src/examples/1_editing_and_masking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69a855f1-55dd-482e-94f2-9ad02804be4d",
   "metadata": {},
   "source": [
    "# Editing and masking of circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41e2465-49d8-46b8-b046-6ae1becfb268",
   "metadata": {},
   "source": [
    "In this notebook we show editing and masking of circuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bde494e-9091-41a4-a601-bbcf9712c564",
   "metadata": {},
   "outputs": [],
   "source": [
    "from genQC.imports import *\n",
    "from genQC.pipeline.diffusion_pipeline import DiffusionPipeline\n",
    "from genQC.inference.infer_srv import convert_tensors_to_srvs, schmidt_rank_vector\n",
    "import genQC.platform.qcircuit_dataset_construction as data_const\n",
    "from genQC.platform.simulation.qcircuit_sim import instruction_name_to_qiskit_gate\n",
    "import genQC.util as util\n",
    "from qiskit.quantum_info import DensityMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "029be4f3-0d9a-4d0a-93d9-2338fda7a983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.\n"
     ]
    }
   ],
   "source": [
    "device = util.infer_torch_device()  # use cuda if we can\n",
    "util.MemoryCleaner.purge_mem()      # clean existing memory alloc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77a3020-247c-4ac0-aaf1-ee5c371b5f06",
   "metadata": {},
   "source": [
    "## Setup and load"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742ae430-46f2-4099-ac8f-f422a4ddc1dc",
   "metadata": {},
   "source": [
    "Load the pre-trained model directly from [Hugging Face: Floki00/qc_srv_3to8qubit](https://huggingface.co/Floki00/qc_srv_3to8qubit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5d60c23-9514-4432-bc82-622c088fced6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1c55308f7c94327b24b9df417f05117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: `genQC.models.unet_qc.QC_Cond_UNet` instantiated from given config on cuda.\n",
      "[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder` instantiated from given config on cuda.\n",
      "[INFO]: `genQC.models.frozen_open_clip.CachedFrozenOpenCLIPEmbedder`. No save_path` provided. No state dict loaded.\n"
     ]
    }
   ],
   "source": [
    "pipeline = DiffusionPipeline.from_pretrained(\"Floki00/qc_srv_3to8qubit\", device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431d3e29-f121-4c61-95bc-bfd7960a4870",
   "metadata": {},
   "source": [
    "Set 20 sample steps and use rescaled guidance-formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96702fba-5a10-44e6-bef9-634d9e41a1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.guidance_sample_mode = \"rescaled\"\n",
    "pipeline.scheduler.set_timesteps(40) \n",
    "g = 7.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65acff8f-8486-42c9-8e78-b44f31de568b",
   "metadata": {},
   "source": [
    "## 1. Editing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16e6870-b895-4acd-96e3-135a96979d9e",
   "metadata": {},
   "source": [
    "Sample a random circuit with desired parameters as the circuit we want to edit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "143ee84d-3c02-4d9c-ae76-b6290efaec34",
   "metadata": {},
   "outputs": [],
   "source": [
