{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 噪声模拟器\n",
"\n",
"[](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.2/mindquantum/zh_cn/middle_level/mindspore_noise_simulator.ipynb) \n",
"[](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.2/mindquantum/zh_cn/middle_level/mindspore_noise_simulator.py) \n",
"[](https://gitee.com/mindspore/docs/blob/r2.2/docs/mindquantum/docs/source_zh_cn/middle_level/noise_simulator.ipynb)\n",
"[](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9taW5kc3BvcmUtd2Vic2l0ZS5vYnMuY24tbm9ydGgtNC5teWh1YXdlaWNsb3VkLmNvbS9ub3RlYm9vay9yMi4yL21pbmRxdWFudHVtL3poX2NuL21pZGRsZV9sZXZlbC9taW5kc3BvcmVfbm9pc2Vfc2ltdWxhdG9yLmlweW5i&imageid=73522c7f-6d98-43d8-b306-8ff374c7d3a7)\n",
"\n",
"MindQuantum 中包含各种噪声信道，利用噪声信道我们可以对真实的量子芯片进行模拟。在 MindQuantum 中，我们定义了各种 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html)，可以有选择性的在量子线路的不同位置添加噪声信道，依次完成含噪声的量子模拟。下面介绍如何利用 MindQuantum 完成此任务。\n",
"\n",
"## [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html)\n",
"\n",
"[ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html) 是一个能够在量子线路特定位置添加特定信道的处理器，例如在测量门后添加比特翻转信道。`ChannelAdder` 类主要由三个函数构成，`_accepter()`、`_excluder()` 和 `_handler(BasicGate)`，其功能对应如下：\n",
"\n",
"- `_accepter()`：返回一个由函数构成的列表，称为接受规则集，其中每个接受规则函数的输入都是一个量子门，当函数返回值为 `True` 时表示我们可以在该量子门后添加信道。\n",
"\n",
"- `_excluder()`：返回一个由函数构成的列表，称为拒绝规则集，其中每个拒绝规则函数的输入都是一个量子门，当函数返回值为 `True` 时表示我们拒绝在该量子门后添加信道。\n",
"\n",
"- `_handler(BasicGate)`：输入一个量子门，返回一段量子线路，表示在输入量子门后添加一段自定义的信道。\n",
"\n",
"我们重定义类 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html) 的 `__call__` 函数，直接调用 `ChannelAdder` 即可生成处理后的量子线路。\n",
"下面介绍几种 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html)。\n",
"\n",
"### [BitFlipAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.BitFlipAdder.html)\n",
"\n",
"[BitFlipAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.BitFlipAdder.html) 的接口定义为：\n",
"\n",
"```python\n",
"BitFlipAdder(flip_rate: float, with_ctrl=True, focus_on: int = None, add_after: bool = True)\n",
"```\n",
"\n",
"该 `Adder` 会在量子门后添加一个比特翻转信道，接口的参数含义为：\n",
"\n",
"- **flip_rate** (float)：比特翻转信道的翻转概率。\n",
"- **with_ctrl** (bool)：是否在控制位上添加比特。默认值： ``True``。\n",
"- **focus_on** (bool)：只将该噪声信道作用在 ``focus_on`` 比特上。如果为 ``None``，则作用在量子门的所有比特上。默认值： ``None``。\n",
"- **add_after** (bool)：是否在量子门后面添加信道。如果为 ``False``，信道将会加在量子门前面。默认值： ``True``。\n",
"\n",
"例如，我们可以通过如下接口，在给定量子线路的每个量子门后都添加一个翻转概率为 ``0.3`` 的比特翻转信道："
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe819f3caf0>"
]
},
"execution_count": 1,
"metadata": {},
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}
],
"source": [
"from mindquantum.core.circuit.channel_adder import BitFlipAdder\n",
"from mindquantum.core import gates as G\n",
"from mindquantum.core.circuit import Circuit\n",
"\n",
"circ = Circuit()+G.H(0)+G.RX('a').on(1)+G.Z(1, 0)\n",
"circ.svg()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe8280fe610>"
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bit_flip_adder = BitFlipAdder(0.3, with_ctrl=False)\n",
"new_circ = bit_flip_adder(circ)\n",
"new_circ.svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [MeasureAccepter](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MeasureAccepter.html)\n",
"\n",
"```python\n",
"MeasureAccepter()\n",
"```\n",
"\n",
"该 `Adder` 会选择对应的测量门，它目前只是一个 `Accepter`，不会改变量子线路中的任何门，需要利用 [MixerAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MixerAdder.html)，跟其他的 `Adder` 搭配使用。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [MixerAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MixerAdder.html)\n",
"\n",
"```python\n",
"MixerAdder(adders: typing.List[ChannelAdderBase])\n",
"```\n",
"\n",
"[MixerAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MixerAdder.html) 可以将多个 `Adder` 混合起来，保证量子门在每一个 `Adder` 中的接受函数集和拒绝函数集同时满足时，顺序添加 `_handler` 产生的量子线路。\n",
