{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "xLOXFOT5Q40E"
},
"source": [
"##### Copyright 2020 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "form",
"execution": {
"iopub.execute_input": "2025-09-13T23:51:29.815683Z",
"iopub.status.busy": "2025-09-13T23:51:29.815410Z",
"iopub.status.idle": "2025-09-13T23:51:29.819547Z",
"shell.execute_reply": "2025-09-13T23:51:29.818867Z"
},
"id": "iiQkM5ZgQ8r2"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j6331ZSsQGY3"
},
"source": [
"# Research tools"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i9Jcnb8bQQyd"
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tOpPugFsILnT"
},
"source": [
"TensorFlow Quantum brings quantum primitives into the TensorFlow ecosystem. Now quantum researchers can leverage tools from TensorFlow. In this tutorial you will take a closer look at incorporating [TensorBoard](https://www.tensorflow.org/tensorboard) into your quantum computing research. Using the [DCGAN tutorial](https://www.tensorflow.org/tutorials/generative/dcgan) from TensorFlow you will quickly build up working experiments and visualizations similar to ones done by [Niu et al.](https://arxiv.org/pdf/2010.11983.pdf). Broadly speaking you will:\n",
"\n",
"1. Train a GAN to produce samples that look like they came from quantum circuits.\n",
"2. Visualize the training progress as well as distribution evolution over time.\n",
"3. Benchmark the experiment by exploring the compute graph."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:51:29.822435Z",
"iopub.status.busy": "2025-09-13T23:51:29.822189Z",
"iopub.status.idle": "2025-09-13T23:52:09.160214Z",
"shell.execute_reply": "2025-09-13T23:52:09.159222Z"
},
"id": "yZvic4nmOH7v"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting tensorflow==2.15.0\r\n"
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"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
"tf-keras 2.19.0 requires tensorflow<2.20,>=2.19, but you have tensorflow 2.15.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
"\u001b[0mSuccessfully installed cachetools-5.5.2 cirq-core-1.3.0 cirq-google-1.3.0 duet-0.2.9 google-api-core-2.25.1 google-auth-2.40.3 google-auth-oauthlib-1.2.2 googleapis-common-protos-1.70.0 grpcio-status-1.62.3 gviz-api-1.10.0 keras-2.15.0 ml-dtypes-0.2.0 mpmath-1.3.0 numpy-1.26.4 oauthlib-3.3.1 proto-plus-1.26.1 pyasn1-0.6.1 pyasn1-modules-0.4.2 requests-oauthlib-2.0.0 rsa-4.9.1 sortedcontainers-2.4.0 sympy-1.12 tensorboard-2.15.2 tensorboard_plugin_profile-2.15.0 tensorflow-2.15.0 tensorflow-estimator-2.15.0 tensorflow-quantum-0.7.3 wrapt-1.14.2\r\n"
]
}
],
"source": [
"!pip install tensorflow==2.15.0 tensorflow-quantum==0.7.3 tensorboard_plugin_profile==2.15.0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
"execution": {
"iopub.execute_input": "2025-09-13T23:52:09.163391Z",
"iopub.status.busy": "2025-09-13T23:52:09.163100Z",
"iopub.status.idle": "2025-09-13T23:52:09.275718Z",
"shell.execute_reply": "2025-09-13T23:52:09.275035Z"
},
"id": "4Ql5PW-ACO0J"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmpfs/tmp/ipykernel_11749/2730622109.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
" import importlib, pkg_resources\n",
"/usr/lib/python3.9/importlib/__init__.py:169: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
" _bootstrap._exec(spec, module)\n"
]
},
{
"data": {
"text/plain": [
"
"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Update package resources to account for version changes.\n",
"import importlib, pkg_resources\n",
"\n",
"importlib.reload(pkg_resources)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "INxjZ39hzHLu"
},
"outputs": [],
"source": [
"#docs_infra: no_execute\n",
"%load_ext tensorboard"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:09.278237Z",
