task2.ipynb

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   "source": [
    "# Task 2\n",
    "Author: [Melissa Silva](https://github.com/melipass)\n",
    "\n",
    "## Introduction\n",
    "### What to do\n",
    "- Prepare 4 random 4-qubit quantum states of your choice.\n",
    "- Create and train a variational circuit that transforms input states into predefined output states. Namely:\n",
    "    - if random state 1 is provided, it returns state |0011>\n",
    "    - if random state 2 is provided, it returns state |0101>\n",
    "    - if random state 3 is provided, it returns state |1010>\n",
    "    - if random state 4 is provided, it returns state |1100>\n",
    "\n",
    "What would happen if you provided a different state? Analyze and discuss the results. For the variational circuit, you can try any circuit you want. You can start from one with a layer of RX, RY and CNOTs, repeated a couple of times (though there are certainly better circuits to achieve this goal). \n",
    "\n",
    "### Goals\n",
    "- Coding skills – clear, readable, well-structured code    \n",
    "- Communication – well-described results, easy to understand, tidy.\n",
    "- Reliability – submitted on time, all the points from the task description are met\n",
    "- Research skills – asking good questions and answering them methodically"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Melissa\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\__init__.py:86: DeprecationWarning: The package qiskit.aqua is deprecated. It was moved/refactored to qiskit-terra For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua', 'qiskit-terra')\n"
     ]
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       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.18.2</td></tr><tr><td><code>qiskit-aer</code></td><td>0.9.0</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.6.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.16.0</td></tr><tr><td><code>qiskit-aqua</code></td><td>0.9.5</td></tr><tr><td><code>qiskit</code></td><td>0.30.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.9.1 (tags/v3.9.1:1e5d33e, Dec  7 2020, 17:08:21) [MSC v.1927 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>31.917896270751953</td></tr><tr><td colspan='2'>Tue Sep 28 10:17:25 2021 Pacific SA Daylight Time</td></tr></table>"
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   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "## My solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit import Aer\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit import execute\n",
    "import random\n",
    "qs = Aer.get_backend('qasm_simulator')\n",
    "#qs.max_parallel_threads = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### State preparation\n",
    "\n",
    "First, I'll create four 4-qubit random states. Each of these random states will be used as an input for my circuit, and the outputs will be one of the given output states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0, 1, 1, 0]), array([1, 1, 1, 1]), array([1, 1, 1, 0]), array([0, 1, 0, 0])]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "# These were the states given at the introduction\n",
    "output_states = ['0011','0101','1010','1100']\n",
    "\n",
    "# Here, I'll store the input states\n",
    "random_states = []\n",
    "for i in range(len(output_states)):\n",
    "    state = np.random.randint(2, size=4)\n",
    "    random_states.append(state)\n",
    "\n",
    "print(random_states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The randomly generated states will be mapped to one of the expected output states:\n",
    "\n",
    "$$\n",
    "\\ket{0110} \\rightarrow \\ket{0011} \\\\\n",
    "\\ket{1111} \\rightarrow \\ket{0101} \\\\\n",
    "\\ket{1110} \\rightarrow \\ket{1010} \\\\\n",
    "\\ket{0100} \\rightarrow \\ket{1100}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The circuit\n",
    "In order to obtain the desired output states, I will be creating a variational circuit based on the hybrid quantum-classical framework DDQCL (Data-Driven Quantum Circuit Learning) designed by Benedetti et al. in '*A generative modeling approach for benchmarking and training shallow quantum circuits*', which can be accessed here: <https://www.nature.com/articles/s41534-019-0157-8>.\n",
