\nGAwGqqurUalUXHXVVYwYMaLHQY7T6eTFF1+UR7YNBgN33303CQkJHsFOTU0Ne/bs4cMPP2TEiBHc\nc889wOnsgRs2bGDTpk0olUpMJhM333zzL1I4WBB+TENDA/fccw9jx46Vs6gJwuWkT3wDio2N5ZZb\nbmHOnDnyest58+bJHU1Pmc1mtm/fjq+vLy+++CILFiyguLiYo0ePysc4nU5OnDjBunXreP7553nl\nlVc4efIku3fvxuFw8O6775KWlsYzzzzDH//4Rw4fPuxxf0EQBOHCmzlzJosXL5Zn+rKysrjjjjvO\na+/FmQKduro63nzzTR5//HGWLVuGw+GgoKCgW0anoqIiPv/8c44cOeJxe2FhIXv37uW//uu/+Otf\n/8rAgQM9MnsJwsUmghtBBDqXuObmZkpLSwkICGDMmDFkZ2djMBgoKirqVXs1NTW4XC45tW5kZCRh\nYWEcPnxYPsZqtVJaWsqgQYMwGo2oVComTpzIrl27sNvtBAcHU15eTnFxMY2NjQQHB3db+iYIF6tT\nMxgMZGZmihdD6HeKiorkfVe5ubkkJibS2NhIY2Njj9tyu90UFhYyaNAgAgMDUavVjBkzhmPHjtHe\n3u5x7MSJE3n66aeZMWOGx36IxsZGOjo6GDp0KIGBgaSlpbF3717xRgmXBLVaTUJCAqGhoeLFEC7P\nz0BfeJC7d+9myZIl8s+SJGE2m/H29u5Vh2I2m5EkSV6mptVq0Wg0HvndXS4Xra2tcs57gICAAIqK\ninC5XCxYsIAlS5Zw5MgRnE4n06dPF8vWhEuCRqNh6NChrFu3TrwYQr/zxBNPeMye2+12Ojo6+MMf\n/sDtt9/eo7YkSaKxsZGAgAB51Nvf35/29nYcDscZj//ham+Hw4HNZpOzGWo0mm5BkiBcLAEBATz9\n9NPihRBEoHMpmzJlikdK3s7OTr799lvWrFnTq/aUSqXHvoWuvPnf3zzalXHk+1wul5yKcfXq1cyf\nP5+hQ4eyZcsW8vLySE9PJywszGOauL29nebm5vPO9y4IgnAp0+l0P5qu/ef0zjvvePxcUlLCRx99\ndNb6HD/mXNf589HVV3QFQF01RM6kurr6vAv+CoIgXMqUSiWBgYE9KnshAp2z6Cre9f0XNzY2tle5\n5OF06l+FQkFbW5ucdtJqtRIeHu5RSCsoKIiGhgb5turqasLDw1GpVGzcuJF///vfZGZmkpWVxW23\n3UZlZSV2u90jm8e7777LX/7yl37RuTmdTtxu93kX1OwL55XT6ewX2Ve6PiO9rdx9KekqNNc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cqV6PV6GhoamD17NvX19WzatImxY8cSFRVFSkoKR48e5d///jd1dXXExcWRlJREQ0MDW7dupaOj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6PX19fVK5QggRAi7KOToqleqcpAGBQOCcVV7VavWQ/QKBAF6vl2AwqIzdDgaDwNmhboFA4Lyf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\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nzpyhs7MTh8OhBhQzZszA6/VSV1dHbm4ubrebxMTEC477I0eOEBQUREJCAmazmdLSUhoa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\nmzfPLTeRFUShIwizjs1mo7m5mfb2dqKjo+nv7+fAgQNkZ2dzzz3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       "prompt_number": 14,
       "text": [
        "<IPython.core.display.Image at 0x7f156c175110>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probability_list = [i ** 2 for i in probability_amplitude_list]\n",
      "fig = plt.figure(figsize=(10, 10))\n",
      "ax1 = fig.add_subplot(221)\n",
      "ax1.set_xlabel(\"Iterations\")\n",
      "ax1.set_ylabel(\"Probability amplitude\")\n",
      "plt.plot(np.arange(k+1), probability_amplitude_list)\n",
      "ax2 = fig.add_subplot(222)\n",
      "ax2.set_xlabel(\"Iterations\")\n",
      "ax2.set_ylabel(\"Probability\")\n",
      "plt.plot(np.arange(k+1), probability_list)\n",
      "print '''Probability amplitudes\n",
      "----------------------'''\n",
      "pprint.pprint(probability_amplitude_list)\n",
      "\n",
