mindquantum 发行版

最新版

v0.9.11

eb306d5

2024-01-31 17:47

v0.9.11

# MindQuantum Release Notes

## MindQuantum 0.9.11 Release Notes

### 主要特性和增强

#### Gates

- [STABLE] [`任意轴旋转门`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.Rn.html#mindquantum.core.gates.Rn): 新增绕布洛赫球上任意轴旋转的单比特门[`Rn`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.Rn.html#mindquantum.core.gates.Rn)。
- [STABLE] [`matrix`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.Rxx.html#mindquantum.core.gates.Rxx.matrix): 量子门支持通过该接口并指定参数`full=True`来获取量子门完整的矩阵形式（受作用位比特和控制位比特影响）。
- [STABLE] [`热弛豫信道`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.ThermalRelaxationChannel.html#mindquantum.core.gates.ThermalRelaxationChannel): 新增 ThermalRelaxationChannel 热弛豫信道。
- [Alpha] [`量子测量`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.Measure.html#mindquantum.core.gates.Measure): 测量门现支持比特重置功能，可将测量后的量子态重置为|0⟩态或者|1⟩态。优化测量门执行速度。
- [STABLE] [`RotPauliString`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.RotPauliString.html#mindquantum.core.gates.RotPauliString): 新增任意泡利串旋转门。
- [STABLE] [`GroupedPauli`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.GroupedPauli.html#mindquantum.core.gates.GroupedPauli): 新增泡利组合门，该门比逐个执行单个泡利门会更加快速。
- [STABLE] [`GroupedPauliChannel`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.GroupedPauliChannel.html#mindquantum.core.gates.GroupedPauliChannel): 新增泡利信道组合信道，该组合信道比逐一执行泡利信道更快。
- [STABLE] [`SX`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/gates/mindquantum.core.gates.SXGate.html): 新增根号X门。
- [STABLE] [Givens]: 新增Givens旋转门。

#### Circuit

- [STABLE] [`summary`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/circuit/mindquantum.core.circuit.Circuit.html#mindquantum.core.circuit.Circuit.summary): 通过该接口展示的量子线路汇总信息会以表格形式呈现，更加美观直接。
- [STABLE] [`svg`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/circuit/mindquantum.core.circuit.Circuit.html#mindquantum.core.circuit.Circuit.svg): 现在可以通过控制参数`scale`来对量子线路图进行缩放。
- [STABLE] [`openqasm`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/circuit/mindquantum.core.circuit.Circuit.html#mindquantum.core.circuit.Circuit): 量子线路直接支持转化为[`openqasm`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/circuit/mindquantum.core.circuit.Circuit.html#mindquantum.core.circuit.Circuit.to_openqasm)或者从[`openqasm`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/circuit/mindquantum.core.circuit.Circuit.html#mindquantum.core.circuit.Circuit.from_openqasm)转化为mindquantum线路。

#### ParameterResolver

- [STABLE] [`PRGenerator`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/parameterresolver/mindquantum.core.parameterresolver.PRGenerator.html#mindquantum.core.parameterresolver.PRGenerator): [`new`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/core/parameterresolver/mindquantum.core.parameterresolver.PRGenerator.html#mindquantum.core.parameterresolver.PRGenerator.new)接口支持配置临时的前缀和后缀。

#### Ansatz

- [STABLE] [`硬件友好型量子线路`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/algorithm/mindquantum.algorithm.nisq.html#ansatz): 新增多种硬件友好型量子线路，请参考论文[Physics-Constrained Hardware-Efficient Ansatz on Quantum Computers that is Universal, Systematically Improvable, and Size-consistent](https://arxiv.org/abs/2307.03563)。

#### Device

- [STABLE] [`QubitsTopology`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/device/mindquantum.device.QubitsTopology.html#mindquantum.device.QubitsTopology): 支持通过[set_edge_color](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/device/mindquantum.device.QubitsTopology.html#mindquantum.device.QubitsTopology.set_edge_color)设置不同边的颜色。支持通过`show`来直接展示拓扑结构图。

#### Simulator

- [STABLE] [`sampling`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/simulator/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.sampling): 加速量子模拟器在对不含噪声且测量门全部在线路末端的量子线路的采样。

#### utils

- [STABLE] [`进度条`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.utils.html#progress-bar): 新增两个基于rich构建的简单易用的进度条，分别为支持单层循环的[`SingleLoopProgress`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/utils/mindquantum.utils.SingleLoopProgress.html#mindquantum.utils.SingleLoopProgress)和支持两层循环的[`TwoLoopsProgress`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/utils/mindquantum.utils.TwoLoopsProgress.html#mindquantum.utils.TwoLoopsProgress)。
- [Alpha] [random_insert_gates]: 支持在量子线路中随机插入量子门。