"\n",
"举例来说，我们可以将上文提到的 [BitFlipAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.BitFlipAdder.html) 和 [MeasureAccepter](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MeasureAccepter.html) 混合起来，达到只在测量门前添加比特翻转信道的功能："
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MixerAdder<\n",
" BitFlipAdder<flip_rate=0.01, with_ctrl=True>\n",
" MeasureAccepter<>\n",
">\n"
]
},
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{
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"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe818473070>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import display_svg\n",
"from mindquantum.core.circuit.channel_adder import MixerAdder, MeasureAccepter\n",
"\n",
"mixer = MixerAdder([\n",
" BitFlipAdder(flip_rate=0.01),\n",
" MeasureAccepter(),\n",
"], add_after=False)\n",
"print(mixer)\n",
"\n",
"circ = Circuit() + G.H(0) + G.RX('a').on(1) + G.Z(1, 0) + G.Measure().on(0)\n",
"display_svg(circ.svg())\n",
"new_circ = mixer(circ)\n",
"new_circ.svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [SequentialAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.SequentialAdder.html)\n",
"\n",
"```python\n",
"SequentialAdder(adders: typing.List[ChannelAdderBase])\n",
"```\n",
"\n",
"[SequentialAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.SequentialAdder.html) 是由多个 `Adder` 顺序构成类，量子线路会经过 [SequentialAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.SequentialAdder.html) 中的 `Adder` 依次处理，生成最终的量子线路。例如，我们想构建一个先在测量门前添加一个 $p=0.01$ 的比特翻转信道，然后在 `q1` 比特上的非测量门和非噪声信道后添加 $p=0.05$ 的去极化信道。\n",
"\n",
"### 自定义 `Adder`\n",
"\n",
"我们首先自定义在某个比特添加比特翻转信道的 `Adder`。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SequentialAdder<\n",
" MixerAdder<\n",
" MeasureAccepter<>\n",
" BitFlipAdder<flip_rate=0.01, with_ctrl=True>\n",
" >\n",
" CustomDepolarizingAdder<q=1, flip_rate=0.05>\n",
">\n"
]
}
],
"source": [
"from mindquantum.core.circuit.channel_adder import ChannelAdderBase, SequentialAdder\n",
"\n",
"class CustomDepolarizingAdder(ChannelAdderBase):\n",
" def __init__(self, q, p):\n",
" self.q = q\n",
" self.p = p\n",
" super().__init__()\n",
"\n",
" def _accepter(self):\n",
" return [lambda x: self.q in x.obj_qubits or self.q in x.ctrl_qubits]\n",
"\n",
" def _excluder(self):\n",
" return [lambda x: isinstance(x, (G.Measure, G.NoiseGate))]\n",
"\n",
" def _handler(self, g):\n",
" return Circuit([G.DepolarizingChannel(self.p).on(self.q)])\n",
"\n",
" def __repr__(self):\n",
" return f\"CustomDepolarizingAdder<q={self.q}, flip_rate={self.p}>\"\n",
"\n",
"seq_adder = SequentialAdder([\n",
" MixerAdder([\n",
" MeasureAccepter(),\n",
" BitFlipAdder(flip_rate=0.01),\n",
" ], add_after=False),\n",
" CustomDepolarizingAdder(q=1, p=0.05),\n",
"])\n",
"print(seq_adder)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/svg+xml": [
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],
"text/plain": [
"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe82b17dac0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"circ = Circuit() + G.H(0) + G.RX('a').on(1) + G.Z(1, 0) + G.Measure().on(0)\n",
"display_svg(circ.svg())\n",
"new_circ = seq_adder(circ)\n",
"new_circ.svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上述自定义量子信道也可以通过 MindQuantum 中的预定义信道搭建而成。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SequentialAdder<\n",
" MixerAdder<\n",
" MeasureAccepter<>\n",
" BitFlipAdder<flip_rate=0.01, with_ctrl=True>\n",
" >\n",
" MixerAdder<\n",
" ReverseAdder<\n",
" MeasureAccepter<>\n",
" >\n",
" NoiseExcluder<>\n",
" NoiseChannelAdder<channel=DC(p=1/20), with_ctrl=True>\n",
" >\n",
">\n"
]
}
],
"source": [
"from mindquantum.core.circuit import ReverseAdder, NoiseExcluder, NoiseChannelAdder\n",
"seq_adder = SequentialAdder([\n",
" MixerAdder([\n",