    "srv_init       = [1, 1, 1, 2, 2]   # psi_0 state\n",
    "desired_length = 5                 # 5 gates initially placed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d94961d3-b053-4064-8845-28320927e05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SRV is [1, 1, 1, 2, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 371.107x451.5 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gate_pool = [instruction_name_to_qiskit_gate(gate) for gate in pipeline.gate_pool]\n",
    "init_qc   = data_const.get_specific_rnd_srv_circuit(srv_init, desired_length, gate_pool)\n",
    "print(\"SRV is\", schmidt_rank_vector(DensityMatrix(init_qc)))\n",
    "init_qc.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9faf40c0-6535-4c2c-b62e-7e8ea6c37263",
   "metadata": {},
   "source": [
    "The editing taks is analogous to image editing, we do img2img with conditioning and copy non-edit areas at every time step. Also called `latent_filling`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f03e2d7d-8dcb-4c6a-82d3-c1525ae5f5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_edited_circuits(pipeline, samples, qc, prompt, new_length, num_of_qubits, system_size, t_start_index):\n",
    "    #-------------------------------------------\n",
    "    # set mask - appending mask!\n",
    "    old_length = len(qc.data)\n",
    "\n",
    "    qubit_mask = torch.ones((system_size, new_length), device=device)\n",
    "    qubit_mask[:, :old_length] = 0\n",
    "    \n",
    "    #-------------------------------------------\n",
    "    # prepare and encode\n",
    " \n",
    "    gate_classes = data_const.gate_pool_to_gate_classes(gate_pool)\n",
    "   \n",
    "    emb_org_image = data_const.encode_circuit(qc, system_size, gate_classes, new_length).unsqueeze(0).to(device)\n",
    "    emb_org_image = pipeline.model.embedd_clrs(emb_org_image)\n",
    "\n",
    "    emb_org_images = emb_org_image.repeat(samples, *[1]*(emb_org_image.dim()-1))\n",
    "    \n",
    "    #-------------------------------------------\n",
    "    # prep condition\n",
    "    \n",
    "    c = pipeline.text_encoder.tokenize_and_push_to_device(str(prompt))\n",
    "    c = c.repeat(samples, *[1]*(c.dim()-1))\n",
    "\n",
    "    #-------------------------------------------\n",
    "    # latent fill\n",
    "    out_tensor = pipeline.latent_filling(emb_org_images, qubit_mask, c=c, g=g, no_bar=False, t_start_index=t_start_index)\n",
    "    out_tensor = pipeline.model.invert_clr(out_tensor)\n",
    "    out_tensor = out_tensor[:, :num_of_qubits]\n",
    "    out_tensor = torch.unique(out_tensor, dim=0) # we only are interested in unique circuits\n",
    "   \n",
    "    qc_list, error_cnt, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool, place_barrier=True)\n",
    "\n",
    "    return qc_list, srv_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c3b99db-e321-4b03-a47f-1ce753495eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Generate SRV: [2, 2, 2, 2, 2]'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples    = 16   # how many circuits we sample\n",
    "new_length = 16   # how many gates the model can place \n",
    "\n",
    "srv_target    = [2, 2, 2, 2, 2]  # desired target SRV\n",
    "\n",
    "num_of_qubits = len(srv_target)\n",