    "\n",
    "Originally, this framework takes an input dataset $\\mathcal{D} = \\left( \\vec{x_1}, \\dots, \\vec{x_D} \\right)$ of randomly generated binary vectors, in my case the random states I generated above. Using this dataset, its goal is to obtain a good approximation to the target probability distribution $P_\\mathcal{D}$, in my case I aim for the best possible probabilities for each of the output states.\n",
    "\n",
    "To do so, I will be using Qiskit to create layers where I'll be applying parameterized rotation gates $R_X(\\theta)$ and $R_Z(\\theta)$ to each circuit, and then entangling every qubit with each other using parameterized $R_{XX}(\\theta)$ gates. This is based on the DDQCL paper, having parameterized single-qubit rotations $ \\{ \\theta_i^{(l,k)} \\} $ and two-qubit entangling rotations $ \\{\\theta_{ij}^{(l)}\\} $ in each layer, where the subscripts denote qubits involved in the operation, $l$ denotes the layer number, and $k\\in\\{1,2,3\\}$ denotes the rotation identifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up some global variables\n",
    "layers = 4\n",
    "qubits = 4\n",
    "measurements = 8000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameterizable $\\theta$ vector\n",
    "First, I will create a parameterizable $\\theta$ vector for each layer, in order to assign unique parameters to each gate during the circuit's creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def theta_vectors_generator(layers, qubits):\n",
    "    vectors = []\n",
    "    for layer in range(layers):\n",
    "        vector = ParameterVector('θ_{}'.format(str(layer)), qubits)\n",
    "        vectors.append(vector)\n",
    "    return vectors\n",
    "theta_vectors = theta_vectors_generator(layers, qubits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DDQCL circuit\n",
    "Now, I'm creating the quantum circuit based on the DDQCL design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ddqcl(layers, qubits):\n",
    "    qr = QuantumRegister(qubits)\n",
    "    cr = ClassicalRegister(qubits)\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    for layer in range(layers):\n",
    "        for qubit in range(qubits):\n",
    "            qc.rx(theta_vectors[layer].params[qubit], qubit)\n",
    "            qc.rz(theta_vectors[layer].params[qubit], qubit)\n",
    "            for other_qubit in range(qubit+1, qubits):\n",
    "                qc.rxx(theta_vectors[layer].params[qubit], qubit, other_qubit)\n",
    "        qc.barrier()\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating initial parameters\n",
    "The function below creates random parameter values between $\\pi$ and $-\\pi$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def theta_values_generator(layers, qubits):\n",
    "    parameters = []\n",
    "    for layer in range(layers):\n",
    "        for qubit in range(qubits):\n",
    "            for i in -(qubit+1)*np.random.uniform(-np.pi,np.pi,[1]):\n",
    "                if i < 3.14 and i > -3.14:\n",
    "                    parameters.append(i)\n",
    "                elif i > 0:\n",
    "                    parameters.append(3.14)\n",
    "                else:\n",
    "                    parameters.append(-3.14)\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost function\n",
    "\n",
    "Based on the cost function proposed by Benedetti et al., which is:\n",
    "\n",
    "$$\n",
    "\\mathcal{C}(\\theta)=-\\frac{1}{\\text{D}}\\sum_{d=1}^D \\ln{(\\max{(\\varepsilon, P_{\\theta}(\\vec{x_d}))})}\n",
    "$$\n",
    "\n",
    "where $D$ is the total number of qubits, $d$ is the current qubit, $\\varepsilon$ is a very small value in order to avoid having zeroes inside the logarithm, and $P_{\\theta}$ are the output probabilities for a given vector. I will be implementing the following cost function, which closely resembles the equation above but is not the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_function(qubits, epsilon, random_states, parameters):\n",
    "    sum_of_ln = 0\n",
    "    for i in range(qubits):\n",
    "        counted_state = get_counts_for_state(random_states[i], parameters)\n",
    "        sum_of_ln += np.log(np.maximum(epsilon, counted_state.get(str(output_states[i]), epsilon)/measurements))\n",