#### Algorithm

- [Alpha] [`MQSABRE`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/algorithm/mapping/mindquantum.algorithm.mapping.MQSABRE.html#mindquantum.algorithm.mapping.MQSABRE): 新增支持设置量子门保真度的比特映射算法。

### Bug Fix

- [`PR1971`](https://gitee.com/mindspore/mindquantum/pulls/1971): 修复[`amplitude_encoder`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/algorithm/library/mindquantum.algorithm.library.amplitude_encoder.html#mindquantum.algorithm.library.amplitude_encoder)中符号错误问题。
- [`PR2094`](https://gitee.com/mindspore/mindquantum/pulls/2094): 修复[`get_expectation_with_grad`](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/simulator/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation_with_grad)在使用parameter shift规则时随机数种子单一性问题。
- [`PR2164`](https://gitee.com/mindspore/mindquantum/pulls/2164): 修复windows系统下的构建脚本传入参数问题。
- [`PR2171`](https://gitee.com/mindspore/mindquantum/pulls/2171): 修复密度矩阵模拟器在量子态复制时可能遇到的空指针问题。
- [`PR2175`](https://gitee.com/mindspore/mindquantum/pulls/2175): 修复泡利信道的概率可以为负数的问题。
- [`PR2176`](https://gitee.com/mindspore/mindquantum/pulls/2176): 修复parameter shift规则在处理含控制位量子门时的问题。
- [`PR2210`](https://gitee.com/mindspore/mindquantum/pulls/2210): 修复parameter shift规则在处理多参数门且部分参数为常数时的问题。

### 贡献者

感谢以下开发者做出的贡献：

yufan, wengwenkang, xuxusheng, Damien Ngyuen, zhouxu, wangzidong, yangkang, lujiale, zhangzhenghai, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng, 朱祎康, dorothy20212021, dsdsdshe, buyulin, norl-corxilea, herunhong, Arapat Ablimit, NoE, panshijie, longhanlin.

欢迎以任何形式对项目提供贡献！

最后提交信息为： !2258 [PY] Fix openqasm for identity gate.

v0.9.0

ca1a612

2023-10-16 11:07

v0.9.0

# MindQuantum Release Notes

## MindQuantum 0.9.0 Release Notes

### 主要特性和增强

#### 数据精度

- [STABLE] `数据精度`: MindQuantum 现支持 `float32`、`float64`、`complex64`和`complex128`四种精度类型，可为各种算符、参数解析器和模拟器设置不同的精度类型。

#### Gates

- [STABLE] [`通用量子门`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#%E9%80%9A%E7%94%A8%E9%87%8F%E5%AD%90%E9%97%A8): 新增多个两比特泡利旋转门，包括：[`Rxx`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rxx.html#mindquantum.core.gates.Rxx)，[`Rxy`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rxy.html#mindquantum.core.gates.Rxy)，[`Rxz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rxz.html#mindquantum.core.gates.Rxz)，[`Ryy`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Ryy.html#mindquantum.core.gates.Ryy)，[`Ryz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Ryz.html#mindquantum.core.gates.Ryz)和[`Rzz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rzz.html#mindquantum.core.gates.Rzz)。
- [STABLE] [`噪声信道`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#%E9%87%8F%E5%AD%90%E4%BF%A1%E9%81%93): 噪声信道现在支持通过 `.matrix()` 接口返回噪声信道的 kraus 算符。

#### Operator

- [STABLE] [`QubitOperator`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.QubitOperator.html#mindquantum.core.operators.QubitOperator): 新增 [`relabel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.QubitOperator.html#mindquantum.core.operators.QubitOperator.relabel) 接口，支持按照新的比特编号来重排算符。[`FermionOperator`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.FermionOperator.html#mindquantum.core.operators.FermionOperator.relabel)同样支持该功能。
- [STABLE] [`基态计算`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.ground_state_of_sum_zz.html#mindquantum.core.operators.ground_state_of_sum_zz): 新增接口支持计算只包含 pauli z 算符和 pauli z 算符的直积的哈密顿量的基态能量。

#### Ansatz

- [STABLE] [`Ansatz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.html#ansatz): 新增 Arixv:[`1905.10876`](https://arxiv.org/abs/1905.10876) 中提到的19个 ansatz，先均已实现。

#### Circuit

- [STABLE] [`ChannelAdder`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.circuit.html#channel-adder): 新增 `ChannelAdder` 模块，支持定制化的将各种量子噪声信道添加量子线路中，以此构成一个噪声模型，更多教案请参考：[`ChannelAdder`](https://mindspore.cn/mindquantum/docs/zh-CN/master/noise_simulator.html)。

