Repository: mit-han-lab/torchquantum (language: python)

Issue:
Adding new examples and algorithms for TQ
To enrich torchquantum's support of various algorithms, we need your help on implementing more quantum algorithms and examples in TQ! You may choose any quantum algorithm you like to implement such as QSVT or generative quantum models. Once complete, add a Python file and notebook to `examples` and (if you see fit) an algorithm to `torchquantum/algorithms`!

Please don't hesitate to let us know for any questions!


Discussion / hints:
Hi @GenericP3rson, Nice to meet you. 

I am Rishabh Singh, and I have been learning about quantum computing since 2020. 

- Experience - Qiskit, Pennylane and Cirq.
- Participation - Quantum Hackathons, Qiskit Summer School and IBM Quantum Challenges.
- Open Source Contribution - Cirq, Pennylane, Scikit-Learn.
- I am interested in this issue and want to contribute under UnitaryHack.

Best Regards 
Rishabh Singh
[LinkedIn](https://www.linkedin.com/in/anonymousr007/) | [GitHub](https://github.com/anonymousr007)
Hello, I've just assigned you the task! What are you planning to implement?

Fix patch:
diff --git a/examples/QCBM/README.md b/examples/QCBM/README.md
new file mode 100644
index 00000000..cf61c65c
--- /dev/null
+++ b/examples/QCBM/README.md
@@ -0,0 +1,42 @@
+# Quantum Circuit Born Machine
+(Implementation by: [Gopal Ramesh Dahale](https://github.com/Gopal-Dahale))
+
+Quantum Circuit Born Machine (QCBM) [1] is a generative modeling algorithm which uses Born rule from quantum mechanics to sample from a quantum state $|\psi \rangle$ learned by training an ansatz $U(\theta)$ [1][2]. In this tutorial we show how `torchquantum` can be used to model a Gaussian mixture with QCBM.
+
+## Setup
+
+Below is the usage of `qcbm_gaussian_mixture.py` which can be obtained by running `python qcbm_gaussian_mixture.py -h`.
+
+```
+usage: qcbm_gaussian_mixture.py [-h] [--n_wires N_WIRES] [--epochs EPOCHS] [--n_blocks N_BLOCKS] [--n_layers_per_block N_LAYERS_PER_BLOCK] [--plot] [--optimizer OPTIMIZER] [--lr LR]
+
+options:
+  -h, --help            show this help message and exit
+  --n_wires N_WIRES     Number of wires used in the circuit
+  --epochs EPOCHS       Number of training epochs
+  --n_blocks N_BLOCKS   Number of blocks in ansatz
+  --n_layers_per_block N_LAYERS_PER_BLOCK
+                        Number of layers per block in ansatz
+  --plot                Visualize the predicted probability distribution
+  --optimizer OPTIMIZER
+                        optimizer class from torch.optim
+  --lr LR
+```
+
+For example:
+
+```
+python qcbm_gaussian_mixture.py --plot --epochs 100 --optimizer RMSprop --lr 0.01 --n_blocks 6 --n_layers_per_block 2 --n_wires 6
+```
+
+Using the command above gives an output similar to the plot below.
+
+<p align="center">
+<img src ='./assets/sample_output.png' width-500 alt='sample output of QCBM'>
+</p>
+
+
+## References
+
+1. Liu, Jin-Guo, and Lei Wang. “Differentiable learning of quantum circuit born machines.” Physical Review A 98.6 (2018): 062324.
+2. Gili, Kaitlin, et al. "Do quantum circuit born machines generalize?." Quantum Science and Technology 8.3 (2023): 035021.
\ No newline at end of file
diff --git a/examples/QCBM/assets/sample_output.png b/examples/QCBM/assets/sample_output.png
new file mode 100644
index 00000000..c1626a4e
Binary files /dev/null and b/examples/QCBM/assets/sample_output.png differ
diff --git a/examples/QCBM/qcbm_gaussian_mixture.ipynb b/examples/QCBM/qcbm_gaussian_mixture.ipynb
new file mode 100644
index 00000000..849f7cdc
--- /dev/null
+++ b/examples/QCBM/qcbm_gaussian_mixture.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1cfe7a69-13c6-48ce-bc02-62e2047eef22",
+   "metadata": {},
+   "source": [
+    "# Learning Gaussian Mixture with Quantum Circuit Born Machine\n",
+    "\n",
+    "A QCBM is a generative model that represents the probability distribution of a classical dataset as a quantum pure state. It is a quantum circuit that generates samples via projective measurements on qubits. Given a target distribution $\\pi(x)$, we can generate samples closer to it using a QCBM.\n",
+    "\n",
+    "The Kerneled MMD loss is used to measure the difference between the generated samples and the target distribution.\n",
+    "\n",
+    "$$\n",
+    "\\mathcal{L} = \\underset{x, y \\sim p_\\boldsymbol{\\theta}}{\\mathbb{E}}[{K(x,y)}]-2\\underset{x\\sim p_\\boldsymbol{\\theta},y\\sim \\pi}{\\mathbb{E}}[K(x,y)]+\\underset{x, y \\sim \\pi}{\\mathbb{E}}[K(x, y)]\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4d440b94-63d4-4f6d-882e-45827d54cb4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torchquantum.algorithm import QCBM, MMDLoss\n",
+    "import torchquantum as tq\n",
+    "from qcbm_gaussian_mixture import gaussian_mixture_pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d14c9f7-4e5d-4fe1-98b4-83962d949519",
+   "metadata": {},
+   "source": [
+    "We use the function `gaussian_mixture_pdf` to generate a gaussian mixture which will be the target distribution $\\pi(x)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5483ab05-1a08-4bdc-8799-0e67433131af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7c8e443138b0>]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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c1IOJkegd5IMINRdZc1d6oxl7z17BqbJaBIq0G/etKD1l9hl7tnWfiJwRkxpyOic0NThboceRi1oE+zn2vjWj+gZi++/GYcGEGLFDIZH4KDzww+IJ+MusYYgJ9RM7nBt6sGkI6pvCy9AZOGOPnBOTGnI6A8JV2LNoAv76y0T4Khx7T1buq0NA48ynu+McY3HIG0mI9EffEF8YTFZ8ffiy2OEQtQuTGnJKEf5eGNNP/BVZW8totjhsPQUR0JiAT2tasyazkAvxkXNiUkPUyQwmC5Jfy8aMtXtx4Ypj1wBRx/riwEVMXf0DNuY5R6F42oDG6eY6g4mzoMgpMakhp/LlwUv41Ye52HrEebrHlZ4yDIpQIcRPgZKqerHDoS707dEyHCypRkmVcyxq1zfEF3sXT8QXvx3lcGs/EbWGYxckEP3Et8fK8N3JCgzprnb4GoUfW/WLYfD38uSFwo0YzRbsOlUBAEgbGCpyNK0jkUgQxpl65MTYU0NOw2Sx2i8S42Ida1XWWwnwkTOhcTN7z1VB32BBiJ8CgyPUYofTZvUNFodeMJCoJUxqyGkUXLiKGoMZAT5yxPfwFzucdrFYBW5w6Sa2HSsDAEwcEOpUCa0gCHj0X3mI/+O3OFVWK3Y4RG3CpIacxo6Tjb00Y/sFQ+ZEFwmbrUcuI+X1bXjpy0KxQ6FOJggCth1vTGruHOhcvYoSiQRGsxUNZityzlaKHQ5Rm7CmhpzGdyfLAQDj+jvPVO4fC1UpUFnbgO9OlsNkscJTxt8UrupoqQ6XtQZ4ecowsk+Q2OG02e8m9ccf7h6AviG+YodC1Cb8ViWnUFpdjxOaGkglwJgY50xqEiK7IcBHDp3BjP1FVWKHQ50o+3hjAn5HTBCUnjKRo2m7wd3ViAn14+KR5HSY1JBT2NHUSzO0Zzd0c9D9c25FJpVgQlOBs+2iR67JNvTkLLOeiFwFkxpyCjtONNbTjHfSoScb2+Jm246XcWaJi7qsrceRS1pIJLAnsc7o8MVqPLGuAMs2HxU7FKJWY1JDDs9otuCHM40Fi+Od+CIBAHfEBEMuk+LClTqcreDMEldk64UbGumPIF/H3nD1ZmqNZnx9+DK2HC7l6sLkNJjUkMPLPV+FelPjeh8Dw1Vih3NbfBQeSO