    "    result = (-1/qubits)*sum_of_ln\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other functions\n",
    "\n",
    "In order to streamline the parameter optimizing phase, where the cost function will be calculated several times based on the inputs given to the circuit, I will leverage the following functions.\n",
    "\n",
    "- The first function creates a DDQCL circuit with a specific input state before the rotation layers.\n",
    "- The second function takes the circuit made in the first function and assigns parameter values to it, measuring and returning the output states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialized_ddqcl(state_vector, layers, qubits):\n",
    "    qr = QuantumRegister(qubits)\n",
    "    cr = ClassicalRegister(qubits)\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    for i in range(len(state_vector)):\n",
    "        if state_vector[i] == 1:\n",
    "            qc.x(i)\n",
    "    for layer in range(layers):\n",
    "        for i in range(qubits):\n",
    "            qc.rx(theta_vectors[layer].params[i], i)\n",
    "            qc.rz(theta_vectors[layer].params[i], i)\n",
    "            for j in range(i+1, qubits):\n",
    "                qc.rxx(theta_vectors[layer].params[i], i, j)\n",
    "        qc.barrier()\n",
    "    return qc\n",
    "\n",
    "def get_counts_for_state(state, parameters):\n",
    "    ddqcl = initialized_ddqcl(state, layers, qubits)\n",
    "    ddqcl = ddqcl.bind_parameters(parameters)\n",
    "    ddqcl.measure(0,0)\n",
    "    ddqcl.measure(1,1)\n",
    "    ddqcl.measure(2,2)\n",
    "    ddqcl.measure(3,3)\n",
    "    ddqcl.draw('mpl')\n",
    "    ddqcl_job = execute(ddqcl, qs, shots = measurements, seed = 1000)\n",
    "    ddqcl_result = ddqcl_job.result()\n",
    "    ddqcl_counts = ddqcl_result.get_counts()\n",
    "    return ddqcl_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter optimization\n",
    "I created an optimizer where the delta variable changes the circuit paremeters over time, morphing according to the number of iterations without finding a successful set of parameters that lowers the cost. There's further explanation in the comments inside the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost: 4.51282049377583 at iteration 0 with parameters:\n",
      "[2.0892086657955846, -1.687862542319067, 3.2555101649591003, -2.935565744512677, -2.1225673000673737, -0.9579662547495005, 1.6585822792531089, 3.1076155984559355, -1.4250249989577617, 0.37220212574204453, -2.832275958694854, -3.2545787882731743, -2.9132896188800452, -2.8373988039967, 3.3310149212950435, 2.955851745583983]\n",
      "Cost: 3.173555736628564 at iteration 1 with parameters:\n",
      "[2.403367931154564, -1.3737032769600876, 3.5696694303180796, -2.6214064791536975, -1.8084080347083944, -0.6438069893905212, 1.9727415446120882, 3.421774863814915, -1.1108657335987824, 0.6863613911010238, -2.5181166933358745, -2.940419522914195, -2.599130353521066, -2.523239538637721, 3.645174186654023, 3.2700110109429623]\n",
      "Cost: 2.858992811388333 at iteration 2 with parameters:\n",
      "[2.7175271965135432, -1.0595440116011083, 3.883828695677059, -2.307247213794718, -1.494248769349415, -0.32964772403154186, 2.2869008099710673, 3.735934129173894, -0.7967064682398031, 1.000520656460003, -2.203957427976895, -2.6262602575552156, -2.2849710881620866, -2.2090802732787416, 3.959333452013002, 3.5841702763019416]\n",
      "Cost: 2.7168191451128956 at iteration 5 with parameters:\n",
      "[2.4453012511207786, -0.8240260969986654, 4.131721762414418, -2.210818746877954, -1.635124127474893, -0.17018698615885897, 2.0125340648149117, 3.8118170037923584, -1.0215524625209642, 1.1169272552095104, -2.1364669993028333, -2.915406683015275, -1.9898931702202427, -2.140280971314063, 3.9918065776010474, 3.730912685590054]\n",
      "Cost: 2.285922824593045 at iteration 15 with parameters:\n",
      "[2.3486880666315857, -0.7871777525494705, 3.93366000103865, -2.18254309435611, -1.8370012689703796, -0.3259100157044733, 2.24550047685169, 4.060684984754269, -1.3333072051595403, 0.8478586241001671, -2.1851140687684794, -2.6874599551779816, -1.9034200564146446, -2.342336877553882, 4.292329317874344, 3.782405514394098]\n",