"iopub.status.busy": "2025-09-13T23:52:09.277990Z",
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},
"id": "OVU9jSM80hvj"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-09-13 23:52:11.089770: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2025-09-13 23:52:11.089814: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2025-09-13 23:52:11.091508: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-09-13 23:52:12.094803: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-09-13 23:52:12.917980: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:274] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
]
}
],
"source": [
"import datetime\n",
"import time\n",
"import cirq\n",
"import tensorflow as tf\n",
"import tensorflow_quantum as tfq\n",
"from tensorflow.keras import layers\n",
"\n",
"# visualization tools\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from cirq.contrib.svg import SVGCircuit"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IYf7A1mubOfW"
},
"source": [
"## 1. Data generation\n",
"\n",
"Start by gathering some data. You can use TensorFlow Quantum to quickly generate some bitstring samples that will be the primary datasource for the rest of your experiments. Like Niu et al. you will explore how easy it is to emulate sampling from random circuits with drastically reduced depth. First, define some helpers:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.010879Z",
"iopub.status.busy": "2025-09-13T23:52:13.009948Z",
"iopub.status.idle": "2025-09-13T23:52:13.015227Z",
"shell.execute_reply": "2025-09-13T23:52:13.014505Z"
},
"id": "1_6ayfdGbExV"
},
"outputs": [],
"source": [
"def generate_circuit(qubits):\n",
" \"\"\"Generate a random circuit on qubits.\"\"\"\n",
" random_circuit = cirq.experiments.random_rotations_between_grid_interaction_layers_circuit(\n",
" qubits, depth=2)\n",
" return random_circuit\n",
"\n",
"\n",
"def generate_data(circuit, n_samples):\n",
" \"\"\"Draw n_samples samples from circuit into a tf.Tensor.\"\"\"\n",
" return tf.squeeze(tfq.layers.Sample()(circuit,\n",
" repetitions=n_samples).to_tensor())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4BaTKnlMfJcW"
},
"source": [
"Now you can inspect the circuit as well as some sample data:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.017616Z",
"iopub.status.busy": "2025-09-13T23:52:13.017396Z",
"iopub.status.idle": "2025-09-13T23:52:13.303840Z",
"shell.execute_reply": "2025-09-13T23:52:13.303026Z"
},
"id": "wpXMPpaCdMHs"
},
"outputs": [
{
"name": "stderr",
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{
"data": {
"image/svg+xml": [
"(0, 0): (0, 1): (0, 2): (0, 3): (0, 4): X^0.5 X^0.5 X^0.5 Y^0.5 Y^0.5 Y^0.5 Y^0.5 Y^0.5 X^0.5 PhX(0.25)^0.5 PhX(0.25)^0.5 X^0.5 X^0.5 Y^0.5 X^0.5 M M M M M "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qubits = cirq.GridQubit.rect(1, 5)\n",
"random_circuit_m = generate_circuit(qubits) + cirq.measure_each(*qubits)\n",
"SVGCircuit(random_circuit_m)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.306766Z",
"iopub.status.busy": "2025-09-13T23:52:13.306351Z",
"iopub.status.idle": "2025-09-13T23:52:13.317753Z",
"shell.execute_reply": "2025-09-13T23:52:13.317014Z"
},
"id": "vswKr--Lfecp"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 Random bitstrings from this circuit:\n",
"q(0, 0)=1111110101\n",
"q(0, 1)=1111111111\n",
"q(0, 2)=1111111111\n",
"q(0, 3)=1111111111\n",
"q(0, 4)=0110000000\n"
]
}
],
"source": [
"samples = cirq.sample(random_circuit_m, repetitions=10)\n",
"print('10 Random bitstrings from this circuit:')\n",
"print(samples)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DQ7Hkc87hO5m"
},
"source": [
"You can do the same thing in TensorFlow Quantum with:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.320399Z",