      "print '''Probabilities\n",
      "----------------------'''\n",
      "pprint.pprint(probability_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Probability amplitudes\n",
        "----------------------\n",
        "[0.0625,\n",
        " 0.1865234375,\n",
        " 0.3076324462890625,\n",
        " 0.4239346981048584,\n",
        " 0.53361297026276588,\n",
        " 0.63495353976031765,\n",
        " 0.72637296019911446,\n",
        " 0.8064428031348001,\n",
        " 0.87391197727150449,\n",
        " 0.9277262767633413,\n",
        " 0.96704485318074529,\n",
        " 0.99125335376719736,\n",
        " 0.99997352070104339]\n",
        "Probabilities\n",
        "----------------------\n",
        "[0.00390625,\n",
        " 0.034790992736816406,\n",
        " 0.094637722009792924,\n",
        " 0.17972062825725743,\n",
        " 0.28474280203265145,\n",
        " 0.40316599765415728,\n",
        " 0.52761767730842435,\n",
        " 0.65034999472791399,\n",
        " 0.76372214401859062,\n",
        " 0.86067604459717173,\n",
        " 0.93517574806336923,\n",
        " 0.98258321135471649,\n",
        " 0.99994704210324004]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WqFGDrVu3UqdOHTZu3EhKSgpt2rRh5cqVBQqbF+rMCi4xERo2NFtc/POfdqcR\nyZm32v3s2bN55plnqFixIqmpqaSlpTF16lQiIyO9kDJ31IcFlxMnIDwcPvkEWrSwO43YwSdTlueH\n9itXrkx8fDyRkZEcOHAgfwnFFpZlDrBt21bFmASf/v37ExsbmzkyFh8fT8+ePVm3bp3NycStnnkG\n7rtPxZjkTa62vThy5Ah9+vShXbt2VKpUiWbNmvkjm3jJmDFmm4tPP7U7iYj/lS1bNss0ZWRkJOnp\n6TYmEjebP98sC1m/3u4k4jR5espy165dJCQkUL9+fV9muoCG+/Nv1Sqz+WtsLFx/vd1pRHLPGxvD\nAnz++edUr16dyMhILMvi9OnTjB07lri4OG9FzZH6sOBw/LiZqvz0U9C4RXDTtheSxdGjZt3YO+/A\nvffanUYkbwra7rt3737JpyknT56c78/OK/VhwaFLF3ME3ZgxdicRu6kgk0wZGXD33VC3LowYYXca\nkbxzU7t307VI9r74Ap59Fn7+GYoVszuN2M0ni/rFmYYPh6Qk81SlSDDbt28f48eP5+DBg2RkZAD+\n3xhW3O3YMfj3v2HmTBVjkn+FcnrDggULdGfnMMuWmWnKzz6Dyy+3O42IvTp16kSJEiVYtGgRd999\nNxUqVCApKcnuWOIiTz1lnmBv2tTuJOJkOU5ZPvDAA8THx9OlSxceffRRKlas6K9smTTcn3sHD5p1\nY1OmwB132J1GJP+81e5r167Npk2bCA8PZ926dYSEhFC/fn1+/vlnL6TMHfVh7jV3Lvzf/8FPP0HR\nonankUCRnzaf4wjZ559/TmxsLCVKlKBdu3bcc889zJ8/X51LAEpLMweGP/aYijGR81JTU7Esi4iI\nCGbNmkViYiIJCQl2xxIXOHoUnngCJk9WMSYFl+tF/Tt27GDSpEm8++67REREsG/fPh5++GEGDx7s\n64y6u8yll16C1avh668hJMTuNCIF4612//7779OxY0eOHz9O27ZtSUxM5PXXX6dXr15eSJk76sPc\nqVMnqFQJRo2yO4kEGp8s6v/kk0/46KOP2L9/P4888ghbt24lNDSUo0ePalFsAFm40ExTxserGBP5\ns/OFV9myZdm+fbvNacQtZs8205R+3D1FXC7HEbJu3brRs2dPW3fn193lpe3dCzfdBLNmaVGpuIe3\n2v2WLVvo378/a9eu5corr+SOO+5g6NChlClTxgspc0d9mLscOQL16sF//ws332x3GglEPllDVrhw\n4SzFmGX5yzFpAAAgAElEQVRZ/Otf/8p7OvGJ1FR44AHo10/FmEh2OnXqRJcuXdi2bRtr164lLCyM\nzp072x1LHOyJJ8wmsCrGxJtyHCELCwtj8+bNma9TU1OpWrUqe/fu9Xm483R3eXH9+sG2bWZTwkI5\nltcizuGtdh8REXHBE5W1atViy5YtBf