      "Cost: 2.2651157720350175 at iteration 19 with parameters:\n",
      "[2.296508746312921, -0.8717160883324833, 3.724497697254626, -1.9109547111984262, -2.0035143721308235, -0.5114590734799829, 2.3678806031718347, 4.085857015491496, -1.6149147694521755, 0.8326869675042275, -2.4648223254894535, -2.9582137996046205, -2.211232663753663, -2.473891954878819, 4.116740367188759, 3.9060129594189887]\n",
      "Cost: 1.957689603511252 at iteration 24 with parameters:\n",
      "[2.4889464716247303, -0.6927315104592873, 3.8441349189057723, -1.740583022900699, -2.1197463505246423, -0.3522931702310839, 2.6552703372652786, 3.9228218738342315, -1.6743724018913924, 0.7028063057943184, -2.1588529415718662, -3.1386153472734226, -2.1729201135451675, -2.2160064375695705, 3.880149085630283, 3.8634431059179124]\n",
      "Cost: 1.8787041026316493 at iteration 27 with parameters:\n",
      "[2.4974492140177866, -0.7127607339843212, 3.691707835635707, -1.7624937825373108, -2.400445706107592, -0.22005954611881468, 2.879008026583139, 3.7850915172507773, -1.9866474294604877, 0.8081680067641099, -1.9177512049486842, -3.2165959702686426, -2.4686667913777156, -2.400782237045271, 3.939551203948375, 3.935660567096794]\n",
      "Cost: 1.6532772562360147 at iteration 31 with parameters:\n",
      "[2.6036346999383664, -0.5973037440344774, 3.8216444486138195, -1.647402020031964, -2.6940028726015273, -0.006647903474874706, 2.6358638945065334, 3.511782388950735, -2.1715240039149695, 0.8857084604482313, -1.8104837434174437, -3.2698110838139876, -2.356570334490821, -2.5764204756921445, 3.7356414201468686, 4.043325661937791]\n",
      "Cost: 1.58474908562305 at iteration 44 with parameters:\n",
      "[2.8263349523850287, -0.3748742297887192, 3.8697112821767274, -1.5028447480732747, -3.001794938583279, -0.3202873608152635, 2.3660198006513005, 3.79819639140787, -1.936593623648636, 1.1659303523160327, -2.084164630255843, -3.261754000539747, -2.5284471092600214, -2.2941586967066585, 3.856994526739689, 4.127489637043529]\n",
      "Cost: 1.5366035905218516 at iteration 60 with parameters:\n",
      "[2.861756112622081, -0.12685878475119972, 4.101819137609368, -1.3923155344055185, -2.7873017633026613, -0.3089157357416517, 2.375910033456603, 3.570581286959888, -2.2085917937770483, 1.0741140188870355, -2.086223761853686, -3.424076391718841, -2.5389623616476533, -2.2786032002159944, 3.936750910152992, 3.980700929789783]\n",
      "Cost: 1.5261283276569988 at iteration 93 with parameters:\n",
      "[2.717488524096857, -0.41897040490207, 3.8064607181348618, -1.122332681593622, -2.7102575307564343, -0.11907428350792798, 2.296765399555621, 3.582384338993122, -2.4025735712351985, 1.159619248891862, -1.8013460078690096, -3.1968861300136258, -2.6845380218515937, -2.252473237500591, 3.7965536380867455, 4.149324303654488]\n",
      "Cost: 1.5232132793137334 at iteration 98 with parameters:\n",
      "[2.8682043267235477, -0.5345036519331843, 4.053445654882222, -1.4235134551284858, -2.813646767374004, -0.34302909918332947, 2.2584278191551714, 3.7652666608265384, -2.5580659068411893, 1.2416366890008768, -1.916010977308683, -3.3146627173992504, -2.5460186648049894, -2.3857463906492704, 3.5904191417176317, 4.310943447533735]\n",
      "Cost: 1.4434447498094638 at iteration 120 with parameters:\n",
      "[3.024763781087371, -0.5466294884020443, 3.914746339805672, -1.2632270045256353, -2.542480404222544, -0.3125172544160584, 2.510724574981797, 3.6201486552275117, -2.4336560123050663, 1.174750949214567, -2.0102763488105677, -3.5595563293237937, -2.5898410979809157, -2.225566848995205, 3.659940986254075, 4.555152234138192]\n",
      "Cost: 1.4423952004047798 at iteration 137 with parameters:\n",
      "[2.9028581411768553, -0.32077364301963307, 3.813559598828781, -1.493466793421756, -2.7968403092427163, -0.33782235626675744, 2.5722415924368605, 3.48716798380309, -2.391922236632849, 0.8888186188260148, -2.2217462346398498, -3.7328496686225643, -2.605980790085994, -2.495678603121229, 3.8712257318160437, 4.641942710237808]\n",
      "Cost: 1.4119346037651628 at iteration 152 with parameters:\n",
      "[3.012591516685581, -0.2646456700630051, 3.9847497775372096, -1.3327342863069702, -2.887220384811314, -0.4176157201474611, 2.272276183413479, 3.653342065874632, -2.5296009029510094, 0.8996783154816452, -2.0935437535573436, -3.425542896588701, -2.523205887361047, -2.5110420048296374, 4.096240129944696, 4.544842662873488]\n",