0TCADYxiEol+QqQ09JvQLgq/BAZW0DjlzSih0OUaswqSGHd23oKcQlChftQ1BN65iQ6zBbrDhWqgMA3DnAuZMauYcUd8Q0ztzafoIJODkHJjXk8K4NPTl3PY3NhKaLXUHxVVypNYocDXUkD5kUexZNwH8fT3WJ6dDj+zcm4LblFIgcHZMacnhfPDESf5+dhNFOOpX7p7r7e2FguApW4dqCguQ6PGRSJPYKcIlexXFNPyQOXdSiooYJODk+JjXk8LzlHkgbGApfheusFWkbgso+ziEoVyEIgsvNaAvxUyKue+O+VeytIWfApIZIBBObhqB2naqA0WwRORrqCAXFVzHqje14+38nxA6lQ9mWUfiOvYrkBJjUkMOqazDjvtU/4P/+d9LlLvxx3dUIVSkQHeyDMi279V3BtuPlKNUacPFqvdihdCjbMgq7TlXAZLGKHA3RzblOfz65nJyzV3CopBpXao14dlI/scPpUFKpBNnPjnOpITV3t3BiDJJ6dUOgE69N05IhPfwR4CNHlb4B+ReuYkTvQLFDIrohfqOSw0rs1Q3vPBQPs1VwiaLLn2JC41qUnjL7sKIrkUklGNcvGJ8fuIQdJ8qZ1JBD4/ATOSx/bzl+PqwHHkqKFDuUTlXfYIHeaBY7DKIbsg1Bcb0acnRMaohE9OqWY4h/+Vt8XnBR7FDoNizbfBRvfHMCF6/WiR1KpxgTEwyZVIKzFbUo1xnEDofohtj/TQ7p26MaFF3RY0JsqEssYnYjai9PNFisKLykEzsUaieDyYJPc4thNFvx82HdxQ6nU6i9PfGPOUn2+hoiR8WkhhzSZ3kl2Ha8HIIAl05qpg+PxM/iIxAV6C12KNROBcVXYTRbEeynQIwLf1bH9XfuzWTJPTCpIYdjsQrIPV8FAC5flBiiUoodAt0m2zYeo/sGuWRBO5EzYU0NOZzjl3XQGczwVXhgUIRz78pNru+HM1cAACP7uHYCDgD/2H0ev/jbXhRy125yUO1KalavXo2oqCgolUqkpKQgNzf3pu03btyI2NhYKJVKxMXFYevWrfbHTCYTnn/+ecTFxcHHxwcRERGYPXs2SktLmx0jKioKEomk2e2NN95oT/jk4Paea7xIJEV1g4fM9fPuExodHv93PhasKxA7FGojbb0Jhy9WAwBG9Q0SN5gukHO2EnvOXkHO2Stih0LUojZfMTZs2ICMjAwsXboUBQUFiI+PR3p6OsrLW57qt2fPHsycORPz5s3DgQMHMHXqVEydOhWFhYUAgLq6OhQUFOCll15CQUEBPv/8c5w8eRL33nvvdcf64x//iMuXL9tvTz75ZFvDJyewz02GnmykEgm+KdRg2/EyGEyutXKyq9t77gqsAtA72AcR/l5ih9PpfpHSE69OHYzJg8PEDoWoRW2uqXnnnXfwyCOPYO7cuQCANWvW4Ouvv8YHH3yARYsWXdf+z3/+MyZPnozf//73AIBXXnkFWVlZWLVqFdasWQO1Wo2srKxmz1m1ahWSk5NRXFyMnj172u/38/NDWBj/MbkyqxvV09jEhPgi2E+BihojCoqvYmQf1//F7yr2/Kiexh1MiHW9xQXJtbSpp6ahoQH5+flIS0u7dgCpFGlpacjJyWnxOTk5Oc3aA0B6evoN2wOAVquFRCKBv79/s/vfeOMNBAYGYujQoXj77bdhNnPBMldzXKODtt4EH7kMg92knkYikdjrMfacYbe+M9ndlNQwESVyDG3qqamsrITFYkFoaPNsPTQ0FCdOtLwzrUajabG9RqNpsb3BYMDzzz+PmTNnQqW6dlF76qmnMGzYMAQEBGDPnj1YvHgxLl++jHfeeafF4xiNRhiN1zYK1Om4Dogz2HeusZcmKSrALeppbEb1DcKXB0vxw9lK/A79xQ6HWkGjNeBshR5SCZDqJr2KAFBSVYedpyoQ4a9kzw05HIea0m0ymfDQQw9BEAS8//77zR7LyMiw//+QIUMgl8vx6KOPYvny5VAort9Abvny5Xj55Zc7PWbqWLYiYXcZerKxFZkevqiFzmCCSukpckR0K7ap3HE9/KH2dp+/r/8d1eDVr49jYmwIkxpyOG36KRwUFASZTIaysrJm95eVld2w1iUsLKxV7W0JzYULF5CVldWsl6YlKSkpMJvNKCoqavHxxYsXQ6vV2m8lJSW3eHckNqtVQG5RY09NSu8AkaPpWt39vRAV6A2LVbD3VpFju7Y+jXsl4LYfHLnnq2CxCiJHQ9Rcm5IauVyOxMREZGdn2++zWq3Izs5Gampqi89JTU1t1h4AsrKymrW3JTSnT5/Gtm3bEBh46y+JgwcPQiqVIiSk5VUuFQoFVCpVsxs5tpNlNaiuM8FbLkNcd7XY4XQ5W2+N7WJJjksQBHs9zSg3q6cZEK6Cn9IDNUYzjpVyWJ8cS5uHnzIyMjBnzhwkJSUhOTkZK1euhF6vt8+Gmj17Nrp3747ly5cDABYuXIixY8dixYoVmDJlCtavX4+8vDysXbsWQGNC88ADD6CgoABbtmyBxWKx19sEBARALpcjJycH+/btw/jx4+Hn54ecnBw888wzePjhh9GtW7eOOhcksmvr0wTA043qaWxG9Q3CJ/uKsecskxpn8Pc5Sdh9phLDernXd5BMKkFyVACyT5Rj77kriOvhfj9AyHG1OamZPn06KioqsGTJEmg0GiQkJCAzM9NeDFxcXAyp9NoFaeTIkVi3bh1efPFFvPDCC4iJicGmTZswePBgAMClS5ewefNmAEBCQkKz19qxYwfGjRsHhUKB9evXY9myZTAajYiOjsYzzzzTrM6GnF9ir26YP6Y3YsP8xA5FFKm9AyGRAKfKGndC5hYKjksikWBID38M6eEvdiiiGNE70J7UPDKmt9jhENlJBEFwi0FRnU4HtVoNrVbLoShyWD9773sUXtJh5fQETB3qmjs+k/M7clGLe1bthp/CAweXToJMyj2vqPO05frtfn38RA7MVp/BuhrHZbZY8YcvjuDLg5dgsljFDkcUAyNU8FOwroYcD5Macgj5F65i16kK6I3uvaDiyKZi4T3cW8dhHb6kxSf7irHky6OQuumu3DKpBMnRjTMUbbVwRI6ASQ05hH/sPofZH+Tiwx/Oix2KqJKjAvCXWcOw5cnRYodCNxDgLcf8Mb0xIznSrYddbFO7mdSQI3GoxffIfYWrvdDd3wupfdxrzY+f8pLLcHdcuNhh0E1EBfnghbsHiB2G6H66Xo07J3jkOJjUkEN46WcD8dLPBsJN6taJnN5P62o4tZscAYefyKFI3LRG4cf0RjP+vO00fv3Rfli5YqtDOVNei12nKlDfYBE7FNGxroYcEZMaEt3Fq3W8eP+IwkOKv31/DttPlOPYZc4scSQb80ow+4NcLN1cKHYoDuHuuHDMSumJIeylIQfB4ScSlSAIuG/VDzBZrPjv4yMRE+qeC+/9mIdMiifG94XKywPhai7A50h+aFrt2balhbubltgD0xJ7iB0GkR2TGhLVmfJaXNE3QOEhRc9Ab7HDcRiPj+sjdgj0E1X6BhxtWpNlpJvt90TkLJjUkKj2nm/ckTqxVzcoPGQiR0N0Yzlnr0AQgP6hfgj2U4gdjsNoMFtx5FI1pBIJhvZ0r32wyPGwpoZEZSswtE0PpWvOVtTiXzlFOF1WI3YoBA493cg/c4ow7f0crNp+RuxQiJjUkHgEQcA+JjU39E7WKbz05VFkFmrEDoXQ2FMDAKP68rP6YynRgQjwkSPARy52KERMakg8Zyv0qKxtrKeJj+TsiZ8a0TRddl/TEB2JR6M14HylHlIJMLzp74UaDe6uQt4f0vD2g/Fih0LEpIbEYxt6GtaT9TQtsfVe5V2oQoPZPTdOdBT7zjd+Vgd3V0Ol9BQ5GscikUgg5WrC5CCY1JBo9hc19kAk85dvi/qG+CLQRw6DyYrDF6vFDsetsfbr1gRBQHmNQewwyM0xqSHR5BVdBQAMj2JS0xKJRIKU3hyCcgR7zzWe/xG9+VltSUlVHVKXb8fEFTu5kCaJikkNiUKjNeBSdT2kEiChp7/Y4Tgs7oQsvh/X0yQxAW9RuFoJncGEGoMZp8trxQ6H3BiTGhJF/oXGXpoB4Sr4Krhc0o2kRDfV1RRdhcnCuhox2OppBkWwnuZGPGRSJET6A2isASMSC5MaEoXtiy+pFxfrupmYEF8E+MhRb7Lg8EWt2OG4JZWXJ+6ICcL4/sFih+LQbP+W85uGlYnEwJ/IJIoqfQMkEiCR3fk3JZVKkBwVgMyjGuw9dwWJTAK73Pj+IRjfP0TsMBye7d/yfvbUkIjYU0Oi+POMoTi0dBLuHBAqdigObwSLhckJDO3pD4kEKKmqR7mOs6BIHExqSDQqpSe85Fyf5lZG9LHV1VSxrqaLXbxax2nKraRSeiI2TAUAyLvAISgSB5Ma6nKCwCmfbdEvxA/+3p6oa7DgyCXW1XSl1TvOIPm1bKzewX2NWsNWV5PHuhoSCZMa6nKP/isfD7y/B/kce28VqVSCacN64JcjekGlZBlcV7LVfsWG+YkdilNIimoqFua/bRIJvyGpS1mtAnLOXUGNwcytEdrgpZ8NFDsEt/TXXyahuq4BSk9+VlvDVsh+tFSH+gYLh5epyzGpoS4lkQCbF4xGXlEVf/2SU/D35u7TrdXd3wthKiU0OgMOllQjtQ+3laCuxeEn6lISiQTRQT54MCkSHjJ+/NqiwWxFXlEVSqvrxQ7FLXC5/7aTSCRI5BAUiYhXFSIn8fSGA3hgTQ42HyoVOxS3cNefv8dDa3JwroLL/rfF8KYhqKIrdSJHQu6Iw0/UpV7aVIioIB88kNgDai8uOd8Ww3p2w75zVbCwB6HTabQGnCyrgVQCBPoqxA7HqUwd2h13x4UjRKUUOxRyQ0xqqMuU1xjwr70XIJEADyb1EDscp/PL1F6YNzoaEolE7FBc3o/3e2Ly3TasQSIxMamhLlPQtCBX/1A/bgzYDpwt1nVsu6LbVnMmIufAmhrqMrYFubh/0e0RBAE1BpPYYbi0vecai1xH9ObsnfbYe+4KZn+Qi5c2FYodCrkZJjXUZWxLp9sW6KK2236iDCmvZ2Ph+oNih+KyNFoDzlfqIZUASdxwtV0azFbsOlWB706Vix0KuRkOP1GXMJgsOFrauMR/Ui9eKNorxE+J8hoj9p9vLBiWSVlf09FYT3P7hvXqhmX3DGRSSF2OPTXUJQ6VVMNkERDip0CPbl5ih+O0BoSr4Kf0QI3RjGOlOrHDcUmsp7l9vgoP/GpUNAZ3V4sdCrkZJjXUJX489MTZO+0nk0qQ3PTr13bxpY7Fehoi58WkhrpEflNSM6wn62lul+1iy6Sm47GepuNo601Yn1uMldtOiR0KuREmNdTprFbBntTwQnH7UpqGRXKLuBBfR2M9TcepNZqx6PMjWLX9DOoazGKHQ26CSQ11unOVtdDWm6D0lGJQhErscJzewHAV/BQeqDGYcfwy62o6EutpOk53fy+Eq5UwWwUcLKkWOxxyE0xqqNPZ1qeJ7+EPT25ieds8ZFIMj2ZdTWc4UFwNgPU0HcW2JlV+03cAUWfjFYY6Hden6Xi2noScs0xqOtKXC0bhs0dTkcKkpkMkNSU1tu8Aos7GdWqo090bHwGV0hMTYkPEDsVlpPYOAgDkcr2aDqXwkCE5mkNPHcVWQ1dQfBVWqwApP6fUyZjUUKcb0y8YY/oFix2GSxkY0bRejcGMo6VaDOnhL3ZIRNeJDfODt1yGGoMZp8prEBvGmjrqXO0aflq9ejWioqKgVCqRkpKC3Nzcm7bfuHEjYmNjoVQqERcXh61bt9ofM5lMeP755xEXFwcfHx9ERERg9uzZKC0tbXaMqqoqzJo1CyqVCv7+/pg3bx5qa2vbEz6R05NJJUhhXU2HmvNBLpZ8WYiKGqPYobgMD5kUQ3v6A7hWW0fUmdqc1GzYsAEZGRlYunQpCgoKEB8fj/T0dJSXt7zHx549ezBz5kzMmzcPBw4cwNSpUzF16lQUFjZudFZXV4eCggK89NJLKCgowOeff46TJ0/i3nvvbXacWbNm4ejRo8jKysKWLVuwa9cuzJ8/vx1vmbrSD2cqsedMJad0dgJbMev5Sr3IkTi/i1frsPNUBT7ZVwwvOXdD70iJTdui5BVViRwJuQOJIAhtWugiJSUFw4cPx6pVqwAAVqsVkZGRePLJJ7Fo0aLr2k+fPh16vR5btmyx3zdixAgkJCRgzZo1Lb7G/v37kZycjAsXLqBnz544fvw4Bg4ciP379yMpKQkAkJmZibvvvhsXL15ERETELePW6XRQq9XQarVQqdgF2lUeeH8P8i5cxf89GI8HEnuIHY5LuapvQIPFilCVUuxQnF5dgxm7TlXgwpU6PDq2j9jhuJRdpyow+4Nc9Ojmhd3PTxA7HHJCbbl+t6mnpqGhAfn5+UhLS7t2AKkUaWlpyMnJafE5OTk5zdoDQHp6+g3bA4BWq4VEIoG/v7/9GP7+/vaEBgDS0