7s3FIf5h6ffw6DBsG6dXDllXankUDl1TVk7733HuPHj2fn\nzp2Eh4dn/vrhw4eJiorKf0rxmrlzzZD52rUqxkT+6vjx41iWRaNGjVi8eDENGzYE4NSpU7Zs3yPO\nd+gQ9OljboBVjIm3XXSELDExkRMnTvDiiy8yfPjwzEqvVKlSlCpVyr8hdXd5ge3b4ZZbYMECaNTI\n7jQi3lfQdl+lSpVLnmW5c+fOfH92XqkPcz7LgqgoqF7dnIQicilePcvywIEDVKhQgd27d2fbqV13\n3XX5S5kP6syyOnMGmjSBRx6BJ5+0O42Ib7ip3bvpWoLVZ5/Ba6+ZGQmNjklOvFqQtW7dmm+//Za6\ndetmW5Bt2LAhfynzQZ1ZVr16wfHj5ty0SwwAiDiat9p9WloaH3zwAUuXLsXj8dCiRQt69erFZZf5\n7yhf9WHOdvAgRESYGYmbbrI7jTiBVwuyQKLO7A/Tp8PgwfDjj1CypN1pRHzHW+2+V69epKWl0blz\nZyzLYsaMGXg8HiZMmOCFlLmjPsy5LAvuuw/q1IEhQ+xOI07h1YJszpw5l1x/cf/99+ctXQGoMzM2\nbYLmzWHJErMHjoibeavd//VJcdBTlpJ706ebNWM//giFC9udRpzCq09Zzp8/P2AKMoGUFOjYEd54\nQ8WYSF4UKVKE7du3U7VqVcAcA1ekSBGbU4kTHDhgthaKjlYxJr6nKUsHsCzo1s1sbTF5staNSXDw\nVrv/7rvv6N69e+aDSHv27GHy5Mk0b968wJ+dW8HehzmRZUGHDmbt2Guv2Z1GnMarI2QDBgxg6NCh\n9OjRI9sfpHMs/efDD80mhKtXqxgTySvLsti8eTNbtmzB4/FQo0YNrtRjcpKDTz6B3bvNmZUi/nDR\ngqzR/za3at++/QW/d6mpTPGun36CAQPg+++haFG704g4T58+ffj555+JiIiwO4o4xP798Oyz8M03\ncMUVdqeRYJGrKctz586xa9cuihQpQqVKlfyRK4tgHe5PSYHISHNMx0MP2Z1GxL+81e47duzI6NGj\n/bp34l8Fax/mRJYF99wDDRuaJ9pF8sMnh4vPnj2b66+/ns6dO3PPPfcQHh5OfHx8rj48Ojqa8PBw\natWqxbBhwy753pEjR2Y5okngmWfMbvwqxkTyb9WqVTRu3Jjw8PDMr3q5fDJGfVjwmTYNfv8dBg60\nO4kEmxx3Ruzfvz+xsbGZI2Px8fH07NmTdevWXfLPpaSk0Lt3b+Li4ihbtiwtW7akbdu2NGjQ4IL3\nrly5kk8//VRToX8yZw4sW2bWjolI/q1cuRIgz3er6sOCz7598PzzsGiRpirF/3IcIStbtmyWacrI\nyEjS09Nz/OC4uDgiIyMJDQ0lJCSEqKgooqOjL3jf0aNH6devHx988IGG9P9n717o3RtmzIASJexO\nI+JMJ0+e5LnnnuOpp55i9uzZXHvttVSpUiXzKyfqw4KLZcFjj5nj6OrXtzuNBKOLjpDNmTMHgBtu\nuIGXXnqJyMhILMvi9OnTuXpCaf/+/YSGhma+LleuHNu2bcvyHsuy6N69OyNHjszy3mCWng5dusDT\nT+vQcJGC6NGjB2FhYTz33HN89NFHvPzyywwdOjTXf159WHCZPNkckfTii3YnkWCV48awRYsWZd++\nfezbty/z9+rUqZPjB3s8HkJCQrL8WmpqapbXb731Fk2aNKFZs2bs2rUrj9HdacQI888XXrA3h4jT\n/fLLL5k3lo0bN6Zu3bp5KsjUhwWP3btNn7tkCVx+ud1pJFhdtCCbMmVKgT64fPnyHDlyJPP14cOH\nqVChQpb37Nq1i0WLFvHxxx9z7tw5fv/9d5o3b87y5csv+LxXXnkl8/sWLVrQokWLAuULRHFx8Pbb\n5oiOv/x/QMT1YmJiiImJ8drnFSr0x4qMwoUL5/kwcfVhwSE9Hbp2NWvHdAqK5Jc3+q8ct73Yt28f\n48eP5+DBg2RkZJg/lIuNYZOTkwkPDycuLo7SpUvTqlUrhgwZQkREBAkJCRc8gr57927uvvtuNmzY\ncGHIIHhkPCkJGjQwZ6ZFRdmdRsR+BW33hQoVolixYpmvT506RdH/bebn8Xg4efLkJf+8+rDg8MYb\nsHChGR3TjbB4i0+2vejUqRMlSpRg0aJF3H333VSoUIGkpKQcP7h48eKMGzeOli1bUqdOHdq0aUPT\npk2ZO3cu3bp1u+D9lmUF9RNKffpAixYqxkS8JSMjg6SkpMyv9PT0zO9zKsZAfVgwiI+H0aPNVhcq\nxsRuOY6Q1a5dm02bNhEeHs66desICQmhfv36/Pzzz/7K6Pq7y5kz4eWXTedQvLjdaUQCg5vavZuu\nxS1OnTKbv/7nP/Dgg3anEbfxyQhZamoqlmURERHBrFmzSExMJCEhId8hJavdu+Gpp8wWFyrGRET8\no39/cxKKijEJFDmOkL3//vt07NiR48eP07ZtWxITE3n99dfp1auXvzK69u4yLQ1atjTHdPTvb3ca\nkcDipnbvpmtxg+ho+Pe/4eefoVQpu9OIG+WnzefqLEu7ubUze+01iImBxYuhUI5jlSLBxU3t3k3X\n4nRHjkBEhJmV0IOu4is+mbLcsmUL9957LxUrVqRq1ar06tWL48eP5zukGKtWwbhxZjGpijEREd87\nvxt/ly4qxiTw5Oopyy5durBt2zbWrl1LWFgYnTt39kc21zp5Eh5+GD74ACpWtDuNiEhwmDTJrNt9\n9VW7k4hcKMcpy4iIiAueqKxVqxZbtmzxabA/c9twf5cuULSoKchEJHtuavduuhan2rYNmjSB5cuh\ndm2704jb5afNX3Tr6uPHj2NZFo0aNWLx4sU0bNgQMJsrVtSwTr5Nn2524v/xR7uTiIgEh3PnoHNn\nGDRIxZgErouOkFWpUuWSmxzu3LnTZ6H+yi13lzt2QOPGsGiR2ZVfRC7OLe0e3HUtTjRokDmaLjoa\ntHev+IOesgxgaWnQrJnZib9fP7vTiAQ+N7T789x0LU6zahXcdx+sWwd/OYpUxGe8OmV5XlpaGh98\n8AFLly7F4/HQokULevXqleeDeoPda69BiRLwzDN2JxERCQ5JSWaq8r33VIxJ4MtxhKxXr16kpaXR\nuXNnLMtixowZeDweJkyY4K+Mjr+7/P576NhRd2gieeH0dv9nbroWJ+nZ00xRfvih3Ukk2PhkhGz5\n8uVs3rw583XLli2pVatW3tMFqYQE81TlxIkqxkRE/GXuXPNE5U8/2Z1EJHdy3IesSJEibN++PfP1\njh07KFKkiE9DuYVlQa9e0L69OR5JRER8b/9+6N0bPvlEZwSLc+Q4Qvb2229zxx13cN111wGwZ88e\nJk+e7PNgbjBtGmzYoC0uRET8JSMDevQwZ1XefLPdaURyL8eCzLIsNm/ezJYtW/B4PNSoUYMrr7zS\nH9kc7bff4LnnYMkS0ICiiIh/jBsHiYkwcKDdSUTyJl879fub0xbEnjsHt95qjkd6+mm704g4k9Pa\n/aW46VoC2caN5ozKVaugWjW700gw88nh4jVq1GDPnj35DhWMXnkFrr4a+vSxO4mISHA4e9bcBA8f\nrmJMnCnHEbJKlSqRnp7O1Vdf/ccf8nhYv369z8P9+ec55e4yJgYefNA82XPNNXanEXEuJ7X7nLjp\nWgJV//7mvMq5c7Ubv9jPJ9terFy5EkCdSS4cPw5du8KkSSrGRET8Zdkyc07wzz+rGBPnuugI2cmT\nJ3n11VfZunUrzZs355lnniEkJMTf+QBn3F1aFjzwAPztbzBmjN1pRJzPCe0+t9x0LYHmxAmIiIAJ\nE6BtW7vTiBheXUPWo0cPrrzySp577jk2bNjAyy+/XOCAbjZjBmzaBG+8YXcSEZHg8cQT0KGDijFx\nvouOkNWsWZNff/0VgLNnz1K3bl22bdvm13DnBfrd5d690LAhfP01REbanUbEHQK93eeFm64lkMyY\nAa+/DmvXanshCSxeXUNWqNAfg2eFCxfWYeIXcX4Twj59VIyJiPjL7t3wzDPwzTcqxsQdLjpCVqhQ\nIYoVK5b5+tSpUxQtWtT8IY+HkydP+ichgX13OW4cfPwxrFwJqllFvCeQ231euelaAkF6OrRqZY6l\n69/f7jQiF/LqCFlGRkaBA7ndr7+aPcdUjImI+M+oUeZpymeftTuJiPeojMintDSzxcUrr0DNmnan\nEREJDqtXw+jRsGYN2PTgv4hP5LhTv2Rv+HAoWRJ697Y7iYhIcDh4EKKi4MMPoXJlu9OIeFeOO/UH\ngkBbfxEfbx6xjo+HSpXsTiPiToHW7gvCTddil3Pn4PbboWVLGDzY7jQil+aTsywlqzNnoEsXeOst\nFWMiIv7y7LNmVmLQILuTiPiG1pDl0cCBULs2PPSQ3UlERILDxx/DwoVm3VghDSOIS6kgy4OYGPj0\nU1i/XueliYj4Q3w89Otn+t9SpexOI+I7utfIpZMnoXt3c17a1VfbnUZExP2OHoV//APGj4c6dexO\nI+JbWtSfSz17mqHyiRNtjSESNAKh3XuLm67FX9LS4K67zAkoOiNYnMarG8PKH778EpYtg59/tjuJ\niEhwGDjQ/HPIEHtziPiLCrIcHDkCvXrBzJlQooTdaURE3O/zz83Xjz/qFBQJHpqyvATLMusXqlWD\nESP8/uNFgpqbpvncdC2+9ssvZq+xRYugQQO704jkj6Ysvezjj2HbNpgxw+4kIiLul5AA990Hb76p\nYkyCj0bILmLPHmjYEBYvhvr1/fqjRQR3jSq56Vp8JSMD7rnHzEiMGWN3GpGC0U79XpKRAT16QN++\nKsZERPxh8GBIToZRo+xOImIPTVlmY9w4OH0a+ve3O4mIiPt9+SV89JFZxH/55XanEbGHpiz/YssW\nuO02WLUKqlf3y48UkWy4aZrPTdfibb/+Ck2bwvz50Lix3WlEvENTlgV07pw5OPy111SMiYj4WlKS\nWcQ/ZIiKMRGfFmTR0dGEh4dTq1Ythg0bdsHvnz17ltatW1OtWjVq1qyZ7Xv8adgwKFvW7DsmIuK0\nPsxJLMus1b3tNnjsMbvTiAQAy0eSk5OtypUrW4cOHbLS0tKspk2bWvHx8Vnec+bMGWvp0qWZ30dE\nRFg//fTTBZ/lw5iZ1qyxrHLlLOv3333+o0QkF/zR7i/FaX2Y0wwbZlmNG1vWmTN2JxHxvvy0eZ+N\nkMXFxREZGUloaCghISFERUURHR2d5T2FCxemZcuWmd9Xq1aNw4cP+yrSRZ0+baYqx4yBihX9/uNF\nJAA5qQ9zmm++gbFjYfZsKFzY7jQigcFnBdn+/fsJDQ3NfF2uXDkOHjx40fcfOnSI2NhYGtuwkGDA\nAKhXDzp18vuPFpEA5aQ+zEl27ICuXeGzz6BSJbvTiAQOn2174fF4CAkJyfJrqamp2b73zJkzdOzY\nkaFDh1KyZElfRcrWsmUwa5Y5ONzj8euPFpEA5pQ+zElOnYL774eXXoJmzexOIxJYfFaQlS9fniNH\njmS+Pnz4MBUqVLjgfWfPniUqKor27dvTtWvXi37eK6+8kvl9ixYtaNGiRYEzJiZC9+4wcaJZzC8i\n9omJiSEmJsbuGJmc0Ic5iWWZxfv16sGTT9qdRsS7vNF/+WwfsuTkZMLDw4mLi6N06dK0atWKIUOG\nEBERQUJCAtdddx2nTp3ivvvu4/bbb6f/JXZh9dUePj16mPUL77/v9Y8WkQKye+8uJ/RhTvL22zBt\nGqxcCUWK2J1GxLcCah+y4sWLM27cOFq2bEmdOnVo06YNTZs2Ze7cuXTr1g0wi2aXL1/O5MmTCQsL\nIywsjIEDB/oqUhbz5sF33+mYDhHJXqD3YU4SEwPDh8PcuSrGRC4mKHfqP3LEDJvPng233uq1jxUR\nL3LTqJKbriWv9u41m75OmwatW9udRsQ/8tPmg7Ige+ABqFwZRo702keKiJe5qYhx07XkxZkzZvF+\nVJTOBpbgkp82H3SHi3/+OWzYYO7WRETEN86eNYVY9erw/PN2pxEJfEE1QnboEEREwJdfQqNGXggm\nIj7jplElN11Lbpw7Bx07QkiI2W/s8svtTiTiXxohuwTLgn//Gx55RMWYiIivpKXBgw9CRoaZkVAx\nJpI7QVOQffopbN1q/ikiIt6XlmaOoTt1Cv77X7jiCrsTiThHUExZHjgA9etDdDQ0bOjFYCLiM26a\n5nPTtVxMerrZaPvQIbMs5Mor7U4kYh9NWWbDsuDxx82XijEREe/LyIBHH4V9+2DBAhVjIvnh+oLs\n449h926z55iIiHhXRgb06mUODY+OhqJF7U4k4kyunrLctw8aNIBFi8yUpYg4h5um+dx0LX9mWeZc\nyp9+gq+/hhIl7E4kEhg0Zfkn5w+yffJJFWMiIt5mWdC3L/z4IyxerGJMpKBcW5BNngwHD8KLL9qd\nRETEXSzL7Ly/YgV8+y2ULGl3IhHnc+WU5Z49ZgH/0qUQHu7DYCLiM26a5nPTtVgWDBxo1ostXQpl\nytidSCTwaMoS01k8+qgZSlcxJiLiXYMHw/z5sGyZijERb3JdQTZxIpw4oYNsRUS8bcgQs/t+TAxc\nfbXdaUTcxVVTlrt2wU03wfLlULu273OJiO+4aZrPDdcyYgRMmmSKsQoV7E4jEtiCesoyI8OcU/n8\n8yrGRES86a23YMIEc7OrYkzEN1xTkL33Hpw+Dc8+a3cSERH3eOcd8xUTAxUr2p1GxL1cMWW5fTs0\nbgwrV0LNmn4MJiI+44ZpvvOcei3vvw/Dh5tirEoVu9OIOEdQTllmZECPHjBggIoxERFvmTTJLOJX\nMSbiH44vyN55xxRlTz9tdxIREXeYOhUGDTL7jFWtancakeDg6CnLrVuhSRNYtQqqV7chmIj4jFOn\n+bLjpGuZMQOee84UY7Vq2Z1GxJmCasoyPd1MVf7nPyrGRES84fPPzYNR336rYkzE3wrZHSC/3n4b\nLrvMHB4uIiL5Z1nw4YfQpw98/TXUqWN3IpHg48gRsi1bYNgwiIuDQo4tKUVE7HfiBDz+OGzeDEuW\nqBgTsYvjypm0NOjWDV59FW64we40IiLO9f33UL8+lC9vbnBVjInYx3EjZKNHQ/Hi0KuX3UlERJwp\nLc3c1E6YYKYq777b7kQi4qiCbONGGDUK1qzRVKWISH7s3AkPPwwlSsC6dToKSSRQOKasOXfOTFUO\nGaJNCkVE8mPGDGjUCKKiYOFCFWMigcQxI2QjRkCZMvDYY3YnERFxlpMnzRPpcXGwaBE0aGB3IhH5\nK8eMkL39tjnKw+OxO4mIiHOsXm0KsCJFYO1aFWMigcoxI2RvvAHXXmt3ChERZ0hPNweDjx0L770H\n999vdyIRuRTHHJ2UkWFpdEwkiDjpuKGc+Pta9u6Fzp3Nw08ffwyVKvntR4sI+WvzjpmyVDEmIpKz\n2bPhxhuhbVtzBJKKMRFncMyUpYiIXFxKCjzzDCxbBvPnm6cpRcQ5HDNCJiIi2YuPh8hIsz3QunUq\nxkScSAWZiIhDZWSY00vatoVXXoEpU8yGryLiPJqyFBFxoAMHzGbZKSlmfzFtmC3ibBohExFxkKQk\nc/5kZCQ0aQLLl6sYE3EDjZCJiAS49HRYuhSmToUFC6BFC5gzxxRkIuIOjtmHzAExRcSL3NTu83st\nmzfDtGnwySdwzTXQtSs8+CCUK+eDkCLiNflp8xohExEJIMeOwWefmdGw3383G7wuXAh169qdTER8\nSSNkIhKQ3NTuc7qWc+dM0TV1KixZAnfdZRbst24Nl+m2WcRxAm6n/ujoaMLDw6lVqxbDhg3L93tE\nROzgyz7Mssz+YU8/DRUrwogRZvuK3bvh00/N9yrGRIKHzwqylJQUevfuzZIlS9i4cSMLFy5k3bp1\neX6P08TExNgdIVeU0/ucktUpOe3mqz7swAEYNQrq1YN//ANKlYJVq2DFCnjsMbjqKl9e1aU55e+G\nU3KCc7Iqp/18VpDFxcURGRlJaGgoISEhREVFER0dnef3OI1T/rIop/c5JatTctrN233YzJnQrh3U\nrg2bNsG4cbB9OwweDFWr+uOKcuaUvxtOyQnOyaqc9vPZgPj+/fsJDQ3NfF2uXDm2bduW5/eIiNjB\n233YpElmXdisWVCsmG8yi4hz+awg83g8hISEZPm11NTUPL9HRMQO3u7DFi3ybj4RcRefFWTly5fn\nyJEjma8PHz5MhQoV8vwegKpVq+LxeHwV1esGDx5sd4RcUU7vc0pWJ+SsavM8XrD2YU74uwHOyQnO\nyaqc3pOf/stn214kJycTHh5OXFwcpUuXplWrVgwZMoSIiAgSEhK47rrrLvqepk2b+iKSiEiuqQ8T\nEX/y2aL+4sWLM27cOFq2bEmdOnVo06Y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       "text": [
        "<matplotlib.figure.Figure at 0x7f154d7f9490>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Grover's Algorithm: Geometric interpretation\n",
      "\n",
      "Let us consider a database of $N$ items having $M$ solutions.\n",
      "Thus, the initial superposition can be written as:\n",
      "<br><br>\n",
      "<div align=\"center\">$|\\psi \\rangle = \\sum\\limits_{x} \\alpha_x |x\\rangle = a|\\alpha \\rangle + b|\\beta \\rangle$</div>\n",
      "where, $|\\alpha \\rangle = \\frac{1}{\\sqrt{N-M}} \\sum\\limits_{\\{x|f(x)\\neq1\\}} |x\\rangle$ and $|\\beta \\rangle = \\frac{1}{\\sqrt{M}} \\sum\\limits_{\\{x|f(x)=1\\}} |x\\rangle$.\n",
      "<br><br>\n",
      "Thus $|\\alpha \\rangle$ is a superposition of the states which do not satisfy the oracle, and $|\\beta \\rangle$ is a superposition of states which satisfy the oracle. The state $|\\psi \\rangle$ can be re-written in terms of $|\\alpha \\rangle$ and $|\\beta \\rangle$ in normalized form as:\n",
      "\n",
      "<div align=\"center\">$\\boxed{|\\psi \\rangle = \\sqrt{\\frac{N-M}{N}} |\\alpha \\rangle + \\sqrt{\\frac{M}{N}} |\\beta \\rangle}$</div>\n",
      "<br>\n",
      "So the initial state is in a space spanned by $|\\alpha \\rangle$ and $|\\beta \\rangle$. Since $a^2 + b^2=1$, we can express the amplitudes of $|\\psi \\rangle$ as sinosoids.\n",
      "<br><br>\n",
      "<div align=\"center\">$\\boxed{|\\psi \\rangle = \\cos{\\frac{\\theta}{2}} |\\alpha \\rangle + \\sin{\\frac{\\theta}{2}} |\\beta \\rangle}$</div>\n",
      "<br>\n",
      "**Phase inversion:**\n",
      "<br>\n",
      "The phase inversion operation on a state vector can be thought of as a rotation of the state by an angle -$\\theta$ clockwise ( or $\\theta$ anticlockwise).\n",
      "<div align=\"center\">$a|\\alpha \\rangle + b|\\beta \\rangle \\xrightarrow{oracle} a|\\alpha \\rangle - b|\\beta \\rangle$</div>\n",
      "<div align=\"center\">$\\Rightarrow |\\psi \\rangle \\xrightarrow{oracle} \\cos{\\frac{\\theta}{2}} |\\alpha \\rangle - \\sin{\\frac{\\theta}{2}} |\\beta \\rangle$</div>\n",
      "<div align=\"center\">$\\boxed{\\Rightarrow |\\psi \\rangle \\xrightarrow{oracle} \\cos{\\frac{-\\theta}{2}} |\\alpha \\rangle + \\sin{\\frac{-\\theta}{2}} |\\beta \\rangle}$</div>\n",