      "Cost: 1.208440401881508 at iteration 155 with parameters:\n",
      "[2.951430549421874, -0.4178996096274547, 3.7336070126071856, -1.4208905656621298, -2.8285769952856996, -0.48388514615569744, 2.356494993020468, 3.4706201229821456, -2.2296535153667465, 0.6763836764774919, -2.402303920664544, -3.500939534709141, -2.62642508233486, -2.6564136126444655, 4.229210369251055, 4.725918822595509]\n",
      "Cost: 0.9969466220449429 at iteration 174 with parameters:\n",
      "[3.0408071527845895, -0.70356820976921, 3.891460117784125, -1.231894168806988, -2.648512741032497, -0.33690269966255487, 2.2575191784942956, 3.4047761763428155, -2.188043156007221, 0.7183054838349662, -2.64498486946546, -3.225463832210953, -2.825142257078421, -2.925553088234206, 4.393411755445728, 4.934910678999674]\n",
      "Cost: 0.9744577565848953 at iteration 266 with parameters:\n",
      "[3.1041780706290223, -0.6231401068688178, 3.7682441089238368, -1.1787950934146902, -2.4950413785719654, -0.6232900243744322, 2.058933329361298, 3.4988879369824213, -2.2161969919373283, 0.5614672317755482, -2.821441676619647, -3.045927120428843, -2.756033137725986, -2.7359609869192516, 4.6501041413281285, 5.0207825889505715]\n",
      "Cost: 0.9533600603610837 at iteration 276 with parameters:\n",
      "[2.8764155038985333, -0.5562790802363518, 3.9383980305289024, -1.3944538305439373, -2.625140965177084, -0.5402082912962037, 1.8766746786469422, 3.3324964267833592, -2.424696729196722, 0.8074464331092159, -2.935581434658104, -3.3547153818807565, -2.9047250624290157, -2.8636277319501433, 4.628540467909923, 4.945163955076356]\n",
      "Cost: 0.8799297276443356 at iteration 334 with parameters:\n",
      "[2.9449627242176684, -0.5252591943108851, 3.714270695843579, -1.6801073381028406, -2.7234991308898606, -0.4141295525939905, 1.7981227409484553, 3.197794282935951, -2.168530345263578, 0.9967689119475648, -2.925348312973512, -3.275726350230601, -2.793226971898355, -2.941011362752987, 4.478453174267283, 4.990192650675661]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1670.93x2492.28 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_optimizer(current_parameters, delta, iterations, epsilon, best_cost):\n",
    "    cost_history = [] # Array to store all successful cost reductions\n",
    "    generated_parameters = []\n",
    "    original_delta = delta # Variable to freely modify the delta value and be able to come back to its original value later\n",
    "    generated_parameters.append(current_parameters) # Array to store all the successful parameters\n",
    "    better = 2 # Variable that I'll use to switch between different deltas if change was successful or not\n",
    "\n",
    "    for i in range(iterations):\n",
    "        cummulative_iterations = 0 # Variable to count how many iterations have passed without improving the cost\n",
    "        new_generated_parameters = []\n",
    "\n",
    "        # Here, I'm applying different deltas to the last successful generated parameters based on the previous run's success\n",
    "        for j in range(qubits*layers):\n",
    "            if better == 1: # successful previous parameters\n",
    "                new_generated_parameters.append(generated_parameters[-1][j] + delta)\n",
    "            elif better == 0: # unsuccessful previous parameters\n",
    "                new_generated_parameters.append(generated_parameters[-1][j] - delta)\n",
    "            else: # \n",
    "                new_generated_parameters.append(generated_parameters[-1][j] + random.uniform(-delta, delta))\n",
    "\n",
    "        new_cost = cost_function(qubits, epsilon, random_states, new_generated_parameters)\n",
    "\n",
    "        # Successful cost reductions are stored and variables updated, otherwise deltas are modified\n",
    "        if new_cost < best_cost:\n",
    "            cost_history.append(new_cost)\n",
    "            generated_parameters.append(new_generated_parameters)\n",
    "            print(\"Cost: \" + str(new_cost) + \" at iteration \" + str(i) + \" with parameters:\")\n",
    "            print(new_generated_parameters)\n",
    "            best_cost = new_cost\n",
    "            better = 1\n",