"iopub.status.busy": "2025-09-13T23:52:13.320142Z",
"iopub.status.idle": "2025-09-13T23:52:13.458368Z",
"shell.execute_reply": "2025-09-13T23:52:13.457441Z"
},
"id": "lr35KSuehN7J"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_data(random_circuit_m, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qjP1bEfkjFPz"
},
"source": [
"Now you can quickly generate your training data with:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.461252Z",
"iopub.status.busy": "2025-09-13T23:52:13.460996Z",
"iopub.status.idle": "2025-09-13T23:52:13.543082Z",
"shell.execute_reply": "2025-09-13T23:52:13.542254Z"
},
"id": "c_vAWkZFc0g-"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N_SAMPLES = 60000\n",
"N_QUBITS = 10\n",
"QUBITS = cirq.GridQubit.rect(1, N_QUBITS)\n",
"REFERENCE_CIRCUIT = generate_circuit(QUBITS)\n",
"all_data = generate_data(REFERENCE_CIRCUIT, N_SAMPLES)\n",
"all_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_aQ1MvuYmoGv"
},
"source": [
"It will be useful to define some helper functions to visualize as training gets underway. Two interesting quantities to use are:\n",
"1. The integer values of samples, so that you can create histograms of the distribution.\n",
"2. The [linear XEB](https://arxiv.org/pdf/1608.00263.pdf) fidelity estimate of a set of samples, to give some indication of how \"truly quantum random\" the samples are.\n",
"\n",
"Note: Since you are dealing with reduced depth random circuits XEB fidelity numbers may tend to vary from run to run."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.545872Z",
"iopub.status.busy": "2025-09-13T23:52:13.545608Z",
"iopub.status.idle": "2025-09-13T23:52:13.551917Z",
"shell.execute_reply": "2025-09-13T23:52:13.551116Z"
},
"id": "MJakIECTbMd7"
},
"outputs": [],
"source": [
"@tf.function\n",
"def bits_to_ints(bits):\n",
" \"\"\"Convert tensor of bitstrings to tensor of ints.\"\"\"\n",
" sigs = tf.constant([1 << i for i in range(N_QUBITS)], dtype=tf.int32)\n",
" rounded_bits = tf.clip_by_value(tf.math.round(\n",
" tf.cast(bits, dtype=tf.dtypes.float32)),\n",
" clip_value_min=0,\n",
" clip_value_max=1)\n",
" return tf.einsum('jk,k->j', tf.cast(rounded_bits, dtype=tf.dtypes.int32),\n",
" sigs)\n",
"\n",
"\n",
"@tf.function\n",
"def xeb_fid(bits):\n",
" \"\"\"Compute linear XEB fidelity of bitstrings.\"\"\"\n",
" final_probs = tf.squeeze(\n",
" tf.abs(tfq.layers.State()(REFERENCE_CIRCUIT).to_tensor())**2)\n",
" nums = bits_to_ints(bits)\n",
" return (2**N_QUBITS) * tf.reduce_mean(tf.gather(final_probs, nums)) - 1.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tWj-bZHboXZI"
},
"source": [
"Here you can visualize your distribution and sanity check things using XEB:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.554314Z",
"iopub.status.busy": "2025-09-13T23:52:13.554074Z",
"iopub.status.idle": "2025-09-13T23:52:13.842688Z",
"shell.execute_reply": "2025-09-13T23:52:13.841983Z"
},
"id": "9HFGMGLToVHu"
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(bits_to_ints(all_data).numpy(), 50)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T23:52:13.845522Z",
"iopub.status.busy": "2025-09-13T23:52:13.845285Z",
"iopub.status.idle": "2025-09-13T23:52:14.296140Z",
"shell.execute_reply": "2025-09-13T23:52:14.295350Z"
},
"id": "9m-PzaTMpB_H"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32. This will discard the imaginary part and may not be what you intended.\n"
]
},
{
"data": {
"text/plain": [
" ![](\"https://www.tensorflow.org/images/tf_logo_32px.png\"){kind=logo}
View on TensorFlow.org\n",
" ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Run in Google Colab\n",
" ![](\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\")
View source on GitHub\n",
" ![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo}
Download notebook\n",
"