    "t_start_index = t_start_index = int(0.05 * pipeline.scheduler.timesteps.shape[0])  # time step index at which we start denoising\n",
    "\n",
    "prompt = f\"Generate SRV: {srv_target}\"  # model was trained with this phrase\n",
    "prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "303b9d6a-7611-4664-b9d7-424d068f974e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7748783c52314ec895ba506bd45150b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/38 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# returns only distinct circuits\n",
    "edited_qc, srv_list = create_edited_circuits(pipeline, samples, init_qc, prompt, new_length, num_of_qubits, num_of_qubits, t_start_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5bfeee-4693-4110-9500-fb03c06ac2f3",
   "metadata": {},
   "source": [
    "Pick only correct ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "313bab5f-8c99-43e4-9ae3-7305a5ac4aae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 12 correct distinct solutions.\n"
     ]
    }
   ],
   "source": [
    "correct_edited_qc = []\n",
    "for qc,srv in zip(edited_qc, srv_list):\n",
    "    if srv==srv_target: correct_edited_qc.append(qc)\n",
    "print(f\"We found {len(correct_edited_qc)} correct distinct solutions.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd981467-9c73-43fa-8dec-3fa4aafc50d3",
   "metadata": {},
   "source": [
    "Compare: initial circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "156f7aad-d1d6-4cf9-bd9e-8fd1908e687f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 371.107x451.5 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_qc.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4189622c-47c1-4d16-b153-b3bb55d25f63",
   "metadata": {},
   "source": [
    "v.s. edited:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b383e40-e2bc-4166-a363-45a8cf6e2b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SRV is [2, 2, 2, 2, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 872.774x451.5 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"SRV is\", schmidt_rank_vector(DensityMatrix(correct_edited_qc[0])))\n",
    "correct_edited_qc[0].draw(\"mpl\", plot_barriers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97f2bf73-8cab-4248-88b2-810b58cfc796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uEq62zat4NENkVJr+2RWnzCyrflpyWJe3iVCjuiEezVCROBwOrd0Wq09m7tbBY6kym226+t5FurlnA91zUzOFhfp7OyJQplitdi36+7i+mr1P0bGZcjgcql4lUMNubKZb+jSUv5/J2xGBCslmsystwyK7g8uw4B4Wi10ZWRZVCvKVj0/F+g0BAEBp44iJlQ4eds22XLIVoGyp0MVEm82un3+P1LivtulYTIasVrsysqy695WVksGgLu2q66UHL9FVl9X0dlSgwkjPtOirOfs0cfoOpWdaZbbaZbc75PdvAW9gr4Z6/r62atEozG0ZTiVk6ePvdmnyrL2y2RzKMduUlWPT4Kf/lMEgPXhLSz01pI1qRZTvCw427IjT2C+3avk/JyVJFotNJpNRvj5G1awWqOfvb6c7r2vituKqw+HQr6ui9NYXW7XrUJLsdofSM626/7W/ZTQadFGTcL00/BJdd2VdGQwGt2SoiL5bdFCvfLJZ8cnZSs+06Mz51RWbYvTPrni9NGmjBvRqqIlPd1SNqoHeDQuUcjabXW9P3a4Pvtkps/V00eJsa7fF6pG3VuuhW1tqzIj2CvCv0F/NAY+w2x1ati5aYz4//f3Cx2RQYqpZDa75QU8NvUj3D2iu0Ep+3o6JMiw7x6qZvx7W21O2KT4pR0ajZLM7VL9mJb380CW66eoGFe7iRADed/REmiZ8vUN/bjipw8dTdfkd8/TQrS111/VNFBjAd1CUHhaLXbOXRerj73frVEKWTsRm6t5XVrr9XCDKvswsq75deEBfztqnpFSzAvxN6tO5tkYPu1j1albydrxyocL+t8jItOiGx37Xxt0JSs/Me2InLdMqSVqyJlprt8XqocEt9c5Tl3OyGnCz4zHp6nbPIp1KyFJWji3Pfdn/3v5mwQHN+eOIPn+5q+64vonLM2zaHa++D/+mtAyLzBZ7nvuS08ySpA++3amv5uzTok+uUad21V2eoTR464stmvD1DqWmW8655