    "            cummulative_iterations = 0\n",
    "            delta = original_delta\n",
    "        elif better == 1:\n",
    "            cummulative_iterations = cummulative_iterations + 1\n",
    "            better = 0\n",
    "        else:\n",
    "            better = 2\n",
    "            cummulative_iterations = cummulative_iterations + 1\n",
    "            # Depending on how many iterations have passed without reaching better parameters,\n",
    "            # the delta value will be multiplied by the values below. If too many iterations pass\n",
    "            # without reaching better parameters, the optimizer returns the best circuit it found.\n",
    "            if cummulative_iterations == 20:\n",
    "                delta = delta * 1.5\n",
    "            if cummulative_iterations == 40:\n",
    "                delta = delta * 1.5\n",
    "            if cummulative_iterations == 60:\n",
    "                delta = delta * 0.2\n",
    "            if cummulative_iterations == 80:\n",
    "                delta = original_delta\n",
    "            if cummulative_iterations == 100:\n",
    "                parameterized_best_circuit = ddqcl(layers, qubits).bind_parameters(generated_parameters[-1])\n",
    "                return parameterized_best_circuit, generated_parameters[-1], cost_history\n",
    "    parameterized_best_circuit = ddqcl(layers, qubits).bind_parameters(generated_parameters[-1])\n",
    "    return parameterized_best_circuit, generated_parameters[-1], cost_history\n",
    "\n",
    "\n",
    "last_circuit, best_parameters, cost_history = my_optimizer(theta_values_generator(layers,qubits), np.pi/10, 500, 0.000001, 10)\n",
    "last_circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Benchmarking\n",
    "Although the optimizer I created is nothing special, here I'm displaying how much the cost decreased everytime it found better parameters. This optimizer can be improved further, since its rules were set by me by trial and error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.linspace(0,len(cost_history),len(cost_history)), cost_history)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output values for input state [0 1 1 0] which should give 0011:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_0 = get_counts_for_state(random_states[0], best_parameters)\n",
    "print(\"Output values for input state \" + str(random_states[0]) +\" which should give \" + output_states[0] + \":\")\n",
    "plot_histogram(counts_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output values for input state [1 1 1 1] which should give 0101:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_1 = get_counts_for_state(random_states[1], best_parameters)\n",
    "print(\"Output values for input state \" + str(random_states[1]) +\" which should give \" + output_states[1] + \":\")\n",
    "plot_histogram(counts_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output values for input state [1 1 1 0] which should give 1010:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_2 = get_counts_for_state(random_states[2], best_parameters)\n",
    "print(\"Output values for input state \" + str(random_states[2]) +\" which should give \" + output_states[2] + \":\")\n",
    "plot_histogram(counts_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output values for input state [0 1 0 0] which should give 1100:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_3 = get_counts_for_state(random_states[3], best_parameters)\n",
    "print(\"Output values for input state \" + str(random_states[3]) +\" which should give \" + output_states[3] + \":\")\n",
    "plot_histogram(counts_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.9449627242176684, -0.5252591943108851, 3.714270695843579, -1.6801073381028406, -2.7234991308898606, -0.4141295525939905, 1.7981227409484553, 3.197794282935951, -2.168530345263578, 0.9967689119475648, -2.925348312973512, -3.275726350230601, -2.793226971898355, -2.941011362752987, 4.478453174267283, 4.990192650675661]\n"
     ]
    }
   ],
   "source": [
    "print(best_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Measurement of other states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output values for input state [1, 0, 1, 1] which should give an unknown state:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_other = get_counts_for_state([1,0,1,1], best_parameters)\n",