tIglUqxb9++Fo9hNBqh0+ma3ajr9Qz0RoRaaZ/aSR2nm4+cCU0H8ZZ7YPLgcCY0nWBoT39IJcDFq/Uo0xnEDodcXJuSmsrKSlgsFoSGhja7PzQ0FBqNpsXnaDSaNrU3GAx4/vnnMXPmTHtGptFoEBLSfOaMh4cHAgICbnic5cuXQ61W22+RkZGteo/Usd55KAF7Fk9EVKC32KEQkQj8lJ7o31QgXMCp3dTJHGqdGpPJhIceegiCIOD999+/rWMtXrwYWq3WfispKemgKKk9uIll5zh8sRqzP8jFbz/JFzsUp1VaXY+V205hP2s+Ok1iL38A1/aAI+osbZrSHRQUBJlMhrKysmb3l5WVISwsrMXnhIWFtaq9LaG5cOECtm/f3mzcLCws7LpCZLPZjKqqqhu+rkKhgEKhaPV7o45XXmNAsK+CCU0n8pBKsetUBbzlMpgsVq7Y3A7fn67Aym2nsft0Jf7z+Eixw3FJE2JDYLEKGNU3SOxQyMW16RtQLpcjMTER2dnZ9vusViuys7ORmpra4nNSU1ObtQeArKysZu1tCc3p06exbds2BAYGXneM6upq5Odf+zW6fft2WK1WpKSktOUtUBcRBAH3vLcbQ1/JwklNjdjhuKzYMD+8fO8g/PfxkfDgwmbtYluVObUPVxHuLBNiQ7H850MwngtwUidr8+J7GRkZmDNnDpKSkpCcnIyVK1dCr9dj7ty5AIDZs2eje/fuWL58OQBg4cKFGDt2LFasWIEpU6Zg/fr1yMvLw9q1awE0JjQPPPAACgoKsGXLFlgsFnudTEBAAORyOQYMGIDJkyfjkUcewZo1a2AymbBgwQLMmDGjVTOfqOuVag0o0xnhIZWgZwDraTqLVCrBnJFRYofhtARBQE7TOj+p3BqByOm1OamZPn06KioqsGTJEmg0GiQkJCAzM9NeDFxcXAyp9FoH0MiRI7Fu3Tq8+OKLeOGFFxATE4NNmzZh8ODBAIBLly5h8+bNAICEhIRmr7Vjxw6MGzcOAPDJJ59gwYIFmDhxIqRSKaZNm4Z33323Pe+ZuoBt7HxghIrrfpDDOl+pR5nOCLlMimGcodepTBYrjpXqIJVIENdDLXY45KLavE6Ns+I6NV1r2eaj+GhPEX41MgrL7h0kdjguzWSx4ouCS8gtqsLr98dB7sG6mtb6ZN8F/OGLQqREB2DDoy0PoVPHWLvrLF7fegLpg0Lx118m3foJRE06bZ0aotYqKG7sqeGv384nk0jwRuYJ/Cf/Ig5frBY7HKfCepquk9irG9RenvBVeIodCrkwJjXU4eobLDhW2rjYIRfd63xSqQQjenMfqLYSBAF7zzVO42Y9TecbGtkNB166Eyseihc7FHJhTGqowx2+WA2zVUCoSoEINVe87Qq2faBymNS02pnyWlTWGqHwkCKhadNF6jxSqQRSztCjTsakhjpcftPQU2KvblyjpovYehryL1yF0WwRORrnYEsAk6K6QeHBYvaupDdyg1vqHExqqMMVXKgGAAzryaGnrtI3xBdBvnIYTFYcKtGKHY5TsNfTcOipyxwsqcaoN7bjgTU33vuP6HYwqaEOJQgCi4RFIJFIkNJ0cWZdza1ZrYL9PLFIuOuEq5W4VF2PkxodatlbQ52ASQ11qKIrdajSN0DuIcWgCE6d70r2upqzTGpu5WRZDa7WmeAtl2FID3+xw3EboSolenTzglUADpVUix0OuSAmNdShbLvwDumuZp1CF7MNoxQUX4XBxLqam1F7eWLhxBj8ckQv7pfVxWwzIrm5JXUG/mumDlV0RQ+AQ09i6BPsg2A/BYxmKw7yV/BNRfh74Zk7+2Hx3QPEDsXt2GrtmNRQZ2BSQx3q2Un9cXDJnXjkjt5ih+J2JBKJfQiKdTXkqGw9NQXFV2G1usWC9tSFmNRQh/P3liPYTyF2GG7Jtggf62purKSqDpmFGlTXNYgdiluKDfODl6cMNQYzzlTUih0OuRgmNUQuxFZXc6CkmnU1N5BZqMFj/85HxmeHxA7FLXnIpEiI9AfAISjqeExqqMOs3XUWM9fuxdeHL4sdituKDvLB3FFReHNanNihOCxvhQx9Q3wxklO5RcNiYeosHmIHQK5j95kryDl3BXfFhYkdituSSCRYeg93Rb+ZWSm9MCulFwSB9RxisdfVMKmhDsakhjrMkp8NRF5RFUb2CRI7FKJb4hYe4hnatNfWuUo9qvQNCPCRixsQuQwOP1GH6RviixnJPdEz0FvsUNxe4SUt/vLdGVzVsxj2xy5erePeWA7A31uOviG+ANhbQx2LSQ2RC/rdxkN4K/Mkvj9TKXYoDuXJTw8g4eUsfHeyXOxQ3F5i03o1xy/rRI6EXAmHn6hDrNtXDLPVijsHhiJc7SV2OG4vfVAYenTzQiC79e20dSYcKqmGVQD6hfqJHY7bWzChLxamxSDCn98X1HGY1FCH+Pv353CuUo8e3byY1DiAZ+7sJ3YIDueHs5WwCo3DpLyQii8ygMPU1PE4/ES3rUrfgHOVjdsjDI3k9gjkmHadqgAAjIkJFjkSIuosTGroth0obiz06xPsg24c7nAoJVV1OKFhzYIgCNeSmn6cnecoth65jN98vB+f5ZWIHQq5CCY1dNsKmpIa20Z15BjW7SvGHW/twPKtJ8QORXRnK2pRqjVA7iFFSjQX3XMU5ypqse14uT3hJLpdrKmh22ZbFTSRO3M7FNvfx77zV2AwWaD0lIkckXh2nmqcBZYcFQAvufueB0eTNjAUSk+ZfSNWotvFpIZui8lixcGSagDAMCY1DqVfqC9C/BQorzEir+gqRse477DL96c59OSIYsNUiA1TiR0GuRAOP9FtOVaqg8FkhUrpgb7BvmKHQz8ikUhwR1NRrO2i7o4MJgv2nmvctXxMPxYJE7kyJjV0W/Kahp6SogIglXLZeUdj65nYddp9F+HLK7oKg8mKUJUC/bk+jcMprzHgv/kXsflQqdihkAtgUkO3Jf9CFQDW0ziq0X0bk5rjl3UorzGIHI04djX1Ut0RE8z9nhxQ7vkqPLvxEP6686zYoZALYFJD7SYIAvKKmnpqmNQ4pEBfBQZ3b6xZ2O2mvTW2mTV3uHFNkSNL6hUAoDHxrjWaRY6GnB2TGmq3i1frUV5jhKdMgvhIf7HDoRsYY6+rcb+kxmi2wN/bE3IPqb2+iBxLmFqJHt28YBWurXlF1F5Maqjd9hc1Dj0N7q526+nCju7HxcJWqyByNF1L4SHD+vmpOLx0EgK4MKTDGh7V2Ftj6/klai8mNdRufYJ9MSe1F+4ZEiF2KHQTib26wVsuQ2VtA4676erCTLodm60mL6+pRo+ovbhODbVbfKQ/h52cgNxDitTegcg+UY7vT1diUIRa7JC6hNUqoLrexB4aJ2DrqTlQXA2zxQoPGX9vU/vwk0PkBmxFsu60HP3RUh0SX83CzLV7IQjuNezmbGJCfKFSeqCuwYLjl2vEDoecGJMaapeiSj32F1XBYLKIHQq1wph+wYhQK9E3xNdtLvCHL1VDEABfpQencjs4qVTCISjqEBx+onbZmF+C1TvO4sHEHnj7wXixw6FbiA7ywQ+LJrjVxX1WSi/cOSAUNZwm7BSSogKw42QF8oquYu6oaLHDISfFpIbaxUMqRZCvgovuOQl3SmZ+LESlRIjYQVCrJP2op0YQBLf9zNLtYVJD7fLMnf3wdFoMLG42RdjZWawCjl/WYXB39ygWJucRH+kPT5kEZTojLl6tR2SAt9ghkRNiTQ21m0Qi4SwFJ2IwWZD82jb87L3dKK2uFzucTvXqlmN4+O/78MMZ91tw0FkpPWX2ZJt1NdRevCJRm+mNZrcpNnUlSk8ZegZ6w0/pgXMVerHD6VRZx8uw+0wl6hpYyO5M7hochmnDeqBHN/bSUPtw+Ina7NnPDiHvwlW8OnUwJg8OEzscaoM1Dyci0Efu0j1sF67oceFKHTykEqT2CRQ7HGqD+WP6iB0COTkmNdQmgiAg78JVVNYaEejLRc2cTahKKXYIne7bo2UAGhd081XwK47InbjuzzXqFMVVdaisNUIukyKOxaZOSxAE1BhMYofRKb4pvAwAuDuOvYjOyGIVcLRUi5KqOrFDISfEpIbaZH/ThnNxPbiJpbPKPl6G0W/uwAtfFIodSoe7rK1HQXE1JBIgfRCTGmf00peFmPLubqzLLRY7FHJC7UpqVq9ejaioKCiVSqSkpCA3N/em7Tdu3IjY2FgolUrExcVh69atzR7//PPPMWnSJAQGBkIikeDgwYPXHWPcuHGQSCTNbo899lh7wqfbkN80KyGJ69M4rQAfOS5V12P78TKXWxE6s1ADoPHzGeIGQ22uKKGHP3wVHlwugtqlzUnNhg0bkJGRgaVLl6KgoADx8fFIT09HeXl5i+337NmDmTNnYt68eThw4ACmTp2KqVOnorDw2q9EvV6P0aNH480337zpaz/yyCO4fPmy/fbWW2+1NXy6TXlNPTVcdM95xffwR7haCX2DBbtPu9aU52+akprJg8NFjoTa676hETi0dBJeuHuA2KGQE2pzUvPOO+/gkUcewdy5czFw4ECsWbMG3t7e+OCDD1ps/+c//xmTJ0/G73//ewwYMACvvPIKhg0bhlWrVtnb/PKXv8SSJUuQlpZ209f29vZGWFiY/aZSqdoaPt2G6roGnC6vBcCkxplJpRL70IwtCXAF5TUG7C9q7EnkrDznpfCQQSblasLUPm1KahoaGpCfn98s+ZBKpUhLS0NOTk6Lz8nJybkuWUlPT79h+5v55JNPEBQUhMGDB2Px4sWoq7txIZnRaIROp2t2o9uTf6Gxl6Z3sA8CfRUiR0O3466mi/6242UwWawiR9Mx/ne0DIIAJET6o7u/l9jhUAdoMLvGZ5O6TpuSmsrKSlgsFoSGhja7PzQ0FBpNy7/4NBpNm9rfyC9+8Qv8+9//xo4dO7B48WL861//wsMPP3zD9suXL4darbbfIiMj2/R6dL28pqSG9TTOLykqAEG+cmjrTcg5e0XscDpEZtOsp7vYS+P0dpwsx9i3d+C3nxSIHQo5GadZxGH+/Pn2/4+Li0N4eDgmTpyIs2fPok+f6xdsWrx4MTIyMux/1ul0TGxuU36RLakJEDkSul0yqQSTBoVh3b5ifFOowZh+wWKHdFuu1Bqx91zj0NNdrKdxeiqlJy5cqYOu3sTNLalN2tRTExQUBJlMhrKysmb3l5WVISys5V9HYWFhbWrfWikpKQCAM2fOtPi4QqGASqVqdqP2M5otOHixGgCQGMWeGldg69HIOqZx+pkmJzQ1UHhIMShChZ6BXGLf2Q3uroLCQ4qrdSacdfEtPahjtSmpkcvlSExMRHZ2tv0+q9WK7OxspKamtvic1NTUZu0BICsr64btW8s27Ts8nL/KukLhJR0azFYE+MjRO8hH7HCoA4zoHQi1lycqaxvsBbbOalTfIBS8dCdW/WKY2KFQB1B4yBDfwx/AtWUkiFqjzbOfMjIy8Le//Q0ff/wxjh8/jscffxx6vR5z584FAMyePRuLFy+2t1+4cCEyMzOxYsUKnDhxAsuWLUNeXh4WLFhgb1NVVYWDBw/i2LFjAICTJ0/i4MGD9rqbs2fP4pVXXkF+fj6KioqwefNmzJ49G2PGjMGQIUNu6wRQ65zQNBZaJ/bqxq5gF+EpkyJtQGO9W6YLzIJSesoQzYTbZSQ19QjbFvwkao0219RMnz4dFRUVWLJkCTQaDRISEpCZmWkvBi4uLoZUei1XGjlyJNatW4cXX3wRL7zwAmJiYrBp0yYMHjzY3mbz5s32pAgAZsyYAQBYunQpli1bBrlcjm3btmHlypXQ6/WIjIzEtGnT8OKLL7b7jVPbzErphUkDw6A3msUOhTrQXYPD8N+Ci8gs1GDJzwZC6oRTaesazPCWO015ILWSLamxzbokag2JIAjOPZjeSjqdDmq1GlqtlvU1RE0MJgsSX8mCvsGCz387EsN6Ol+91G8+3o8LV+rw8n2DMLJPkNjhUAfR1pkQ/8dvAQD7/5CGYD8uI+Gu2nL95t5PRG5M6SnDhAGh6B3sA129821waTRbsPdcFU6X1yKIaye5FLW3J/qF+gJgXQ21Hvts6ZY25pXgy4OleDCpB+5L6C52ONTB3po2BEpPqVPWSik8ZPhh0QTsOVOJmBBfscOhDpYcHYBTZbXYd76KW19Qq7Cnhm5p56kK7D5TiaLKG6/gTM7LSy5zyoTGRu3libviwp36PVDLRvQOBAD7GkREt8KeGrqlp9P6ITk6ACnRgWKHQp3IYLLgUnU9+gQ7R48HF2VzfbbvnBMaHarrGuDvLRc5InJ07KmhW+ob4ovZqVHoH+YndijUSfYXVSHxlSw8+q98sUNptW8KNbhv1W58tr9E7FCokwT7KdA3xBeCAOw7z94aujUmNUSE/mF+aLBYoTeaUaVvEDucVtl65DIOXdTibEWt2KFQJxrRu3FbliMXtSJHQs6Aw090U//KKYJMKsWdA0M5pdKFqZSeyHx6DKIDfZxirRqDyYIdJ8oBAHfFsYDUlc2/ow8euaM3egZw+wu6NSY1dEOCIGDVjjMo0xkRHeTDpMbFOUstDQB8d7Ic+gYLItRKxPdQix0OdSLu5UVtweEnuqELV+pQpjNCLpNiaE9/scOhLmIwWXBZWy92GDf1773FAIB7EiJYLExEdkxq6Ib2nrsCAEiI9IfSUyZyNNQVtp8oQ+rybLzw+RGxQ7mh02U12H2mElIJ8MsRvcQOh7rAnrOVePRfeVi57ZTYoZCDY1JDN2SbbWAr1CPXFx3ki6t1Jnx3qgJFlXqxw2nRh3uKAACTBoahRzcOTbiDK7UN+N/RMnx7tEzsUMjBMamhFgmCYO+pSenN9WncRXSQD8b1D4YgAP/MuSB2ONfR1pnwecFFAMCvRkWJGwx1mdQ+gXhucn+8dv/gWzcmt8akhlpUXFWHy1oDPGUSp9zkkNrvVyOjADRuj+Fou7JvyCuGwWRFbJgfUqLZg+gugnwV+O24vhjK7yK6BSY11KJ9TcuSx/fwh5ec9TTuZExMMKKDfFBjNNt7RRyBxSrg4z2NvUe/HhXNAmEiug6TGmqRbehpBIee3I5UKsHs1MYC3I9zLkAQBJEjapR1rAyXquvRzdsT9yZEiB0OdTG90YwvD17Cu9mnxQ6FHBiTGrqOIAj2IuEUFgm7pQcSe8BHLsOZ8lr8cOaK2OEAAD7acx4A8IuUnpyN54ZqjWYsXH8Qf9p2Cto6k9jhkINiUkPXuXi1Hpeq6+EhlSCxF8ew3ZGf0hMPJPYAAHzUNNtITBargKE9uyHIV4GHOY3bLYWqlOgd5ANBaNyrjKglTGroOrahpyE91PCWc9FpdzW7qWA4+0QZSqrqRI1FJpXg+cmx2Lt4AsLVXqLGQuKxzcS0fUcR/RSTGrrO3nO2oSfW07izPsG+uCMmqGl6d5HY4QAAPGT8ynJntjWz9p5nUkMt4zcEXefuuDDMTO6JCbEhYodCIpvbtBbMhv0lqGsQZ3r31iOXsfNUBaxWxyhYJvHYJi4cLdVBW8+6Groekxq6zsQBoVj+8zgMj2KRsLsb1y8EvQK9oTOY8eXB0i5/fZPFile2HMOcD3KxtfByl78+OZZmdTXnWVdD12NSQ0Q3JJVKkHFnP7xy3yDcG9/106gNJgsmDw5DVKA37hwY2uWvT47HNiOTdTXUElaBUjM7TpTD39sTg7ur4cn6BQJwX0J30V7bT+mJpfcMwktTBkIq5WJ71DgE9WluCetqqEW8alEzSzYX4v6/7MHuM5Vih0IOqL7BIsrrMqEhm5ToxrqaY6yroRYwqSE7g8mCAWEqBPnKkcT1aegndp6qwPj/+w5Zxzp/p2SrVcBz/zmE/Ausm6DmwtRKRAf5wCoAeVyvhn6CSQ3ZKT1lWDs7Cfv/kAY/pafY4ZCD2XOmEhqdAX/bda7Tt074cE8RPsu7iDkf7OevcbrOCNbV0A2wpoauw40CqSXP3NkPfkoPzBvdu1M/I6fLavBm5gkAwKK7YqH2YoJNzdnras6xp4aaY1JDdhev1qG7vxeTGmqR0lOGBRNiOvU1GsxWPL3hIBrMVozrH4xZKT079fXIOY3oHYh74yMwsg8XCKXmOPxEAIDL2nqMfnMH7nhrBxrMVrHDIQdntQr48IfzKNMZOvS472afxtFSHbp5e+KtaUOYYFOLQlVKvDtzKGYkM+ml5pjUEADg+9ONs50CfRWQe/BjQTf38ldH8fJXx/Dcfw53WH1N/oUq/OW7MwCA1++PQ4hK2SHHJSL3wasXAQB2naoAAIyNCRI5EnIGv0ztBbmHFDtPVWD9/pLbPp7eaEbGZ4dgFYCfD+2Ou+LCOyBKcmWCIOBMeQ0+y7v9zx+5DiY1BItVsK9LM6ZfsMjRkDPoG+KH59L7AwBe3XLstnfxfvXr47hwpQ4RaiWW3TeoI0IkF6erN2PSn3bhuf8cxmVtvdjhkINgUkMovKRFdZ0JfgoPxEf6ix0OOYlfj4pGcnQA9A0WPLvxULs3nNx+ogyf5hYDAP7voXiouJwAtYLa2xPDowIwum8QdPXibLZKjodJDdmHnkb2DeTWCNRqUqkE//dAPLzlMuSer8I/dp9v8zGu1Brx3H+OAADmjY7GyD4c/qTWWz9/BP79mxT0D/MTOxRyELyCEXadbkxqOPREbdUz0BsvThkIAHht63HM+2g/jlzUtvr5j/wzD5W1RsSE+OL3TcNZRK3F2XH0U0xq3FyNwYSC4moAwJgYJjXUdjOTI/GrkVGQSoDsE+W4Z9Vu/ObjPBReuj65qW+wwGy5tmTAPfERCPKV488zhkLpKevKsMmFlOsMqKw1ih0GOQAmNW5uz9krsFgFRAf5IDLAW+xwyAlJJBIsu3cQtmWMxf1Du0MqAbYdL8PP3tuNJz4psNfa/GvvBYx+czu+PnLZ/tyZyT2x67nxGBihEit8cnKvfX0Mya9n4997L4gdCjkAJjVuzlZPM4ZTuek29Q72xZ+mJ+DbZ8bivoQISCSAwlNq32FbW9eAK/oGbDpwyf4cpacM3nIubE7t1yfYF8C17zJyb/w2cWOCILCehjpc3xBf/HnGUCwY3xfeimtfMbNHRiFc7YV7EyJEjI5cje2762BJNbR1Jqi9OXvOnbGnxo1duFKHkqp6eMokGNGbe6hQx4oJ9UN3fy/7n1VKT0xL7MEZdtShIvy90DfEF1YB+OFspdjhkMj47eLGynQG9Ar0RmKvbvBRsNOOiJyTbZIDh6CIVzI3ltI7EDt/Px56IxeuIiLnNaZfED744Tx2naqAIAic6u3G2tVTs3r1akRFRUGpVCIlJQW5ubk3bb9x40bExsZCqVQiLi4OW7dubfb4559/jkmTJiEwMBASiQQHDx687hgGgwFPPPEEAgMD4evri2nTpqGsrKw94dNPsJeGiJxZSnQg5B5SlGoNOFNeK3Y4JKI2JzUbNmxARkYGli5dioKCAsTHxyM9PR3l5eUttt+zZw9mzpyJefPm4cCBA5g6dSqmTp2KwsJCexu9Xo/Ro0fjzTffvOHrPvPMM/jqq6+wceNG7Ny5E6Wlpfj5z3/e1vCpSY3BBNOP1gshInJWXnIZUqIDAAA7OQTl1iSCILRpw5aUlBQMHz4cq1atAgBYrVZERkbiySefxKJFi65rP336dOj1emzZssV+34gRI5CQkIA1a9Y0a1tUVITo6GgcOHAACQkJ9vu1Wi2Cg4Oxbt06PPDAAwCAEydOYMCAAcjJycGIESNuGbdOp4NarYZWq4VKxTUx3so8gX/mXMDTaTH4zR29xQ6HiOi2/G3XOby29TjG9AvGP3+dLHY41IHacv1uU09NQ0MD8vPzkZaWdu0AUinS0tKQk5PT4nNycnKatQeA9PT0G7ZvSX5+PkwmU7PjxMbGomfPnm06Dl1z6GI1ao1mBPjIxQ6FiOi22aZ27zt3BQaTReRoSCxtKqaorKyExWJBaGhos/tDQ0Nx4sSJFp+j0WhabK/RaFr9uhqNBnK5HP7+/q0+jtFohNF4bdlsnU7X6tdzB//6dQqOlurQk6sIE5EL6BfqizCVEhqdAbnnq7j2lpty2Sndy5cvh1qttt8iIyPFDsmhSKUSxPVQc6EqInIJEokEdzStjM6p3e6rTUlNUFAQZDLZdbOOysrKEBYW1uJzwsLC2tT+RsdoaGhAdXV1q4+zePFiaLVa+62kpKTVr0dERM5nTL9gBPnK4SXn5qjuqk1JjVwuR2JiIrKzs+33Wa1WZGdnIzU1tcXnpKamNmsPAFlZWTds35LExER4eno2O87JkydRXFx8w+MoFAqoVKpmN2rcGuGe93bjdxsP4Qp3tSUiF3LX4DDkvpCGZyf1FzsUEkmbFyjJyMjAnDlzkJSUhOTkZKxcuRJ6vR5z584FAMyePRvdu3fH8uXLAQALFy7E2LFjsWLFCkyZMgXr169HXl4e1q5daz9mVVUViouLUVpaCqAxYQEae2jCwsKgVqsxb948ZGRkICAgACqVCk8++SRSU1NbNfOJrjl+uQZHLmlxprwWr90/WOxwiIg6jAe34HB7bU5qpk+fjoqKCixZsgQajQYJCQnIzMy0FwMXFxdDKr32wRo5ciTWrVuHF198ES+88AJiYmKwadMmDB587YK6efNme1IEADNmzAAALF26FMuWLQMA/OlPf4JUKsW0adNgNBqRnp6Ov/zlL+160+7MtoHliN4BUHiwi5aIXI8gCCjVGprtPUbuoc3r1DgrrlPTaNbf9+KHM1ew9J6BmDsqWuxwiIg61IUrejywJgcNZisKXroTMim3THB2nbZODTm3ugYz9p+/CgCc7khELqm7vxcMJgsazFYUXdGLHQ51MW7640b2nruCBosV3f290DvIR+xwiIg6nIdMiv88NhJRQd4cYndDTGrcyLdHG6fWj48N5i62ROSy+of5iR0CiYTDT27CYhXw7bHGpGbyoHCRoyEi6nyCIMDMjXvdCpMaN7G/qApV+gaovTyR0jtA7HCIiDrV+txiTFixE//MuSB2KNSFmNS4if8dbdwjK21AKDy5lgMRubh6kwXnK/XIPNr6fQbJ+fHq5gYEQcD/Chv/YacPCr1FayIi55c+qHELnf1FVaio4erp7oJJjRs4ckmLUq0BXp4yTuUmIrcQ4e+F+B5qCAKw7XjZrZ9ALoFJjRsouNC4Ns342GAoPTnFkYjcQ/rgxt6abwo5BOUuOKXbDfxqVDQmDQpDvckidihERF1m8qAwvJV5EnvOVEJbb4Lay1PskKiTsafGTUT4e6FPsK/YYRARdZnewb7oF+oLs1XA9hMcgnIHTGpcnNXqFlt7ERG1aHJTwXAmh6DcApMaF/fAmj2Y80EuzpTXih0KEVGXs9XV7DxVgboGs8jRUGdjUuPCLmvrUVBcje9PV8Dfm2PJROR+BoarEBngBYPJil2nKsQOhzoZkxoXFq72wraMsXj7gXgE+SrEDoeIqMtJJBIOQbkRJjUurm+IL6Yl9hA7DCIi0UxuGoLKPl6OBjP3gnJlTGqIiMilDY3shmA/BdTenii5Wid2ONSJuE6Ni/riwEVkHSvDjOE9uYowEbk1qVSCrxaMRqhKAYlEInY41InYU+OivjxYiq1HNDhySSt2KEREogtTK5nQuAEmNS5IZzBhz5krALiBJRHRjzWYrdDWmcQOgzoJkxoXtONEORosVvQJ9kHfED+xwyEicgif5hYj6dUs/GnbKbFDoU7CpMYF/e9o47RFW8U/EREBQb4K6AxmFBRfFTsU6iQsFHYxBpMFO040LjCVPohJDRGRzR0xQfjs0VQk9uomdijUSZjUuJjvT1ei3mRBhFqJuO5qscMhInIYSk8ZkqMDxA6DOhGHn1zMpgOXADTud8JKfyKiljWYrRAEbvjrapjUuJCr+gZkHSsDADzAVYSJiFr0p6xTSF2ejX3nq8QOhToYkxoX8uXBS2iwWDEwXIVBERx6IiJqSXmNAVf0Dfgsr0TsUKiDMalxIRvzLwIAHkpiLw0R0Y08mBQJANh65DJqDFyzxpUwqXERR0u1OFqqg1wmxX0J3cUOh4jIYQ2N9EffEF8YTFZsOXxZ7HCoAzGpcRFHL+ngKZPgzoGh6OYjFzscIiKHJZFI7D3aHIJyLZzS7SIeGh6JtIGh0BvNYodCROTw7h/aA29mnsSB4mqcLqtBTChXX3cF7KlxIQE+ckQGeIsdBhGRwwv2U2BCbAiAa/WI5PyY1LiAcp1B7BCIiJzOQ00Fw58XXITJYhU5GuoITGqcnEZrQOob2/Hgmj0wmCxih0NE5DTG9Q9GkK8ClbUN2HGiXOxwqAMwqXFyuUVV9lUxlZ4ykaMhInIenjIppg1rnC36WR6HoFwBC4Wd3L3xEUiOCkCVvkHsUIiInM6DST3w113nsONkOcprDAjxU4odEt0G9tS4gDC1EgMjVGKHQUTkdPqG+GFYT39YrAK+KLgkdjh0m9hT48R0BhNUSk+xwyAicmoPJUWi3mRFmJq9NM6OSY2T0hvNGLl8OxIi/bHqF0Ph780F94iI2uOhpEjMSO4pdhjUATj85KS+PnIZtUYzLlXXQ+3F3hoiovaSSiVih0AdhEmNk/pPU6X+A4k9IJHwHyQR0e2qNZqxYX8x6hq4Mruz4vCTEzpfqUduURWkEmDaMO7ITUTUER5ck4Pjl3XwkEoxLZHfrc6ISY0Tsm3ANqZfMAvbiIg6yJS4MBjNFsg9OIjhrNr1N7d69WpERUVBqVQiJSUFubm5N22/ceNGxMbGQqlUIi4uDlu3bm32uCAIWLJkCcLDw+Hl5YW0tDScPn26WZuoqChIJJJmtzfeeKM94Tu1WqMZ6/YVAwBmDI8UORoiItcxf0wfZGeMxT3xEWKHQu3U5qRmw4YNyMjIwNKlS1FQUID4+Hikp6ejvLzlJab37NmDmTNnYt68eThw4ACmTp2KqVOnorCw0N7mrbfewrvvvos1a9Zg