/Rrkpxm1mPj1uizH3br98nXqXKIa0+6Wa12DX1phRavPK7Uc06+p//7Gb1mW6zueHa5+nWrp2/Gdudq7xJyOBwaPWG9vpi9TxlZVqePycw+vfyH3w5r2bporZx2g5o3rOzJmECZkZ1j1Q2PLdXabbG5x8650v79Djpp5m79vjZaf025np6/gBudSshSrwcWKzo2M/d73RnHYjL00scbNfbLrfp2XHddd2U9L6VEWbZpd7xuGPG70rMsud9Zz0hKTdT9r/6tp0M3aNmX16lp/VAvpQRQkWRkWjT46T/1z654JaRky/7vaY6Nu+K1+/A6vTxpo1596FI9entr7wYFJP38+2E9+fY6pWXm/T/69bwDWrTyuFo0rKx5H/dRlcr8ZkJeH367U+OnbFNapkVZ2f+dU957JFnfLz6szu2q64d3e3DxRAlVyDOvVqtd1z6yRGu3x+YrJJ4rNcOi//24Ry9P2uihdEDFFJ+Urc5DFuhYTHq+QuLZ7HYpNd2ih99crbl/HnVpht2HktRn+K9KSM7JV0g8m8VqV2JKjq57dIm27090aYbS4N2p2/TuNGeFxLzSM63asjdRPe5frBxzwe9ZUTkcDg198S/NX34sXyHxXKkZFs1bfkxDX1ohB0OUlciYz7ect5B4NpvNodjEbF11z0LFxGd6IB1QttjtDg0a9YfWbD1VYCHxbNk5Nu05nKw+w39z6ecpgP8kpuSo413ztScyOV8h8YysHJsSknN053N/6bdVUR5OiLJu694E9X3oN8UkZOUrJJ6RmmHR0ZPpunLYQh2JTvNwQgAVTVa2VV2HLdTS9dGKS/qvkHhGZpZVpxKy9dKkTRr/1TbvhAT+9fXc/XpozGqdjHf+fzQuKVtrtp1SxzvnKTk1xwsJUVq99tkmvf6/zYpNzM5TSJROn0eOS8rWb6ujdNU9i/i9XUIVspj46Y+7tXlvgnLM+YsF+xcMyrcsPdOqT2bu0da9CZ6IB1RIj7y1WqcSsvJ9uZWcH5epGRbd8/LKC14QUBQDR/6hpFTnJ5ecZUhOM+vmJ5eVqyLWwWOpGvvltnxD8Z1x7utgsdq1NzJZ70x13Q+PhSuOa/HfUU5PwDt7HzKzrVq88rgWrTzusgwVzaHjqXp32vYCC4np6+/Ot8zhkBJScvTkO+vcHQ8oc+b+eVQrNsUUeHGMs2PKbLFr9+Fkff7THnfHAyqke19ZqahTGU6/a66afkOe28lpZg198S+Xfs9E+Wa3O3TjE0uVkJL/5Oa5+5d0upfszU8u80Q0ABXY42+v1Z7DybJY8p6zmD+pT57byWlmvTd9hzbvjvdkPCDXsZPpeub9DU7PyZ29v9rtUmR0uu58/i8PpkNptm5brCZ9v1spTjpEnL3vmC127TqYqNHvrfdkvHKnwhUTHQ6H3vt6hzILOGEaHOi8q2tapkXvTtvuzmhAhZWYkqMla6JltTkvyhV0XFptdn274KBLMvyzM06nErIKvL+gDEmpOVq7LdYlGUqD97/Zcd6eac5eh6wcmz6ZuUc2W8G9OYvirS+3FtgjsaD3ITXDorFfbnVJ+xXRRzN2nbc3rsnofJhvm82hBSuOKdHJiTOgInt7yrYCe6VIBR9TmdlWTfh6R7m6SAUoDeISs7RqS4xsBXzXDA/NP1x7ZrZV37joeybKv6Vrowu8GM/Z/iVJx09laIcbRjlJSTNrwFPL1Pi6H3XwWIpufnIpPTjgUnsjk9X17gVqeO0PanbDzxr5zjqX/RaE62RkWrTgr2NOf+dFhAfkW5aYkqMxk7d4IhqQz4SvtyuxgP9V5+6vNrtDG3fFM0oSJEmvfba5wI4h5+472Wa7Zv0eqeycC48eBOdKXTHRarXqiSee0AsvvKA+ffpo3TrX9nhYuSnmvCd3CuJwSItWHldKAUPiACi+L2fvlcVa9B8fGVlWveOiIv+707YXOOTV+SSnmcvNcCBZ2VbN/PWwbPain8Q2W2xa/HfJhwM7eCxV+yKTi7Xu7sPJOnQ8tcQZSpNx48ape/fueuKJJ3Tfffe5pY2sbKumzTtQYDG/MKb8ss+FiYCybW9ksnYfTi72+qkZFv31z0nXBQLKiKysLD366KMaNWqUevTo4dJt/++nvUX+DZiZbdPE6TtcmgPl11tfbC3yb4nElByX/ZY52/Ujlmjun0cVGZ0ui9WhecuP6foRv7u8HVRMqelm9Rvxu9ZsjdXRExk6eCxVn/+8V4+PX+vtaDjH978eKvAih4Ks3Rar1HTOe8KzbDa7fv79iNPRIwqSmJqjT2budnmWdw+cHiVmfVKCVsSXn44D5VViSo62FHEkybRMq35aEummROVfqSsmTp48WQMHDtT48eMVFBSkU6dO6a233tJHH33kku2v3nKqWAUDSTKZDNpxoPzNjwZ42+9ropV9nnkSzycxJUdZhZiP6kJWbTlV7HXXbXfvF4xvjkdqa0qSJGn0TvddKXjgWKqMBfSWuZCUdIuWu+Dk94YdcbJYi1fUstkc2rAjrsQZSot169YpJiZGK1asUIMGDdS6dWu3tLN1X4JMpuK975KUlW3TnGVHXBcIKOP+WH+iwN5PhZGWYdGvzNWGCujTTz/ViBEj9P777ysiIkJz5szRCy+8oD/++KPE2169Jea8PfALkpSaI3sxLrJCxbP/aEqx1tu4y7XfXXceSNT+o/kvrtt/NMUtvSBR8UyZs09HT6TnWZZttmnJmihZi3GBME47cuSIXnnlFY0dO1YPPPCA1q1bpylTpmjbtuJfuPzPzvgCh9wviMNxerhJwJNO9yor2vctm82hTW4YltfmcOjjw/v1c/Qxl28brhcZnVbkUX0ys63azFR2xeZ8vDgv2rBhg+644w5ZLBb5+fnppptuUr9+/fT666+7ZPsJycUf3sPuUIFD7wEovpIcVz5Gg1IzLAoMKNnHWUkKks7mX3W1H6KPaWVCnA5luu+LfWq6+fSvh2JKTMkueYYMc7F6qUqnxz8vT5/Rc+fO1fDhwyVJJpNJjRo10tixY2U2m/XGG2+4rJ2k1JK977nbACDp9EUuOZaSTeoem1jwsNtAebVz506NHDlSUVFRatq0qQYOHKhOnTpp0aJF6tWrV4Hrde3aVUlJSefd9hHTbZKhbu7tVdNvyDP0ZJN6odr1y8Dc20mpZl05bKFSUlLU+qK2Mqpkx3RpdMh0j9suVKqIEkyPSIYgSYXfvyTpUORxl74P6YYGijfeLBny/jZKSMpQvwFDVMlxxGVtIb+KcFydNPSSzXRJvuXHjp9Um7btZVLZ+V0QHh6u1atXezuGJOnDDz/