    "print(\"Output values for input state \" + str([1,0,1,1]) +\" which should give an unknown state:\")\n",
    "plot_histogram(counts_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output values for input state [0, 0, 0, 1] which should give an unknown state:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_other = get_counts_for_state([0,0,0,1], best_parameters)\n",
    "print(\"Output values for input state \" + str([0,0,0,1]) +\" which should give an unknown state:\")\n",
    "plot_histogram(counts_other)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "### Results\n",
    "Although not always perfect, all the different runs of the optimizer that I did gave a minimum of **three clearly correct answers**, such as the ones seen above, with a **fourth answer being not as clear**. Also, **there are runs where all 4 states are obtained successfully**.\n",
    "\n",
    "Something I didn't explore much into were the hyperparameters such as $\\varepsilon$ for the cost function, or the number of layers the circuit should have. What I have right now is close to the perfect answer but not there yet (that is, the four states correctly mapped without other high probability states that could cause confusion), I'm sure that **tuning the hyperparameters further could lead to a better circuit and better outputs**.\n",
    "\n",
    "When measuring states that don't belong in our randomized inputs set, there tends to be two output states with noticeably high probability.\n",
    "\n",
    "### Optimizer\n",
    "There's a lot of room for improvement, with the right rules **it could take considerably less iterations to sustantially lower the cost**.\n",
    "\n",
    "One thing I believe could be beneficial is: when a successful parameter set that lowers the cost is found, try adding randomized values to each element with the same sign as the previous addition ( $+$ or $-$ ), seeing if going further that way in different proportions is useful or not; if that doesn't work, try adding the same numbers with switched signs ( $-$ or $+$ ).\n",
    "\n",
    "### Cost function\n",
    "One final thing I believe could be interesting to try is to **measure the cost differently for each input state's circuit**. Something I noticed when measuring the outputs of the initialized circuits is that wrong measurements usually end up being in both 0011 and 1100, or both 1010 and 0101, but not a mix of them. This leds me to believe that these states are treated a specific way by the qasm_simulator or Qiskit.\n",
    "\n",
    "One idea I have is **transpiling the code into basic gates for a quantum computer with a known topology and study how the qubits interact, seeing the relations between the different connections they have to each other**. This way, I think I could weight each mapped qubit's cost function result and sum those values as a circuit cost function, so to say, to consider some results better (with less added cost) than others.\n",
    "\n",
    "Another idea I have is to read the states that I don't want and make a decision based on that. Let's say that I'm getting high probabilities on 1010 and 1110, and I'm only expecting to see 1010: **what if I make wrong results with a high probability** (such as 1110) **more costly than less-probable wrong results, to avoid getting that state in my final circuit?**\n",
    "\n",
    "## Thanks\n",
    "Just wanted to say thanks to the Quantum Open Source Foundation for preparing these tasks. Due to very limited time, I wasn't able to try the other tasks during the 2-week period, but I chose this one, learned a lot and I'm happy with the results. I will definitely be improving this notebook in the future, but I will leave the main branch at GitHub as the untouched submission within the time period until the chosen candidates are announced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Benedetti, M., Garcia-Pintos, D., Perdomo, O. _et al._ A generative modeling approach for benchmarking and training shallow quantum circuits. _npj Quantum Inf_ **5,** 45 (2019). <https://doi.org/10.1038/s41534-019-0157-8>"
   ]
  }
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