37598PHxQXp6OgyG5nsa/fGPf8Tly5fttyeffLKt4Tu99bnF0Nab0DvIB3cODBM7HCIilyH3kLJG0clJBNsa+62UkpKC4cOHY9WqVQAAq9WKyMhIPPnkk1i0aNF17adPnw69Xo8tW7bY7xsxYgQSEhKwZs0aCIKAiIgIPPvss/jd734HANBqtQgNDcVHH32EGTNmAGjsqXn66afx9NNPt+uN6nQ6qNVqaLVaqFTOuVCd0WzBmLd2oExnxJvT4jB9OKcgEhF1NKPZgs8LLiG1dyCignzEDsftteX63aaemoaGBuTn5yMtLe3aAaRSpKWlIScnp8Xn5OTkNGsPAOnp6fb258+fh0ajadZGrVYjJSXlumO+8cYbCAwMxNChQ/H222/DbL5xhbrRaIROp2t2c3ZfHihFmc6IMJUSU4d2FzscIiKXtPi/R7D48yP4666zYodCbdSmpKayshIWiwWhoaHN7g8NDYVGo2nxORqN5qbtbf+91TGfeuoprF+/Hjt27MCjjz6K119/Hc8999wNY12+fDnUarX9Fhnp3PUnFquANU3/wH5zRzQUHty8koioM/wipbEX/L/5l1CmM9yiNTkSpynxzsjIwLhx4zBkyBA89thjWLFiBd577z0YjcYW2y9evBhardZ+Kykp6eKIO9a3RzU4V6GH2suTK18SEXWipKgAJEcFoMFixd+/Pyd2ONQGbUpqgoKCIJPJUFZW1uz+srIyhIW1XLQaFhZ20/a2/7blmEBjbY/ZbEZRUVGLjysUCqhUqmY3ZyUIAt7f2dhLMye1F3wVnIlPRNSZHh/XBwDwyb5iVNc1iBwNtVabkhq5XI7ExERkZ2fb77NarcjOzkZqamqLz0lNTW3WHgCysrLs7aOjoxEWFtasjU6nw759+254TAA4ePAgpFIpQkJC2vIWnJJEIsHr98fhvoQI/GpUtNjhEBG5vHH9gzEgXIW6Bgv+mXNB7HColdr8kz8jIwNz5sxBUlISkpOTsXLlSuj1esydOxcAMHv2bHTv3h3Lly8HACxcuBBjx47FihUrMGXKFKxfvx55eXlYu3YtgMYL9tNPP41XX30VMTExiI6OxksvvYSIiAhMnToVQGOx8b59+zB+/Hj4+fkhJycHzzzzDB5++GF069atg06FYxvcXY0/zxgqdhhERG5BIpHg8XF98NSnB/DhD+fxmzui4S1nL7mja/Pf0PTp01FRUYElS5ZAo9EgISEBmZmZ9kLf4uJiSKXXOoBGjhyJdevW4cUXX8QLL7yAmJgYbNq0CYMHD7a3ee6556DX6zF//nxUV1dj9OjRyMzMhFLZuFquQqHA+vXrsWzZMhiNRkRHR+OZZ55BRkbG7b5/hycIAtdNICISwd2Dw7Ai0BsXrtRhfW4Jfj2aPeWOrs3r1DgrZ12n5un1B+Ct8MCC8X0R4e8ldjhERG7lk30X8IcvChGuVmLn78dzCwURdNo6NdS1Sqrq8OWhUqzbV4xaI3eNJSLqatOG9UCwnwKXtQZ8efCS2OHQLTCpcWA9unlhw/xUPHtnP/QL9RM7HCIit6P0lOE3TcNOa3aehdXqFoMbTotJjQOTSCRIjg7AkxNjxA6FiMht/SKlJ1RKD5yt0OPbYy0vNEuOgUmNg6oxmMQOgYiIAPgpPTE7NQp+Sg9creN3syNjobADKqrU4573dmPOyCg8nRYDDxlzTyIiMekMJkjQmOBQ12rL9ZuT7h2MIAh4+aujqDGacehiNWRSTucmIhKbismMU2AXgIPZdrwcO05WwFMmwcv3DuIaNUREDmbHiXJ8sPu82GFQC9hT40AMJgv+uOUoAOA3d/RG72BfkSMiIqIfKyi+irkf7YenTIIx/YLRN4Tf046ESY0DWbPzLEqq6hGuVmLB+L5ih0NERD8xNNIfkwaGIirIB2Fqpdjh0E8wqXEQJVV1eP+7xp24/zBlAHy4EzcRkcORSCRY83AipKx3dEisqXEQL391DEazFSP7BGJKXLjY4RAR0Q38OKGxWgU0mK0iRkM/xqTGAew4UY5tx8vgIZXgj/exOJiIyBkcv6zDg3/Nwcptp8QOhZowqRGZ0WzBy181Fgf/enQ0+oZwOwQiImdQUlWH/AtX8bfvz+FsRa3Y4RCY1Ijub7vOoehKHUL8FHiK2yEQETmNOweGYnz/YJgsApZtPgo3WcvWoTGpEdHFq3VYteMMgMbiYF8WBxMROQ2JRIKl9wyCXCbF96crkVnIfaHExqRGRLp6M3oF+CAlOgD3xkeIHQ4REbVRVJAPHhvbGwDwypZj0BvNIkfk3pjUiGhghApfPzUaq34xjMXBRERO6vFxfdGjmxdKtQYs/vwIh6FExKRGBHUN1zJ5D5kUwX4KEaMhIqLb4SWX4Z2HEiCTSrD5UCk+/KFI7JDcFpOaLlZRY0Taip1Ytf00rFZm80REriA5OgAv3D0AAPD61uPIPV8lckTuiUlNF9t8qBSlWgM2HSyFwWwROxwiIuogvx4VhXvjI2C2CnhiXQHKdQaxQ3I7nG7TxeaNjoaf0gPDenaDt5ynn4jIVUgkErwxLQ6nympwQlOD335SgHWPjIDcg/0HXYVnWgQPJUVyZ1ciIhfkLffAmocT4af0QN6Fq/hnTpHYIbkVJjVd4Ex5DR77Vz6q9A1ih0JERJ0sKsgHK6cnYHZqL8xOjRI7HLfC8Y9OVms049F/5eNshR5echn+ND1B7JCIiKiTTRwQiokDQsUOw+2wp6YTWawCfr/xEM5W6BGmUtor44mIyH2YLVas3nEG1XXsre9sTGo6icliRcZnB/FNoQaeMgn+8vAwrkdDROSGFn1+BG//7yR+9eF+LuXRyZjUdAKj2YLfflKALw+WwkMqwZ+mJ2BYz25ih0VERCKYP6Y3wtVKLBjfF1IpV4/vTKyp6WB1DY01NN+froTcQ4o1Dw/DhFiOqxIRuat+oX7Y8btxUHrK7PcJgsDtcToBe2o6kM5gwux/5OL705Xwlsvw0a+GM6EhIqJmCU3xlTrct/oHnCqrETEi18SkpoNU6Rsw62/7kHfhKlRKD/z7NykY2TdI7LCIiMjBvPL1MRy+qMVDf83B4YvVYofjUpjUdIBynQHT/5qDI5e0CPSR49P5I1hDQ0RELXr7gSFIiPRHdZ0Jv/jbPuw7d0XskFwGk5oOsOjzIzhdXoswlRIbHk3FoAi12CEREZGD8veW49+/SUFq70DUGs2Y/UEuthwuFTssl8CkpgO8dv9gjOobiI2PpXL7AyIiuiVfhQc+nDscE2JDYDRbsWDdATz16QGuZXObJIIguMWkeZ1OB7VaDa1WC5VKJXY4REREMFmseC/7NFZ/dxYWq4AQPwXenDYE42NDxA7NYbTl+s2eGiIiIpF4yqTImNQf/318JPoE+6C8xoi5H+3Hov8eRo3BJHZ4TodJDRERkcgSIv3x9VN34DejoyGRAOv3l2Dyyu+x52yl2KE5FSY1REREDkDpKcOLPxuITx8ZgR7dvHCpuh7fn2ZS0xZcUZiIiMiBjOgdiMynx+Dv35/D4+P62O/PK6qCl1zGGbY3wZ4aIiIiB+Or8MDTaf2g8GhciVgQBLy4qRBT3t2NTQcuiRyd42JSQ0RE5OBqjWb0C/WDSumBcf2D7fcXVephMFlEjMyxcPiJiIjIwfkpPfHuzKHQGUxQKT3t9y/4tADnKvSYEBuCuwaHY1z/YPgo3PfS7r7vnIiIyMn8OKGpMZhwVW9CXYMFWw5fxpbDl6HwkGJsv2DcHReO8bEhUHt53uRoroeL7xERETkpQRBw6KIW3xRexjdHNCiuqrM/JpEAfYN9kRDpj4Se/hga2Q39Qn3hIXOuypNOX3xv9erViIqKglKpREpKCnJzc2/afuPGjYiNjYVSqURcXBy2bt3a7HFBELBkyRKEh4fDy8sLaWlpOH36dLM2VVVVmDVrFlQqFfz9/TFv3jzU1ta2J3wiIiKXIJFIkBDpj8V3DcDO34/D10+NxpMT+qJPsA8EAThdXouN+Rfxhy8Kcfe73yNu2bco0xnszz9fqUdJVR0sVtfo32hzUrNhwwZkZGRg6dKlKCgoQHx8PNLT01FeXt5i+z179mDmzJmYN28eDhw4gKlTp2Lq1KkoLCy0t3nrrbfw7rvvYs2aNdi3bx98fHyQnp4Og+HaiZ81axaOHj2KrKwsbNmyBbt27cL8+fPb8ZaJiIhcj0QiwaAINZ6d1B/Zz47D/j+k4e+zk/DE+D4Y1TcQvgoPeMllCPFT2J/z2tfHcMdbO7Aut9h+3+myGqzafhqf5hbj26MaFBRfRfGVOtQ1mMV4W23S5uGnlJQUDB8+HKtWrQIAWK1WREZG4sknn8SiRYuuaz99+nTo9Xps2bLFft+IESOQkJCANWvWQBAERERE4Nlnn8Xvfvc7AIBWq0VoaCg++ugjzJgxA8ePH8fAgQOxf/9+JCUlAQAyMzNx99134+LFi4iIiLhl3Bx+IiIid2a1CtDoDIjw97LfN++j/fj+dCX+8ask3BHTOKvqv/kX8ezGQy0eQ+kphUrpCV+lB3wV127+3p5464H4Tom704afGhoakJ+fj7S0tGsHkEqRlpaGnJycFp+Tk5PTrD0ApKen29ufP38eGo2mWRu1Wo2UlBR7m5ycHPj7+9sTGgBIS0uDVCrFvn37Wnxdo9EInU7X7EZEROSupFJJs4QGAP7xq+E4/spkpPYOtN8XGeCN6UmRmBgbgvhIf3T394LCozFdMJisKK8x4lyFHocvarHn7BV8e6wM/zta1qXv5UbaNPupsrISFosFoaGhze4PDQ3FiRMnWnyORqNpsb1Go7E/brvvZm1CQprvWOrh4YGAgAB7m59avnw5Xn755Va+MyIiIvckk0oASOx/To4OQHJ0QLM2giBA32BBVW0Daowm1BrM0DeYUWMwo9boOMNSLjule/HixcjIyLD/WafTITIyUsSIiIiInJNEIrEPNTmyNg0/BQUFQSaToayseTdTWVkZwsLCWnxOWFjYTdvb/nurNj8tRDabzaiqqrrh6yoUCqhUqmY3IiIicl1tSmrkcjkSExORnZ1tv89qtSI7OxupqaktPic1NbVZewDIysqyt4+OjkZYWFizNjqdDvv27bO3SU1NRXV1NfLz8+1ttm/fDqvVipSUlLa8BSIiInJRbe5HysjIwJw5c5CUlITk5GSsXLkSer0ec+fOBQDMnj0b3bt3x/LlywEACxcuxNixY7FixQpMmTIF69evR15eHtauXQugsUvr6aefxquvvoqYmBhER0fjpZdeQkREBKZOnQoAGDBgACZPnoxHHnkEa9asgclkwoIFCzBjxoxWzXwiIiIi19fmpGb69OmoqKjAkiVLoNFokJCQgMzMTHuhb3FxMaTSax1AI0eOxLp16/Diiy/ihRdeQExMDDZt2oTBgwfb2zz33HPQ6/WYP38+qqurMXr0aGRmZkKpVNrbfPLJJ1iwYAEmTpwIqVSKadOm4d13372d905EREQuhNskEBERkcPq9G0SiIiIiBwNkxoiIiJyCUxqiIiIyCUwqSEiIiKXwKSGiIiIXAKTGiIiInIJTGqIiIjIJTCpISIiIpfg2NttdiDbGoM6nU7kSIiIiKi1bNft1qwV7DZJTU1NDQAgMjJS5EiIiIiorWpqaqBWq2/axm22SbBarSgtLYWfnx8kEkmHHlun0yEyMhIlJSXcguEneG5ujufnxnhubo7n5+Z4fm7M2c6NIAioqalBREREs70lW+I2PTVSqRQ9evTo1NdQqVRO8QERA8/NzfH83BjPzc3x/Nwcz8+NOdO5uVUPjQ0LhYmIiMglMKkhIiIil8CkpgMoFAosXboUCoVC7FAcDs/NzfH83BjPzc3x/Nwcz8+NufK5cZtCYSIiInJt7KkhIiIil8CkhoiIiFwCkxoiIiJyCUxqiIiIyCUwqblNq1evRlRUFJRKJVJSUpCbmyt2SKLYtWsX7rnnHkREREAikWDTpk3NHhcEAUuWLEF4eDi8vLyQlpaG06dPixNsF1u+fDmGDx8OPz8/hISEYOrUqTh58mSzNgaDAU888QQCAwPh6+uLadOmoaysTKSIu9b777+PIUOG2BcCS01NxTfffGN/3J3PzU+98cYbkEgkePrpp+33ufP5WbZsGSQSSbNbbGys/XF3PjcAcOnSJTz88MMIDAyEl5cX4uLikJeXZ3/cFb+XmdTchg0bNiAjIwNLly5FQUEB4uPjkZ6ejvLycrFD63J6vR7x8fFYvXp1i4+/9dZbePfdd7FmzRrs27cPPj4+SE9Ph8Fg6OJIu97OnTvxxBNPYO/evcjKyoLJZMKkSZOg1+vtbZ555hl89dVX2LhxI3bu3InS0lL8/Oc/FzHqrtOjRw+88cYbyM/PR15eHiZMmID77rsPR48eBeDe5+bH9u/fj7/+9a8YMmRIs/vd/fwMGjQIly9ftt92795tf8ydz83Vq1cxatQoeHp64ptvvsGxY8ewYsUKdOvWzd7GJb+XBWq35ORk4YknnrD/2WKxCBEREcLy5ctFjEp8AIQvvvjC/mer1SqEhYUJb7/9tv2+6upqQaFQCJ9++qkIEYqrvLxcACDs3LlTEITGc+Hp6Sls3LjR3ub48eMCACEnJ0esMEXVrVs34e9//zvPTZOamhohJiZGyMrKEsaOHSssXLhQEAR+dpYuXSrEx8e3+Ji7n5vnn39eGD169A0fd9XvZfbUtFNDQwPy8/ORlpZmv08qlSItLQ05OTkiRuZ4zp8/D41G0+xcqdVqpKSkuOW50mq1AICAgAAAQH5+PkwmU7PzExsbi549e7rd+bFYLFi/fj30ej1SU1N5bpo88cQTmDJlSrPzAPCzAwCnT59GREQEevfujVmzZqG4uBgAz83mzZuRlJSEBx98ECEhIRg6dCj+9re/2R931e9lJjXtVFlZCYvFgtDQ0Gb3h4aGQqPRiBSVY7KdD56rxt3in376aYwaNQqDBw8G0Hh+5HI5/P39m7V1p/Nz5MgR+Pr6QqFQ4LHHHsMXX3yBgQMH8twAWL9+PQoKCrB8+fLrHnP385OSkoKPPvoImZmZeP/993H+/HnccccdqKmpcftzc+7cObz//vuIiYnB//73Pzz++ON46qmn8PHHHwNw3e9lt9mlm8gRPPHEEygsLGw27k9A//79cfDgQWi1WvznP//BnDlzsHPnTrHDEl1JSQkWLlyIrKwsKJVKscNxOHfddZf9/4cMGYKUlBT06tULn332Gby8vESMTHxWqxVJSUl4/fXXAQBDhw5FYWEh1qxZgzlz5ogcXedhT007BQUFQSaTXVdJX1ZWhrCwMJGicky28+Hu52rBggXYsmULduzYgR49etjvDwsLQ0NDA6qrq5u1d6fzI5fL0bdvXyQmJmL58uWIj4/Hn//8Z7c/N/n5+SgvL8ewYcPg4eEBDw8P7Ny5E++++y48PDwQGhrq1ufnp/z9/dGvXz+cOXPG7T874eHhGDhwYLP7BgwYYB+ec9XvZSY17SSXy5GYmIjs7Gz7fVarFdnZ2UhNTRUxMscTHR2NsLCwZudKp9Nh3759bnGuBEHAggUL8MUXX2D79u2Ijo5u9nhiYiI8PT2bnZ+TJ0+iuLjYLc5PS6xWK4xGo9ufm4kTJ+LIkSM4ePCg/ZaUlIRZs2bZ/9+dz89P1dbW4uzZswgPD3f7z86oUaOuWzri1KlT6NWrFwAX/l4Wu1LZma1fv15QKBTCRx99JBw7dkyYP3++4O/vL2g0GrFD63I1NTXCgQMHhAMHDggAhHfeeUc4cOCAcOHCBUEQBOGNN94Q/P39hS+//FI4fPiwcN999wnR0dFCfX29yJF3vscff1xQq9XCd999J1y+fNl+q6urs7d57LHHhJ49ewrbt28X8vLyhNTUVCE1NVXEqLvOokWLhJ07dwrnz58XDh8+LCxatEiQSCTCt99+KwiCe5+blvx49pMguPf5efbZZ4XvvvtOOH/+vPDDDz8IaWlpQlBQkFBeXi4Ignufm9zcXMHDw0N47bXXhNOnTwuffPKJ4O3tLfz73/+2t3HF72UmNbfpvffeE3r27CnI5XIhOTlZ2Lt3r9ghiWLHjh0CgOtuc+bMEQShcfrgSy+9JISGhgoKhUKYOHGicPLkSXGD7iItnRcAwocffmhvU19fL/z2t78VunXrJnh7ewv333+/cPnyZfGC7kK//vWvhV69eglyuVwIDg4WJk6caE9oBMG9z01LfprUuPP5mT59uhAeHi7I5XKhe/fuwvTp04UzZ87YH3fncyMIgvDVV18JgwcPFhQKhRAbGyusXbu22eOu+L0sEQRBEKePiIiIiKjjsKaGiIiIXAKTGiIiInIJTGqIiIjIJTCpISIiIpfApIaIiIhcApMaIiIicglMaoiIiMglMKkhIiIil8CkhoiIiFwCkxoiIiJyCUxqiIiIyCUwqSEiIiKX8P/GRD+I1MXD4AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_wires = 6\n",
+    "x_max = 2**n_wires\n",
+    "x_input = np.arange(x_max)\n",
+    "mus = [(2 / 8) * x_max, (5 / 8) * x_max]\n",
+    "sigmas = [x_max / 10] * 2\n",
+    "data = gaussian_mixture_pdf(x_input, mus, sigmas)\n",
+    "\n",
+    "# This is the target distribution that the QCBM will learn\n",
+    "target_probs = torch.tensor(data, dtype=torch.float32)\n",
+    "\n",
+    "plt.plot(x_input, target_probs, linestyle=\"-.\", label=r\"$\\pi(x)$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b1bb110-e81c-455e-86a6-6b722f3a4433",
+   "metadata": {},
+   "source": [
+    "Using `torchquantum`, we can create a parameterized quantum circuit which will be used to generate a probability distribution. The gradient-based learning algorithm is used to update the circuit parameters $\\theta$ iteratively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8347fa01-d519-40e3-a7ea-67fabca8ed56",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gopald/Documents/tq-env/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3B7fk/mPAEyoqKip/+/pnGYDqRUaffhofZ/OhuuZC9vJyS3x+2O0VCq7DgH9eTpbPtM8b50J35kKS9XIBd7NaPhiIuZBEPgT4g6LCghqXeToXkqTMYxk+//mRmJtd53VTD+yTw2F3XzAAfAq5EADAH/ntgH9cXJyysrK0bt06DR06tMqytLQ03XvvvZKk3r17y2azeTwed93hHx5a8zJnpqN35a626kSEBalNmza1V2SikPJsSZLD4XDpb1tRVqzocIcifaB9YSG1TNXgBr/8/oVWbFykV6ZvqtxPrZt11I1jHtecRTdo/oxNigiLcnp7rVq3dvsdXVbjzn3gjfav3PyB9qRt1MGMHVqxcdEpy1+/5ze1aNLW6e25+xjwhODg4Mrfvv5ZBqB6waGn/3ysLR+qby5UUVZgic+P/GN7FdOyi8vrOYqP+Ez7PH0udHcuJFkvF3A3q+WDgZgLSeRDgD8IsZXXuMxdudDpttUwMtT3Pz+K63YzSP6xFLVu3crNwQDwJeRCAABfFRcXV+d1/XZK/2nTpmnevHlKSEjQsmXL1LlzZ0nSmjVrNGnSJO3Zs0dlZWW6/fbb9fzzz1e7DXdO6Z+fn6+oKNc6DKuz7ZA0/9u6r//wpcYV3NmF0sMfur7+zclSt9Z1r98b7A5p1idShovP2B2cKF07tPZy3lBRKn071+woXJM8TQoOc9/2An0fBHr7PeWhJVJOkRQTIT0ywexoANTVPz52/Tx/XH1zoR5tpJtG1q1ub/rud+nDmp98UK2QIOOzMaqBZ2JyVaCfCwO9/ZL19oEVciGJfAjwB3U5zx9X31xIkh64WGrasG7rektRqfTQh1JpzddGVOvCvtKoHh4JCYCPIBcCAPij+t9y7qNmzpyppk2b6sCBA+rRo4d69eqlTp06afDgwUpMTNQ555wjSerTp4/JkbomITaw63dGkE0a3sn19UZ0dn8sAADA/czMR+ItkAtJ0qBEKczFGf37tfOdwX4AAFCztk3NqzsyTIqt//0sHhcRJg1s79o6wUHSkI4eCQcAAADwKL8d8I+Pj9fKlSs1btw4hYeHKyUlRbGxsZo/f74+++wz7dixQ5L1BvwbhkvNGplTd/NGRv1WcGZnqVNL58uf18PcL8wAAMB57ZsFZt2uiAyTrj7D+fJNG0oX9fdcPAAAwH3aNJFCXbywz13aN5O88GRMtxjXV2oR7Xz5q4ZIjSzS7wUAAACczG8H/CWpW7du+vTTT5WXl6e8vDz9/PPPmjJligoKCpSSkqKgoCD17NnT7DBddoZJVxtb6SrnkGDpz2c79/iB83pIY6113QcAAAFtQHtj+nlvaxIpdan7o7S8rn976bphxt1qpxMXI00dRQc3AABWERZizMxjhjOSzKm3LqIaSLefa1wgcTpBNunqIcajHgEAAAArCjE7ADNs3bpVDodDnTt3VmRk5CnLFy9eLEn67bffqvy/ffv2GjhwoPcCrcEZHaUvNkkVdu/VGRxk3oUGddUg1HjG7u+HpNU7pd8OSo6Tlg9NMqb+t8rUvAAAwNAw3OjkXrPXu/UO6yQFWexy2YEdpMTm0o+7jJ/8khPL2jczHmnUp615dwkCAIC6GdFZ+mWPd+tsHCn1aOPdOusrJlK6+wJp0wFp1Q5pz9ETy2ySkrtLw5LMm00TAAAAcIeAHPDfvHmzpJqn87/iiiuq/f/111+vBQsWeDQ2ZzQMN646/nGX9+ockmid6fxPFmSTurcxfgpLpVkfGx3d0eHGVG0AAMCaRnaT1qZIDketRd0iPNRad7SdLLahMaXt6N7Sw0ukvBLjbv67LjA7MgAAUFdtm0pJLaVdh71X59lda585yBeFBBszH/VvL+UXS49/avQNNQqXLupndnQAAABA/THgXw2Ht3qO62F8P+OO9Zwiz9cVEyFd6AdfgCLDTnwxtcrz5gBnlJYV65//vlr7Dv+mBqERatywhaZNeEltmlUdmVqzfale++y+yv9nFxxRbKM4vXTXOu1N26zHF06qXFZQnK3C4lwt+b9Mr7UDAFzRpol0Tjfpm9+8U9+lA6w/5X1w0IkZCoL8PBdy9txYVJKvR96+TDtTf1WFvVwfPZptTsD15Gx7Jem9FU/q67Vvye6wK6F5F91z1ZtqGNFYGTmHNOe9G3Q4K0WhwQ3Uplkn3XnZy2rcsLkJLXJO6tGdenLR9copyFBUeIzuvWqB2sf1qFImPTNFTy6arF2H1iuuSQfNn76hctn6Xcv1+ud/UVFJvmw2m4Z0Hacbxz6uoD9M5TH73cn6+te39OH/ZalhRGMvtAwAnHPVEGn2Z1JZhefrattUOquL5+vxtIbh9A0BAADA/zDgb1GRYcYXu1dWuLZeblHV3864aohRH6zD2U7f05VzpePYF73w0TT9+NvHOpy1Ty/dtV5JbfpWW27N71/qzaV/V3l5qRqERequy+arY+sTnw0/b/tcC5b+XXa7XXZ7ua4Yea/OH3i9l1rhvLFDpmhw1zGy2Wz6aPXzevr9P+upW1dUKTOoywUa1OXE7Zx/f+NC9emYLEnq0KpXlQ7weR9OlY3eDwA+bkxvaetBKT3H+XXqkgt1b80zXa3ImXNjcHCorkq+T40iYnXPyyNNidNdnGnvrzu+1tI1b2reHT8rMryR/r3sH3rji/s1bcILCg4K1nWjHlDPDiMkSa98eq9e+fRezbx6gfcb46TnPrhZY4dM0QWDJuv7TYv15KLJeuHONVXKRIZH64bR/1BBcY7e+OL+KssaRTTR/RPfVaumiSotK9bMV0bp61/f1gWDJleWWbl5iUKCQ73RHABwWfNG0oV9pQ9/dX6duuRCwUHStUOteXc/AAAAEAgCMlVfvny5HA6Hxo0bZ3Yo9dK9jTS2t2vrPP2l9PCHxm9njO1t1APrGTtkit6cuV3zp2/U0B4X6+n3/+xyOWe34YvO7H25nrltlVo2aVdjmbzCLD22cKJmXvWWXpmxSVPGPanH/zOxcrnD4dATC6/TvVcu0PzpG/To//tUz35wswqL87zRBKeFhYZrSLexlQP03dqeocNZKaddJyPnkNbv/EajBkw6ZVlpWbGWr/+3Rg+60RPhAoDbhARL/+8s1x475GouFBdjdHBzDZS1OHtuDAtpoH5J51j+rm1n27vn0Eb17DBCkeHGg4oHdx2rb9b9S5LUpFHLysF+Seradkit+YSZsvKPaEfqWo3qf50k6cxel+lo9gEdzKj63LPoyFj17DBC4WFRp2wjqU0/tWpqXM0TFhqujq37VmlzVt5hLVw+S7eMf9pzDQGAejqzizSog/PlXc2FbJImDjVyIgAAAAC+KSAH/P3JeT2lC3p5ZtsX9DK2D+txupP7NOXqMojsS3onnqXmjeNPW+bQsd2KjmxaOfVrr8QzdSR7v3amrjtRyGZTfnG2JKmwOFfRkU0VGtLAU2G7xYerntPQHheftsxXaxdocNexatKwxSnLVm1ZolaxiTXOigAAvqRFtHTbOZ6Zbj8uRrr1HNcuKIBvcubc6E9qam+n+AFat3OZMnPT5XA49M36f6uwJE+5hVUf4VNhr9B/Vz+vYT68z45mH1BsdCsFBxuT1tlsNrVo0lZHsvfXaXuZuelauWmxhnS7sPK1pxffpJvGza68QAIAfFGQTbr6DGlAe/dv22aTrhkq9ffAtgEAAAC4T0BO6e9PbDZjOtsmUdKHa6WS8vpvs0GIdOlA6YyO9d8WfIOzndynK+ePHeXxzTopt/CYtqb8oB7th+mHrR+rsCRP6Vkp6hTfXzabTX+fuEiPvDVB4WFRyi/K0kN/WqLQEN99xsV/vpmlQxm7NPvmb2os43A4tHTNG7rt4rnVLv/il9c1ejB39wOwjtZNpDvPl975QUrJcM82eycYjzWK8u1rvOAEZ86N/uR07e2blKwrzr5Hf3/zQgXbgjW856WSpOCgE18LHQ6H5i25TY0imujSEXd6LW4zFRTn6oE3x+vKkTPVJWGgJOnzn19Ti8Zt1S/pHJOjA4DaBQdJE4cZU/x/tUWyO+q/zegI6ZozpG6t678tAAAAAJ7FgL+fOKOj1Lml9O7P0o70um+nc5x09RAptqH7YoP7TZs3VAczdla77KW716tF44TK/zvbyX26cr7WUe5K+08nKiJGD05arNe/+KuKS/LVrd1QtWvZvbLTu6KiXP/+5h966Pol6p14lrYfWKMH37xIr8zYrJioZm5rj7u8v2KOVm1ZotlTlik8LLLGcpv2fKfS8mIN7HLBKcvSMvfq930/6aE/feDJUAHA7Zo1kqadJ323Xfp8o1RWUbftRIZJlw2S+rdjGn9/4Oy50V84096Lht2mi4bdJkn6bd9Pah4Tr6jw6MrlL/x3mo7kHNAj13+koCDfnRCueeMEZeamqaKiXMHBIXI4HDqStV8tGrd1aTuFxXn622ujNazHxbr87OmVr2/c/a027/leP2/7tPK1KU/31v9N/q+S2vRzWzsAwF2CbNLo3lLPeOk/P0mHsuq+rUEdpEsHSJFc+AgAAABYAgP+fiS2oTHt7K7D0qqd0uYDzl3VHWSTeiVIwztJnVrSuW0Fc+/40alyznZyn66cL3aUO9t+Z/RNSlbfpGRJUml5ia76vzi1a9ldkrTr0AYdyz2k3olnSZK6JAxSs5h47Tq4XgM6n+e2GNxh8XdP69sNC/XElGW1Pof4i19e1/kDJys4KPiUZUt/eUPDe15q+WcZAwhMQUFScjdjsP7H3dKPO6WcIufWbRFt5EKDEo1Bf1ifK+dGf+Bse4/lpqlpdCsVlxbqraUP6sqRMyuXvfDRNB3K2KWHJ3/k0zMaSVKThi2U1Ka/lq17RxcMmqyVmz9Qs8bxatMsyeltFJXk66+vjdbALqM1cdTfqyz767X/rvL/8+616ZXpmwLiWAJgbfGx0ozR0taD0qodzt8UEhpsPBZgeGcpIdajIQIAAABwMwb8/YzNJnWKM35yCo3B//2ZUmqmlFsklVdIIcHG1GzxsVLbWCm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",
+      "text/plain": [
+       "<Figure size 2628.61x1120.39 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ansatz\n",
+    "layers = tq.RXYZCXLayer0(\n",
+    "    {\n",
+    "        \"n_blocks\": 6,\n",
+    "        \"n_wires\": n_wires,\n",
+    "        \"n_layers_per_block\": 1,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# We use `tq2qiskit` to visualize the ansatz.\n",
+    "qdev = tq.QuantumDevice(n_wires=n_wires, bsz=1, device=\"cpu\")\n",
+    "tq.plugin.qiskit.tq2qiskit(qdev, layers).draw(output=\"mpl\", fold=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a14839c3-d9ff-44dc-adf6-efeebae18bfe",
+   "metadata": {},
+   "source": [
+    "We can now simulate the circuit to model the gaussian mixture distribution. The algorithm minimizes the kerneled maximum mean discrepancy (MMD) loss to train the QCBM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6490b2e9-d18d-42e0-9310-a3f644197c8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcbm = QCBM(n_wires, layers)\n",
+    "\n",
+    "# To train QCBMs, we use MMDLoss with radial basis function kernel.\n",
+    "bandwidth = torch.tensor([0.25, 60])\n",
+    "space = torch.arange(2**n_wires)\n",
+    "mmd = MMDLoss(bandwidth, space)\n",
+    "\n",
+    "# Optimization\n",
+    "optimizer = torch.optim.RMSprop(qcbm.parameters(), lr=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4335ef3e-8dea-47be-a310-8146abc214fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration: 0, Loss: 0.007511706091463566\n",
+      "Iteration: 10, Loss: 0.0008048344170674682\n",
+      "Iteration: 20, Loss: 0.0004957925993949175\n",
+      "Iteration: 30, Loss: 0.0003518108860589564\n",
+      "Iteration: 40, Loss: 0.0002739735064096749\n",
+      "Iteration: 50, Loss: 0.0002034252102021128\n",
+      "Iteration: 60, Loss: 0.00014893575280439109\n",
+      "Iteration: 70, Loss: 0.0001268944761250168\n",
+      "Iteration: 80, Loss: 0.00010558744543232024\n",
+      "Iteration: 90, Loss: 8.735547453397885e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(100):\n",
+    "    optimizer.zero_grad(set_to_none=True)\n",
+    "    pred_probs = qcbm()\n",
+    "    loss = mmd(pred_probs, target_probs)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    if i % 10 == 0:\n",
+    "        print(f\"Iteration: {i}, Loss: {loss.item()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "246f5d90-47af-4385-8d41-5ff6fadcb9ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the results\n",
+    "with torch.no_grad():\n",
+    "    pred_probs = qcbm()\n",
+    "\n",
+    "plt.plot(x_input, target_probs, linestyle=\"-.\", color=\"black\", label=r\"$\\pi(x)$\")\n",
+    "plt.bar(x_input, pred_probs, color=\"green\", alpha=0.5, label=\"samples\")\n",
+    "plt.xlabel(\"Samples\")\n",
+    "plt.ylabel(\"Prob. Distribution\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7f5ff14-793c-4dc3-aad2-eed38aa3e5a5",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "1. Liu, Jin-Guo, and Lei Wang. \"Differentiable learning of quantum circuit born machines.\" Physical Review A 98.6 (2018): 062324."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tq-env)",
+   "language": "python",
+   "name": "tq-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/QCBM/qcbm_gaussian_mixture.py b/examples/QCBM/qcbm_gaussian_mixture.py
new file mode 100644
index 00000000..fdc2acbd
--- /dev/null
+++ b/examples/QCBM/qcbm_gaussian_mixture.py
@@ -0,0 +1,129 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torchquantum.algorithm import QCBM, MMDLoss
+import torchquantum as tq
+import argparse
+import os
+from pprint import pprint
+
+
+# Reproducibility
+def set_seed(seed: int = 42) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    # When running on the CuDNN backend, two further options must be set
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    # Set a fixed value for the hash seed
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    print(f"Random seed set as {seed}")
+
+
+def _setup_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--n_wires", type=int, default=6, help="Number of wires used in the circuit"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=10, help="Number of training epochs"
+    )
+    parser.add_argument(
+        "--n_blocks", type=int, default=6, help="Number of blocks in ansatz"
+    )
+    parser.add_argument(
+        "--n_layers_per_block",
+        type=int,
+        default=1,
+        help="Number of layers per block in ansatz",
+    )
+    parser.add_argument(
+        "--plot",
+        action="store_true",
+        help="Visualize the predicted probability distribution",
+    )
+    parser.add_argument(
+        "--optimizer", type=str, default="Adam", help="optimizer class from torch.optim"
+    )
+    parser.add_argument("--lr", type=float, default=1e-2)
