src/examples/0_hello_circuit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69a855f1-55dd-482e-94f2-9ad02804be4d",
   "metadata": {},
   "source": [
    "# Generate a circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41e2465-49d8-46b8-b046-6ae1becfb268",
   "metadata": {},
   "source": [
    "A minimal example to generate a circuit. We load a pre-trained (SRV, 3 to 8 qubit) model and condition on a given Schmidt-Rank-Vector (SRV)."
   ]
  },
  {
   "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",
    "import genQC.inference.infer_srv as infer_srv\n",
    "import genQC.util as util"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "id": "4f36be1a-a5c6-4484-a096-4a37c9772e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "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": "dc1b8a80999f45f1a474f9ee25f32f4f",
       "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": "104f977d-a6c5-4dbf-b272-b1a8fc6f013a",
   "metadata": {},
   "source": [
    "Check on what gates the model was trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62b067ac-d5a4-4424-b7da-571ae95067c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h', 'cx']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.gate_pool"
   ]
  },
  {
   "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(20) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65acff8f-8486-42c9-8e78-b44f31de568b",
   "metadata": {},
   "source": [
    "## Inference / sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09bd191-0374-45af-b923-f131c5d36af9",
   "metadata": {},
   "source": [
    "Set our desired condition SRV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1d4b69e-c14a-4dac-9cdf-2b65ecaee158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Generate SRV: [2, 1, 2, 1, 2]'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srv           = [2, 1, 2, 1, 2]  # set your target SRV; can be 3 to 8 qubit\n",
    "num_of_qubits = len(srv)          \n",
    "\n",
    "prompt = f\"Generate SRV: {srv}\"  # model was trained with this phrase\n",
    "prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0184031c-627c-4b82-9607-e35a22f699f4",
   "metadata": {},
   "source": [
    "Define sample parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "721ba4f1-60f0-4f23-9306-bdf07f8fe659",
   "metadata": {},
   "outputs": [],
   "source": [
    "g         = 10      # guidance scale\n",
    "max_gates = 16      # how many time steps the tensor encoding has\n",
    "samples   = 64      # how many circuits to generate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2842cc0c-770a-451c-ac44-e7265dbd87c2",
   "metadata": {},
   "source": [
    "Sample tokenized circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72a4c09-3af9-4221-a987-2dc7fa8b2b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "226882c117744a86896c82b71cc87232",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: (generate_srv_tensors) Generated 64 tensors\n"
     ]
    }
   ],
   "source": [
    "out_tensor = infer_srv.generate_srv_tensors(pipeline, prompt, samples, num_of_qubits, num_of_qubits, max_gates, g, no_bar=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d07a692-8c6f-4c31-8fd2-d8cd89edecf0",
   "metadata": {},
   "source": [
    "Check how many distinct tensors we got:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72c575b4-ebc5-46b7-b588-55f3340df04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_tensor.unique(dim=0).shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e5728b-22d5-480f-bde8-5add698611b4",
   "metadata": {},
   "source": [
    "Let's look what is generated. Note, 3 is the padding token (or empty action)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9116f267-9f6b-4f80-a0f6-ef38fb694a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0,  0,  2,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 0,  0,  0,  2,  1,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 0, -2,  0,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 1,  2, -2, -2,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3]],\n",
       "\n",
       "        [[-2,  1,  0, -2, -2,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 2,  0,  0,  0,  2,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 0,  0,  1,  0,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3],\n",
       "         [ 0,  0,  0,  2,  0,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3]]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_tensor[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a3c5f4-3333-49a0-a965-2c392dcc2002",
   "metadata": {},
   "source": [
    "## Convert to qiskit circuit "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f71bbfa-8fd0-4a0c-ba9c-1a4f09da527d",
   "metadata": {},
   "source": [
    "To get a qiskit circuit we need to do: \n",
    "\n",
    "- apply cosine similarity to go from embeddings to token matrices (the function `infer_srv.generate_srv_tensors` did this already)\n",
    "- parse token matrix to qiskit and filter out error circuits\n",
    "- calculate SRV and plot circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79d9c737-6773-47af-8d3a-7e1ec9ec704b",
   "metadata": {},
   "outputs": [],
   "source": [
    "qc_list, error_cnt, srv_list = infer_srv.convert_tensors_to_srvs(out_tensor, pipeline.gate_pool)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b033c5a9-d77a-467e-8474-537ace2d9b4b",
   "metadata": {},
   "source": [
    "Generated error circuits (token matrices that don't correspond to circuits):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f9dd437-cac3-4fa7-bdfe-0046c6a727e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a93fdc6-9e12-4e07-b25a-ca3f21a5cda4",
   "metadata": {},
   "source": [
    "What SRVs did we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "363350c2-9b04-4231-a898-d82dff6382ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 1, 2, 1, 2], [2, 1, 2, 1, 2], [2, 1, 2, 1, 2], [2, 1, 2, 1, 2]]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srv_list[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995435c3-ec71-4b2d-84cc-8aa1ededd546",
   "metadata": {},
   "source": [
    "That is an accuracy of:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f15f0507-e9c9-4176-8acf-27c2e063694f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9375"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(srv==x for x in srv_list)/len(srv_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782ffa59-6386-49d1-9014-2bf093674e43",
   "metadata": {},
   "source": [
    "Finally plot some of the circuits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc0308c1-1a0b-4be1-90b1-2811a13caee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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,is_srv,ax in zip(qc_list, srv_list, axs.flatten()): \n",
    "    is_srv = [int(x) for x in is_srv]\n",
    "    qc.draw(\"mpl\", plot_barriers=False, ax=ax, style = \"clifford\")\n",
    "    ax.set_title(f\"{'Correct' if is_srv==srv else 'NOT correct'}, is SRV = {is_srv}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0244290d-5c57-4b70-b670-a839876a9ccf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52132fa0-9208-442d-a31d-af65bcfba714",
   "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
}