UhAkTdPz4cc2aNUudOnXSjh07VL169QLX6d+/v+LiCr4g4VBON0ktcm/Pn9Qnd8i/Nk3CtXZG/9z74pKydePjS5WUlKQ77hqiYGPZv/hgZ/aN6tRpnLdjlDvueF0tjkAlZA2UdPr/6Nn7qpR3fz2zr0rSmrXr1anT60Vq64vwmmrl61/g/SaDQU80bq71SQnKtp3/+9+ePXs0vFOnIrVfmpSHYyTdFqGknL6SAiUVft/5YeZPWjfnKZfnKYuvaUREhBYsWFDoxxscpWxSlp9++kkLFixQgwYNlJycrEmTJumtt97S/fffr9q1a5933WbNmikjI+O8j0kL7K704N65t/cvGJRnDq4aVQPzzZuWkWVV8/6zZLBnqkrq9/KzHi3GMytbToU/oxpJE7wdA6oY70VC6L0y+zXOvX2h4/LMMSlJBnu2aiS+K4NKVkQ6Ff6s7KaQYmbIUM3Et4vV7rI27dUsMPi8j/nmeKTahobpksrhGr1ziyZedOkFt3sgK0O9d20uUhaLqaYSKt8jh/F0nnNfA+n8r0NQ5mpVzvytSG2eK8u/rZIr3SQZ/JxmON9ntOw5CsuYr8Ccwg0XFRwcrAMHDpQorzs9/PDDevTRR9WsWTN16tRJ8+fPV7169fTUU0/p448/vuD6wcHBsl3gy68k2QNbyFL7AckUlLssff3deeZ08/czKcecd1s2u0OVOn4jSTLkRMnv6PjCPrVyJafph/I/+JS3Y6AQPPVeWcN7y1btRslgkpT/eJIufEwZk1fKN/ZHt2c9ozTtx6UpS3lnMpku+NvJk6ZPn661a9fKbDarb9++GjhwoMaMGaNXXnlFfn7O55wrrFtGLtOcPwr+Dbfrl4FqM2BOvuXhoX5KXDW0RG2XVq1vnq3dc2/xdoxyo27vmYqOdT53U0H7lyRd0qKKtvw8wGU5klNz1HbQLzoek/fYrl8zWFtnDVB4aMEnUFFyFeG4Wr7hhAY89YdSzhkK8+Jm4do2a4AMhuKPeFKRvfPOOzKZTNq1a5cefPBBbd++XRkZGbrjjjsueC60IO9O26aXJm2S1cnIP2tn9FfnIflPHlevEqCdcwYqokpgsdosTTrdNV/rvrvR2zHKHXe8rhaLXXV6z1RckvML1AvaX0fc1kqfvNSlaG2NekE6eLjA+989sEfPNmuVW0zsXq3ggr6aNpbv+2X3PEh5OEZOxGboklvnFmnf8fUxauLTV+jxO9u4PE95eE0vpNT1TBw8eLAGDx6sZcuWKTo6WhMmTFBMTIxWr16tW2+99bzrFuak8B/rTmjQ6D9yhzo9cxL8jOhlt6tO7x+crlsppLIOrPmrQnwBr91rpk7sOuHtGFDFeC/e+mKr3vpiS24Pv6Icl3VrV9XRHUdK/KPl5ieXat7y/4YxKEqGvlc116//K957ZHl0lBQVXax1z6dZs+Y6sXRhkdbJzLKqdq/vlZJ+ujB77msgFfw6hFby1dcfvKoBvaYWL/C/9kUmq+NdC3J/nBblfagcWknrF/6o5g0rlyhDaTF06FDdddddatu2rUwmk+rXr68333xTjz32WKHWL+wJ4v1HUnTZbXOVnvVf79wzBY0zsv4ZpsDLpxe4jRuv7aK5H5W8Z2pZFNDha2VnV8znXtZ46r364ddDGj5mde4cNeceT9L5jyl/P6PeGjNaT99T8DHnaqVpPy5NWeBZw4YN07BhwzRx4kS1b99er776qkwmkzZt2qTOnTuXaNv3DWiuPzacVEoRp7vo0u48J5CAswzq00if/LC7SMNcB/qbdM9NzVyaIyzUXw8NaqEPZ+xS/L+jMlUL89cDt7SoEOcx4H5XX15L3TrU0PL1J3N/P9SpHqQPnu1EIbEEnnvuOUnS6NGj1aFDB3XpUrQCiTP33tRc7329s8CT7M40qRdaLgqJKFt8fY3q3qGmZi09Uuh1IsID9Pidru8J/myzVpKkjuFVXb5tuF7t6sGqVzO4SJ9z4aF+GnJDUzemKt9KXTHxjN69e1/4QcXQs2MtBQaYlJxW9HX7dqnDF3DADR4a1ELvTtte5OFCgwJMenrYxS750fLcfW21YmNMkedUDQvx0/P3tytx++dzd71GuX8XpldicQUF+ujWaxpp6twDRZ4fyNfHqP7d65c4Q4tGYWpSL0Sb9xR9/PKm9UPLTSFROj1s244dOyRJ1157rcaMGaOTJ0/q77//VvPmzV3WTvOGlVW/ViXtPpxcrPVDgn312B3lezgnoChuvLqBHnxjVbHXNxoNuqsfP25QcY0ePVqSNH686670vrZrXQX6m5RShN+AVSr768UHL3FZBpRvI4e20bcLDyoxpfDTqgQG+Ojem133ne6Ml4ZfquuurKePvt8lOaQn7mqty1pHuLwdVEwGg0FzP+yjhSuOacaiQ1q2Llprvu2v+rUqeTtauTBx4kSXbSuiSqAua11VS9ZEF2pWi8ohfnr1oUtc1j5QFC8Pv0R/bjhZ6P+jdWsEq0WjMPeGQpnw8vBLdM8rK5WafuER64xGqcslNajvlIDR2wE8zWAwaPTdFysowFSk9UIr+eq5+9q6KRVQsUVUCVTPK2rlGwbuQnx9jC67mrdT2+qqUrno/0xCg33V7bKaLslQGjw97GJVCiradSb+fiY9fGtL+fi45l/KSw9eopBg3yKtExLsq5fK6Qm/+Ph41ahRQ6+99po+//xz3X///S5v47n72hb5fT+jUpCvel5RvKF3gPIoKNBHQ25oKh+f4l3o0uPyWqoVEXThBwIoNJPJqNcevlSVKzn/fnHu3L8+JoOa1gtVZ3omopAa1A5Rr461Feif/zyDs7mlKwX56J6bmim0UsmG8C1I+9bVNP2t7po+tjuFRLic0WjQjT0a6Kf3eqpmtSAKiaXYtDe7qW71/NOqnNuLJyjAR9d0rq2+XevmeyzgCe1aVNXd/Zs6PRd07v5as2qgfnqvp6eioZS7uWcD9bi8ltNaz7n7Tr0awfri1a6eilYuVbhioiQ9fmdrtWkSLj/f/E8/46xh3s6oFOSj+25urg5t+BIOuMvkV69URJUAp/c5Oy5Dg301+dWuLvsBbjAYNOeDXgoLcb49ZxkqV/LVnA97l6vhXFo0CtNTQ9ooJMj5ybZzXwcfH4Oa1gtxaSFvQK8G6nlFLacnY5y9D0EBJvXuWFs392zgsgylSbVq1TR9unuHOxzct5GCAwsu4NoK6KkaHOij1x6+VMYiXggAlHdPD7tYAX4FX7h2vmPq1Yfc1wMdqMgeHtxK9w1o7rSgeOWw/4aG9/M1qkm9UC35vG+5+o4H95sxvrsubVk138