" MeasureAccepter(),\n",
" BitFlipAdder(flip_rate=0.01),\n",
" ], add_after=False),\n",
" MixerAdder([\n",
" ReverseAdder(MeasureAccepter()),\n",
" NoiseExcluder(),\n",
" NoiseChannelAdder(G.DepolarizingChannel(0.05), focus_on=1),\n",
" ])\n",
"])\n",
"print(seq_adder)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
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],
"text/plain": [
"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe789aca6d0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"seq_adder(circ).svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 更复杂的例子\n",
"\n",
"下面我们来搭建一个更复杂的 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html) 例子，在该例子中，芯片的不同比特上的单比特门操作的噪声可以忽略不记，而双比特门在不同比特上具有不同的去极化信道，且线路的测量具有一个翻转概率为0.01的比特翻转错误。\n",
"\n",
"我们假设不同比特上的去极化信道为："
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dc0 = G.DepolarizingChannel(0.01)\n",
"dc1 = G.DepolarizingChannel(0.02)\n",
"dc2 = G.DepolarizingChannel(0.03)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"然后，我们定义出满足要求的 `Adder`："
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SequentialAdder<\n",
" MixerAdder<\n",
" NoiseExcluder<>\n",
" ReverseAdder<\n",
" MeasureAccepter<>\n",
" >\n",
" QubitNumberConstrain<n_qubits=2, with_ctrl=True>\n",
" NoiseChannelAdder<channel=DC(p=1/100), with_ctrl=True>\n",
" >\n",
" MixerAdder<\n",
" NoiseExcluder<>\n",
" ReverseAdder<\n",
" MeasureAccepter<>\n",
" >\n",
" QubitNumberConstrain<n_qubits=2, with_ctrl=True>\n",
" NoiseChannelAdder<channel=DC(p=1/50), with_ctrl=True>\n",
" >\n",
" MixerAdder<\n",
" NoiseExcluder<>\n",
" ReverseAdder<\n",
" MeasureAccepter<>\n",
" >\n",
" QubitNumberConstrain<n_qubits=2, with_ctrl=True>\n",
" NoiseChannelAdder<channel=DC(p=0.03), with_ctrl=True>\n",
" >\n",
" MixerAdder<\n",
" NoiseExcluder<>\n",
" MeasureAccepter<>\n",
" BitFlipAdder<flip_rate=0.01, with_ctrl=True>\n",
" >\n",
">"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from mindquantum.core.circuit import QubitNumberConstrain\n",
"noise_adder = SequentialAdder([\n",
" MixerAdder([\n",
" NoiseExcluder(),\n",
" ReverseAdder(MeasureAccepter()),\n",
" QubitNumberConstrain(2),\n",
" NoiseChannelAdder(dc0, focus_on=0),\n",
" ]),\n",
" MixerAdder([\n",
" NoiseExcluder(),\n",
" ReverseAdder(MeasureAccepter()),\n",
" QubitNumberConstrain(2),\n",
" NoiseChannelAdder(dc1, focus_on=1),\n",
" ]),\n",
" MixerAdder([\n",
" NoiseExcluder(),\n",
" ReverseAdder(MeasureAccepter()),\n",
" QubitNumberConstrain(2),\n",
" NoiseChannelAdder(dc2, focus_on=2),\n",
" ]),\n",
" MixerAdder([\n",
" NoiseExcluder(),\n",
" MeasureAccepter(),\n",
" BitFlipAdder(0.01)\n",
" ], add_after=False),\n",
"])\n",
"noise_adder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"假设我们想要处理的量子线路是哈密顿量 $H=a_{01} Z_0Z_1 + a_{12} Z_1Z_2 + b_0 X_0 + b_1 X_1 + b_2 X_2$ 含时演化的一阶Trotter近似线路："
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" b_0 [X0] +\n",
" b_1 [X1] +\n",
" b_2 [X2] +\n",
"a_01 [Z0 Z1] +\n",
"a_12 [Z1 Z2]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from mindquantum.core.operators import TimeEvolution, QubitOperator\n",
"\n",
"ham = sum([\n",
" QubitOperator('X0', 'b_0'),\n",
" QubitOperator('X1', 'b_1'),\n",
" QubitOperator('X2', 'b_2'),\n",
" QubitOperator('Z0 Z1', 'a_01'),\n",
" QubitOperator('Z1 Z2', 'a_12')\n",
"])\n",
"ham"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe789b34cd0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"circ = TimeEvolution(ham).circuit\n",
"circ.barrier()\n",
"circ.measure_all()\n",
"circ.svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"此线路经过上述定义的 `noise_adder` 处理后的量子线路为："
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe789aca610>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"noise_adder(circ).svg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html) 列表\n",