+    return parser
+
+
+# Function to create a gaussian mixture
+def gaussian_mixture_pdf(x, mus, sigmas):
+    mus, sigmas = np.array(mus), np.array(sigmas)
+    vars = sigmas**2
+    values = [
+        (1 / np.sqrt(2 * np.pi * v)) * np.exp(-((x - m) ** 2) / (2 * v))
+        for m, v in zip(mus, vars)
+    ]
+    values = np.sum([val / sum(val) for val in values], axis=0)
+    return values / np.sum(values)
+
+
+def main():
+    set_seed()
+    parser = _setup_parser()
+    args = parser.parse_args()
+
+    print("Configuration:")
+    pprint(vars(args))
+
+    # Create a gaussian mixture
+    n_wires = args.n_wires
+    assert n_wires >= 1, "Number of wires must be at least 1"
+
+    x_max = 2**n_wires
+    x_input = np.arange(x_max)
+    mus = [(2 / 8) * x_max, (5 / 8) * x_max]
+    sigmas = [x_max / 10] * 2
+    data = gaussian_mixture_pdf(x_input, mus, sigmas)
+
+    # This is the target distribution that the QCBM will learn
+    target_probs = torch.tensor(data, dtype=torch.float32)
+
+    # Ansatz
+    layers = tq.RXYZCXLayer0(
+        {
+            "n_blocks": args.n_blocks,
+            "n_wires": n_wires,
+            "n_layers_per_block": args.n_layers_per_block,
+        }
+    )
+
+    qcbm = QCBM(n_wires, layers)
+
+    # To train QCBMs, we use MMDLoss with radial basis function kernel.
+    bandwidth = torch.tensor([0.25, 60])
+    space = torch.arange(2**n_wires)
+    mmd = MMDLoss(bandwidth, space)
+
+    # Optimization
+    optimizer_class = getattr(torch.optim, args.optimizer)
+    optimizer = optimizer_class(qcbm.parameters(), lr=args.lr)
+
+    for i in range(args.epochs):
+        optimizer.zero_grad(set_to_none=True)
+        pred_probs = qcbm()
+        loss = mmd(pred_probs, target_probs)
+        loss.backward()
+        optimizer.step()
+        print(i, loss.item())
+
+    # Visualize the results
+    if args.plot:
+        with torch.no_grad():
+            pred_probs = qcbm()
+
+        plt.plot(x_input, target_probs, linestyle="-.", label=r"$\pi(x)$")
+        plt.bar(x_input, pred_probs, color="green", alpha=0.5, label="samples")
+        plt.xlabel("Samples")
+        plt.ylabel("Prob. Distribution")
+
+        plt.legend()
+        plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/torchquantum/algorithm/__init__.py b/torchquantum/algorithm/__init__.py
index 7dfb672a..c7413a2e 100644
--- a/torchquantum/algorithm/__init__.py
+++ b/torchquantum/algorithm/__init__.py
@@ -22,7 +22,8 @@
 SOFTWARE.
 """
 
-from .vqe import *
-from .hamiltonian import *
-from .qft import *
-from .grover import *
+from .vqe import VQE
+from .hamiltonian import Hamiltonian
+from .qft import QFT
+from .grover import Grover
+from .qcbm import QCBM, MMDLoss
diff --git a/torchquantum/algorithm/qcbm.py b/torchquantum/algorithm/qcbm.py
new file mode 100644
index 00000000..35a6fb75
--- /dev/null
+++ b/torchquantum/algorithm/qcbm.py
@@ -0,0 +1,96 @@
+import torch
+import torch.nn as nn
+
+import torchquantum as tq
+
+__all__ = ["QCBM", "MMDLoss"]
+
+
+class MMDLoss(nn.Module):
+    """Squared maximum mean discrepancy with radial basis function kerne"""
+
+    def __init__(self, scales, space):
+        """
+        Initialize MMDLoss object. Calculates and stores the kernel matrix.
+
+        Args:
+            scales: Bandwidth parameters.
+            space: Basis input space.
+        """
+        super().__init__()
+
+        gammas = 1 / (2 * (scales**2))
+
+        # squared Euclidean distance
+        sq_dists = torch.abs(space[:, None] - space[None, :]) ** 2
+
+        # Kernel matrix
+        self.K = sum(torch.exp(-gamma * sq_dists) for gamma in gammas) / len(scales)
+        self.scales = scales
+
+    def k_expval(self, px, py):
+        """
+        Kernel expectation value
+
+        Args:
+            px: First probability distribution
+            py: Second probability distribution
+
+        Returns:
+            Expectation value of the RBF Kernel.
+        """
+
+        return px @ self.K @ py
+
+    def forward(self, px, py):
+        """
+        Squared MMD loss.
+
+        Args:
+            px: First probability distribution
+            py: Second probability distribution
+
+        Returns:
+            Squared MMD loss.
+        """
+        pxy = px - py
+        return self.k_expval(pxy, pxy)
+
+
+class QCBM(nn.Module):
+    """
+    Quantum Circuit Born Machine (QCBM)
+
+    Attributes:
+        ansatz: An Ansatz object
+        n_wires: Number of wires in the ansatz used.
+
+    Methods:
+        __init__: Initialize the QCBM object.
+        forward: Returns the probability distribution (output from measurement).
+    """
+
+    def __init__(self, n_wires, ansatz):
+        """
+        Initialize QCBM object
+
+        Args:
+            ansatz (Ansatz): An Ansatz object
+            n_wires (int): Number of wires in the ansatz used.
+        """
+        super().__init__()
+
+        self.ansatz = ansatz
+        self.n_wires = n_wires
+
+    def forward(self):
+        """
+        Execute and obtain the probability distribution
+
+        Returns:
+            Probabilities (torch.Tensor)
+        """
+        qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=1, device="cpu")
+        self.ansatz(qdev)
+        probs = torch.abs(qdev.states.flatten()) ** 2
+        return probs


Test patch:
diff --git a/test/algorithm/test_qcbm.py b/test/algorithm/test_qcbm.py
new file mode 100644
index 00000000..333a25bb
--- /dev/null
+++ b/test/algorithm/test_qcbm.py
@@ -0,0 +1,31 @@
+from torchquantum.algorithm.qcbm import QCBM, MMDLoss
+import torchquantum as tq
+import torch
+
+
+def test_qcbm_forward():
+    n_wires = 3
+    n_layers = 3
+    ops = []
+    for l in range(n_layers):
+        for q in range(n_wires):
+            ops.append({"name": "rx", "wires": q, "params": 0.0, "trainable": True})
+        for q in range(n_wires - 1):
+            ops.append({"name": "cnot", "wires": [q, q + 1]})
+
+    data = torch.ones(2**n_wires)
+    qmodule = tq.QuantumModule.from_op_history(ops)
+    qcbm = QCBM(n_wires, qmodule)
+    probs = qcbm()
+    expected = torch.tensor([1.0, 0, 0, 0, 0, 0, 0, 0])
+    assert torch.allclose(probs, expected)
+
+
+def test_mmd_loss():
+    n_wires = 2
+    bandwidth = torch.tensor([0.1, 1.0])
+    space = torch.arange(2**n_wires)
+
+    mmd = MMDLoss(bandwidth, space)
+    loss = mmd(torch.zeros(4), torch.zeros(4))
+    assert torch.isclose(loss, torch.tensor(0.0), rtol=1e-5)