mss/cvSQoJ8lWfTnU0YdQVnowHoAKqWS1Iq765Qc3qh+b5/3fj40tz/64W5q9BfRpq5js9+L8Hr3r/mY56eHBLVQvzl/GsU/Zn9teQIB/VrxmsP766Tk3rh3opJUobg8GgWRN76carG6haWN5OImf2nbAQP7VoWFmrv+nPUM4lVCGLiX6+Ji378jq1b1VVwYF5e2KcOz9XaLCv7u7fTBOf7ujJiECFU6NqoFZ/c4Pq1giWv1/ej6azj0uD4XRP4fef6ajbrm3i0gztWlTVok+vUZVQf/mY8n6JPjuDyWRQWIif5n/cR5e1rubSDKXB64+014jbWynUyRVhZ78OQQE+at04XCu/vkGBAa4bNdtgMOin93rqmi518l2Vdu5ndEiwr67pXFc/TOCHT0kE+Pvoz6+uU2gBPTaczfkWHOijwX0bafigFu6OB5Q5TeqF6vu3e+T7nnlGQcfUe6OvUMe29IQC3OX9ZzppwqgrVDsiKN8FZIH+JlWp7K/b+jbSxh9uUhjDH6GI/HxN+mtqPw0f1FJVK/sr+JxRH0KDfVW9SoCevfdizf6gFxdjAfCI+rUqac+8W/TtuKt1+UXVVKtaoGpUDVCtiEDdc1MzrZp+g6aP7S6TqUKeIkYpYjAY9O7IK7Txh5v06G2tVad60Ol9tVqgLm4WrsmvXqmDiwardZNwb0dFKePjY9TMd3toxbR+GtKviWpFnP6c8/UxqFPbCH33dnftnnuL6tTI31MbRVNq50x0t9BKflo57QZ9u/Cg3p6yTXFJ2co222Sx2uXva5KPj0HtmlfRy8Mv0TVd6OYPeELjuqHaMXuAPv1hjyZ9v0s5FruycqzKMdtVKchHRoNB13atqxcfbKd2LdwzGXKXS2po26wBem/6dk2ff1AOh0NZOTY5HFJggElGg0F39WuiZ+65WA1qh7glg7cZDAaNf+pyXXtlXb05eav+2Rknq80us8Uuq82hsBA/VQ7x03P3ttW9NzdTgL/r/5X4+Zo054PemrPsiMZ+uVWR0WkyW+wyW+3y8zHKz9eoRnVD9NIDl+iWPg0pJLpA6ybhWvNNf/V8YLEys61Kz8zfC1SSTEaD/P1MGn5LC733dEdee6AA/a+ur5/f66nBz/wpm+30/xJn/H2N8vEx6tMXO2vYTa6fOwtAXg8Oaqn7B7bQsnXR+mb+QcUlZSs40EfdO9TUvTc3d9uwk6gYfH2N+uDZThr/ZAfN/PWwFq08rl9XR+mGq+ppcN9GuvHqBvJ1MkISALiTyWRU/6vrq//V9b0dBbigBrVDNOmFzpr0QmdvR0EZ07pJuL4df3Xu7U53zdfaGTd6L1A5VGGLidLpL/r3DWiu+wY016bd8dpxIFEZWVaFBvvqqvY11bBO+SwUAKVZWKi/Xhp+iZ6/v63++idGkdFpGj1xvb549Upd06WORybJrVszWB8+11lvP3W5fl8TrZPxWZKkmtUCdU3nOi7thVeade9QS9071FL0qQwt/+ekUtLNemnSJv36WV91bBvh9iKS0WjQoGsaadA1jbTncLI27IhTWqZFIUG+uuLiCLVqHObW9iuiNk3DFfnrbfrht0N6e+p2xcRlyWQyyO5wyGgwyGK1a3DfRhp998Vq05SrAYELue6qeor89TZ9OWefPvh2p8zm0wXFlHSLKlfylcFg0KO3tdKjt7XiKknAg4xGg67pUpeLRuE2Af4+uvfm5rr35uZqffNs/cjcTgAAACjjKsYZ8UK4rHW1cjlcIVBWmUxG9epUW5L06mebddu1jT2eIcDfRzf2KJ/z8BVFnRrBGnJDU0nS2C+3qVM7zw/B16pxGMVDDwkK9NF9A1rovgEttPNAoo7FZCjHbFNYiJ86tKmmkGB6bABFUS08QC/c307P3nOxNu6KV2xitgaO+kMLP7lGHS+uTg8VAAAAAPAAQ83qcj57ffG2BVQ0FBMBAIBTFzWroouaVfF2DKBcMJmMufMhmowGXdm+ppcTAQAAAEDF4fPsSG9HAMo0iokA4EWG6hEuuyrq3O0CAAAAAAAAAFBSFBMBwIt8Xn/B2xEAAAAAAAAAACgQk7QAAAAAAAAAAAAAcIpiIgAAAAAAAAAAAACnKCYCAAAAAAAAAAAAcIpiIgAAAAAAAAAAAACnKCYCAAAAAAAAAAAAcIpiIgAAAAAAAAAAAACnfLwdAAAAAAAAAABQ9ljf/UCOmNgSb8dQs7p8nh3pgkQAAHegmAgAAAAAAAAAKDJHTKx08HDJt+OCLAAA96GYCAAAAACAi1jHvifHqZL30DjDUKO6fF562mXbA0rClfs3+zYAoCKLTchS1KkMGY0GNaxdSWGh/t6OhGLIyLTocFSa0jMtOh6Trno1K3k7kttQTAQAAAAAwEUcp2KlI0ddtz2XbQk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",
      "text/plain": [
       "<Figure size 1800x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,4, figsize=(18,5), constrained_layout=True)\n",
    "for qc,ax in zip(correct_edited_qc, axs.flatten()): \n",
    "    qc.draw(\"mpl\", plot_barriers=False, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c30bcc-1967-4c78-a6df-61f831838535",
   "metadata": {},
   "source": [
    "## 2. Masking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eda0d38-a56b-4f3f-b8cd-61a21604bd57",
   "metadata": {},
   "source": [
    "First we set a desired mask, i.e. a specific layout of a quantum processor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c75b5feb-6eeb-4cc1-94f4-85dc401cc464",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_gates     = 16\n",
    "num_of_qubits = 5\n",
    "\n",
    "d = 3\n",
    "#------\n",