"\n",
"下面列举出 MindQuantum 中现有的一些 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html)，并给出具体含义：\n",
"\n",
"|[ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html)| 功能|\n",
"|--|--|\n",
"|[ChannelAdderBase](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html)|在量子门前面或者后面添加信道|\n",
"|[NoiseChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.NoiseChannelAdder.html)|添加一个单比特量子信道|\n",
"|[MeasureAccepter](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MeasureAccepter.html)|选取测量门|\n",
"|[ReverseAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ReverseAdder.html)|翻转给定信道添加器的接受和拒绝规则|\n",
"|[NoiseExcluder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.NoiseExcluder.html)|排除噪声门|\n",
"|[BitFlipAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.BitFlipAdder.html)|在量子门前面或者后面添加一个比特翻转信道|\n",
"|[MixerAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.MixerAdder.html)|在子添加器的接受集被满足、拒绝集被拒绝时依次执行所有的添加器|\n",
"|[SequentialAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.SequentialAdder.html)|依次执行每一个添加器|\n",
"|[QubitNumberConstrain](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.QubitNumberConstrain.html)|只将噪声信道作用在比特数为 ``n_qubits`` 的量子门上|\n",
"|[QubitIDConstrain](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.QubitIDConstrain.html)|只将噪声信道作用在给定比特序号的量子门上|\n",
"|[GateSelector](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.GateSelector.html)|选择想要的量子门添加信道|\n",
"|[DepolarizingChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.DepolarizingChannelAdder.html)|添加去极化信道|\n",
"\n",
"MindQuantum 中 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html) 的API接口文档请参考：[channel_adder](https://mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/mindquantum.core.circuit.html#channel-adder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 基于 [ChannelAdder](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.9/core/circuit/mindquantum.core.circuit.ChannelAdderBase.html) 的噪声模拟器\n",
"\n",
"我们可以将如上定义的各种 `Adder` 与现有的模拟器组合，构成一个含噪声模拟器。"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mindquantum.simulator import Simulator\n",
"from mindquantum.simulator.noise import NoiseBackend\n",
"\n",
"noiseless_sim = Simulator('mqvector', 2)\n",
"noiseless_circ = Circuit().h(0).rx(1.0, 1).z(1, 0).measure(1)\n",
"display_svg(noiseless_circ.svg())\n",
"res1 = noiseless_sim.sampling(noiseless_circ, shots=10000)\n",
"display(res1.svg())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
"<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe819f3cdc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"noise_sim = Simulator(NoiseBackend('mqvector', 2, seq_adder))\n",
"res2 = noise_sim.sampling(noiseless_circ, shots=10000)\n",
"display(res2.svg())\n",
"display(noise_sim.backend.transform_circ(noiseless_circ).svg())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<table border=\"1\">\n",
" <tr>\n",
" <th>Software</th>\n",
" <th>Version</th>\n",
" </tr>\n",
"<tr><td>mindquantum</td><td>0.9.11</td></tr>\n",
"<tr><td>scipy</td><td>1.10.1</td></tr>\n",
"<tr><td>numpy</td><td>1.24.4</td></tr>\n",
"<tr>\n",
" <th>System</th>\n",
" <th>Info</th>\n",
"</tr>\n",
"<tr><td>Python</td><td>3.8.17</td></tr><tr><td>OS</td><td>Linux x86_64</td></tr><tr><td>Memory</td><td>16.62 GB</td></tr><tr><td>CPU Max Thread</td><td>16</td></tr><tr><td>Date</td><td>Tue Jan 2 15:11:15 2024</td></tr>\n",
"</table>\n"
],
"text/plain": [
"<mindquantum.utils.show_info.InfoTable at 0x7fe789ad0820>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from mindquantum.utils.show_info import InfoTable\n",
"\n",
"InfoTable('mindquantum', 'scipy', 'numpy')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "MindSpore",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.17"
}
},
"nbformat": 4,
"nbformat_minor": 2
}