      "<br>\n",
      "**Reflection about mean:**\n",
      "<br>\n",
      "The Grover operator $G$ reflects the $O|\\psi\\rangle$ state about the mean $|\\psi\\rangle$. This operation also includes the unitary transformation which performs phase inversion of the search element.\n",
      "<br>\n",
      "<div align=\"center\">$\\Rightarrow G|\\psi\\rangle = \\cos{\\frac{3\\theta}{2}} |\\alpha \\rangle + \\sin{\\frac{3\\theta}{2}} |\\beta \\rangle$</div>\n",
      "In general, for $k$ iterations,\n",
      "<br>\n",
      "<div align=\"center\">$\\boxed{G^k|\\psi\\rangle = \\cos{\\frac{(2k+1)\\theta}{2}} |\\alpha \\rangle + \\sin{\\frac{(2k+1)\\theta}{2}} |\\beta \\rangle}$</div>\n",
      "<br>\n",
      "Grover operator $G = 2|\\psi\\rangle\\langle\\psi|-I$ performs reflection in the plane defined by $|\\alpha\\rangle$ and $|\\beta\\rangle$ about the vector $|\\psi\\rangle$. So the $G^k|\\psi\\rangle$ remains in the space spanned by $|\\alpha\\rangle$ and $|\\beta\\rangle$ for all $k$. Repeated rotation of the state vector using the Grover operator brings it close to $|\\beta\\rangle$. A subsequent measurement in the computational basis produces one of the outcomes in the superposition $|\\beta\\rangle$ with high probability, thus finding a solution in the search stack."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Grover's Algorithm: Complexity\n",
      "Let the rotation brought about by the Grover operator be repeated for $k$ number of times. We are interested in finding the minimum value of this number $k$. The effect of Grover's operator $G$ on the initial state is given below.\n",
      "<br>\n",
      "<div align=\"center\">$\\boxed{G^k|\\psi\\rangle = \\cos{\\frac{(2k+1)\\theta}{2}} |\\alpha \\rangle + \\sin{\\frac{(2k+1)\\theta}{2}} |\\beta \\rangle}$</div>\n",
      "<br>\n",
      "Ideally we want to get as close as possible to $|\\beta\\rangle$, with the smallest possible value of $k$. This means that we want the value of $\\sin{\\frac{(2k+1)\\theta}{2}}$ to be as close as possible to $1$. It follows that the ideal value of $k$ would satisfy the following condition:\n",
      "<br>\n",
      "<div align=\"center\">$(2k+1)\\frac{\\theta}{2} = \\frac{\\pi}{2}$</div>\n",
      "<div align=\"center\">$(2k+1)\\phi = \\frac{\\pi}{2}$</div>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{k = \\frac{\\pi}{4\\phi} - \\frac{1}{2}}$</div>\n",
      "<br>\n",
      "Since it is logical for $k$ to be an integer, we can round it to the nearest integer using the floor function.\n",
      "<br>\n",
      "<div align=\"center\">$k = round(\\frac{\\pi}{4\\phi} - \\frac{1}{2}) = \\big\\lfloor \\frac{\\pi}{4\\phi} \\big\\rfloor$</div>\n",