    "def con_set(q1, q2, x, d): \n",
    "    qubit_mask[q1, x:x+d] = 1\n",
    "    qubit_mask[q2, x:x+d] = 1\n",
    "    return x+d\n",
    "\n",
    "#------\n",
    "x = 0\n",
    "\n",
    "qubit_mask = torch.zeros((num_of_qubits, max_gates), device=device) # mask: ones are getting filled, zeros are fixed !\n",
    "x = con_set(0, 1, x, d)\n",
    "x = con_set(1, 2, x, d)\n",
    "x = con_set(1, 3, x, d)\n",
    "x = con_set(3, 4, x, d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5927f5d5-9e0d-4d85-b4a3-a37b2ad9136e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 370x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_mask():\n",
    "    fig = plt.figure(figsize=(3.7,2), constrained_layout=True)\n",
    "    plt.imshow(qubit_mask.cpu(), cmap=\"Greens\")\n",
    "    plt.xticks(range(0, qubit_mask.shape[1], 2),fontsize=9)\n",
    "    plt.yticks(range(num_of_qubits), fontsize=9)\n",
    "    plt.xlabel(\"Gate sequence / time\", fontsize=12)\n",
    "    plt.ylabel(\"Qubits\", fontsize=12)\n",
    "    plt.show()\n",
    "plot_mask()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5f4eca-1486-45f2-86be-b3f7be87591e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_emb_org_images(pipeline, samples, system_size, max_gates, target_num_gates, target_num_bits, qubit_mask):\n",
    "    org_image = torch.zeros((1, system_size, max_gates), device=device, dtype=torch.int32) \n",
    "    \n",
    "    padd_tok = len(pipeline.gate_pool) + 1\n",
    "    padd_pos = (torch.ceil(torch.tensor(target_num_gates) / 4) * 4).to(torch.int32)\n",
    "    org_image[:,                :, padd_pos:] = padd_tok\n",
    "    org_image[:, target_num_bits:,          ] = padd_tok\n",
    "\n",
    "    emb_org_image  = pipeline.model.embedd_clrs(org_image)\n",
    "    emb_org_images = emb_org_image.repeat(samples, *[1]*(emb_org_image.dim()-1))\n",
    "    \n",
    "    return emb_org_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3732510e-4888-4a2f-88b6-a579e35ef721",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_pattern_SRV(pipeline, prompt, samples, system_size, num_of_qubits, max_gates, qubit_mask, t_start_index=0, target_num_gates=None, target_num_bits=None): \n",
    "\n",
    "    if not exists(target_num_gates):\n",
    "        target_num_gates = max_gates\n",
    "\n",
    "    if not exists(target_num_bits):\n",
    "        target_num_bits = num_of_qubits\n",
    "    \n",
    "    emb_org_images = get_emb_org_images(pipeline, samples, system_size, max_gates, target_num_gates, target_num_bits, qubit_mask)\n",
    "\n",
    "    #----------------\n",
    "    # prep condition\n",
    "\n",
    "    c = pipeline.text_encoder.tokenize_and_push_to_device(str(prompt))\n",
    "    c = c.repeat(samples, *[1]*(c.dim()-1))\n",
    "\n",
    "    #----------------\n",
    "    # latent fill\n",
    "    \n",
    "    out_tensor = pipeline.latent_filling(emb_org_images, qubit_mask, c=c, g=g, no_bar=False, t_start_index=t_start_index)\n",
    "    out_tensor = pipeline.model.invert_clr(out_tensor)\n",
    "    out_tensor = out_tensor[:, :num_of_qubits]\n",
    "    out_tensor = torch.unique(out_tensor, dim=0)\n",
    "     \n",
    "    qc_list, error_cnt, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool, place_barrier=True)\n",
    "\n",
    "    return qc_list, srv_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3bb0b7-6409-43f1-a513-bd09676b3a98",
   "metadata": {},
   "source": [
    "Now generate circuits corresponding to the mask. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c32b8784-0132-49a4-ab17-9169960cc801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Generate SRV: [2, 1, 2, 2, 2]'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples    = 512              # how many circuits we sample\n",
    "srv_target = [2, 1, 2, 2, 2]  # desired target SRV\n",
    "\n",
    "assert len(srv_target)==qubit_mask.shape[0]\n",
    "\n",
    "prompt = f\"Generate SRV: {srv_target}\"  # model was trained with this phrase\n",
    "prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b24cfdeb-825f-43c0-9efc-a3dc1819b54f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "184b7eff29b44d74a8d61a69e310e53f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/39 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qc_list, srv_list = generate_pattern_SRV(pipeline, prompt, samples, num_of_qubits, num_of_qubits, max_gates, qubit_mask, t_start_index=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad4beb7-97ea-4293-9abc-bc1472f2f7f0",
   "metadata": {},
   "source": [
    "Pick only correct ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83294a34-a93f-4597-b5ad-65b88e7f332f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 19 correct distinct solutions.\n"
     ]
    }
   ],
   "source": [
    "correct_qc = []\n",
    "for qc,srv in zip(qc_list, srv_list):\n",
    "    if srv==srv_target: correct_qc.append(qc)\n",
    "print(f\"We found {len(correct_qc)} correct distinct solutions.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda61abc-5fd2-48df-b7b6-11c0d98f2450",
   "metadata": {},
   "source": [
    "Let's plot them. Mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4b89db4-8163-4651-b049-033d9984f882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 370x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mask()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cbbef9-2fa7-4292-b6a5-ad10b11421f1",
   "metadata": {},
   "source": [
    "v.s. solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cf638ca-0513-4d82-8fa3-cac6deaaeb8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SRV is [2, 1, 2, 2, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1123.61x451.5 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"SRV is\", schmidt_rank_vector(DensityMatrix(correct_qc[0])))\n",
    "correct_qc[0].draw(\"mpl\", plot_barriers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f93f5ff7-0699-4229-94e1-ff3e065bd00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, min(len(correct_qc), 4), figsize=(18,5), constrained_layout=True)\n",
    "for qc,ax in zip(correct_qc, axs.flatten()): \n",
    "    qc.draw(\"mpl\", plot_barriers=False, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "684de45e-432d-4bee-8a88-dac2134f4b3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c48bb0e5-b8e4-4940-b7cd-4efd81c438e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genQC Version 0.1.0\n"
     ]
    }
   ],
   "source": [
    "import genQC\n",
    "print(\"genQC Version\", genQC.__version__)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}