      "<br>\n",
      "In worst case scenario there is only one $x$ which satisfies the oracle defined earlier, i.e., $M=1$.\n",
      "<div align=\"center\">$\\sin{\\phi} = \\langle \\beta | \\psi \\rangle = \\sqrt{\\frac{M}{N}} = \\sqrt{\\frac{1}{N}}$</div>\n",
      "<div align=\"center\">$\\Rightarrow \\phi = \\sqrt{\\frac{1}{N}} \\hspace{10 mm} (\\because \\sin{\\theta} \\approx \\theta$ for small $\\theta$ i.e., for large $N)$</div>\n",
      "<div align=\"center\">$\\Rightarrow \\boxed{k = \\bigg\\lfloor \\frac{\\pi \\sqrt{N} }{4} \\bigg\\rfloor}$</div>\n",
      "<br>\n",
      "Thus we need $O(\\sqrt N)$ Grover iterations for the algorithm to complete. The algorithm succeeds with probability $O(1)$."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###References\n",
      "\n",
      "####Papers\n",
      "[1] Lov K. Grover, *\"A fast quantum mechanical algorithm for database search\"*, Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC), pp. 212, 1996 [arXiv.org:quant-ph/9605043]\n",
      "\n",
      "[2] Lov K. Grover, *\"Quantum Mechanics helps in searching for a needle in a haystack\"*, Physical Review Letters, Volume 79, Number 2, pp. 325-328, 1997 [arXiv.org:quant-ph/9706033]\n",
      "\n",
      "[3] Samuel J. Lomonaco Jr., *\"Grover's Quantum Search Algorithm\"*, Proceedings of Symposia in Applied Mathematics, \n",
      "\n",
      "[4] Lov K. Grover, *\"Quantum computer can search arbitrarily large databases by a single query\"*, Physical Review Letters, pp 4709-4712, 1997.\n",
      "\n",
      "[5] C. Bennett, E. Bernstein, G. Brassard, and U. Vazirani, *\"Strengths and Weaknesses of Quantum Computing\"*, SIAM Journal of Computing, 26, 1510, 1997 [arXiv.org:quant-ph/9701001]\n",
      "\n",
      "####Books\n",
      "[6] M. A. Neilsen and I. L. Chuang, *\"Quantum Computation and Quantum Information\"*, Cambridge University Press, 2000.\n",
      "\n",
      "####Course notes\n",
      "[7] Dieter van Melkebeek, *\"Quantum Search\"*, CS880: Quantum Information Processing, University of Wisconsin-Madison, Fall 2010\n",
      "\n",
      "[8] Umesh Vazirani, *\"Quantum Search + Quantum Zeno effect\"*, CS191: Quantum Information, University of California-Berkeley, Spring 2012\n",
      "\n",
      "####Web links\n",
      "[9] *\"Cooley Tuckey FFT Algorithm*\" [http://en.wikipedia.org/wiki/Cooley-Tukey_FFT_algorithm]\n",
      "\n",
      "[10] *\"Discrete Fourier Transform\"* [http://en.wikipedia.org/wiki/Discrete_Fourier_transform]\n",
      "\n",
      "[11] *\"Fastest Fourier Transform in the West\"* [http://en.wikipedia.org/wiki/FFTW]"
     ]
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