#### Simulator

- [STABLE] [`密度矩阵模拟器`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator): 新增密度矩阵模拟器，模拟器名称为 `mqmatrix`。支持变分量子算法、噪声模拟等，与现有 `mqvector` 全振幅模拟器功能基本对齐。
- [BETA] [`parameter shift`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation_with_grad): 量子模拟器梯度算子现支持 parameter shift 算法，更贴近于实验。
- [STABLE] [`期望计算`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation): 接口与 [`get_expectation_with_grad`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation_with_grad)基本对齐，但是不会计算梯度值，节省时间。

#### Device

- [STABLE] [`QubitNode`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.QubitNode.html#mindquantum.device.QubitNode): 新增量子比特拓扑接口中的比特节点对象，支持对比特的位置和颜色以及连通性进行配置。
- [STABLE] [`QubitsTopology`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.QubitsTopology.html#mindquantum.device.QubitsTopology): 量子比特拓扑结构，支持自定义拓扑结构。同时可使用预定义结构：线性拓扑结构 [`LinearQubits`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.LinearQubits.html#mindquantum.device.LinearQubits) 和方格点拓扑结构 [`GridQubits`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.GridQubits.html#mindquantum.device.GridQubits)

#### Algorithm

- [STABLE] [`比特映射`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.mapping.SABRE.html#mindquantum.algorithm.mapping.SABRE): 新增比特映射算法 [`SABRE`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.mapping.SABRE.html#mindquantum.algorithm.mapping.SABRE)，论文请参考 Arxiv [`1809.02573`](https://arxiv.org/abs/1809.02573)。
- [BETA] [`误差缓解`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.error_mitigation.zne.html#mindquantum.algorithm.error_mitigation.zne): 新增零噪声外推算法算法来进行量子误差缓解。
- [STABLE] [`线路折叠`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.error_mitigation.fold_at_random.html#mindquantum.algorithm.error_mitigation.fold_at_random): 新增量子线路折叠功能，支持保证量子线路等价性的同时增长量子线路。
- [BETA] [`量子线路编译`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.compiler.html#module-mindquantum.algorithm.compiler): 新增量子线路编译模块，利用 [`DAG`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.compiler.DAGCircuit.html#mindquantum.algorithm.compiler.DAGCircuit) 图对量子线路进行编译，支持门替换、门融合和门分解等量子编译算法。
- [STABLE] [`ansatz_variance`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.ansatz_variance.html#mindquantum.algorithm.nisq.ansatz_variance): 新增接口计算变分量子线路中的某个参数的梯度的方差，可用于验证变分量子线路的[`贫瘠高原`](https://www.nature.com/articles/s41467-018-07090-4)现象。

#### Framework

- [STABLE] [`QRamVecLayer`](https://mindspore.cn/mindquantum/docs/zh-CN/master/layer/mindquantum.framework.QRamVecLayer.html#mindquantum.framework.QRamVecLayer): 新增 QRam 量子编码层，支持将经典数据直接编码为全振幅量子态。对应的算子为 [`QRamVecOps`](https://mindspore.cn/mindquantum/docs/zh-CN/master/operations/mindquantum.framework.QRamVecOps.html#mindquantum.framework.QRamVecOps)。

#### IO

- [STABLE] [`OpenQASM`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.io.OpenQASM.html#mindquantum.io.OpenQASM): OpenQASM 新增 [`from_string`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.io.OpenQASM.html#mindquantum.io.OpenQASM.from_string) 接口，支持将字符串格式的 OpenQASM 转化为 MindQuantum 中的量子线路。

#### Bug fix

- [`PR1757`](https://gitee.com/mindspore/mindquantum/pulls/1757): 修复[`StronglyEntangling`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.StronglyEntangling.html#mindquantum.algorithm.nisq.StronglyEntangling)在深度大于2时的bug。
- [`PR1700`](https://gitee.com/mindspore/mindquantum/pulls/1700): 修复[`CNOT`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.CNOTGate.html#mindquantum.core.gates.CNOTGate)门矩阵表达式和[`AmplitudeDampingChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.AmplitudeDampingChannel.html#mindquantum.core.gates.AmplitudeDampingChannel)的逻辑错误。
- [`PR1523`](https://gitee.com/mindspore/mindquantum/pulls/1523): 修复[`PhaseDampingChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.PhaseDampingChannel.html#mindquantum.core.gates.PhaseDampingChannel)的逻辑错误。

### 贡献者

感谢以下开发者做出的贡献：

yufan, wengwenkang, xuxusheng, Damien Ngyuen, zhouxu, wangzidong, yangkang, lujiale, zhangzhenghai, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng, 朱祎康, dorothy20212021, dsdsdshe, buyulin, norl-corxilea, herunhong, Arapat Ablimit, NoE, panshijie, longhanlin.

欢迎以任何形式对项目提供贡献！

最后提交信息为： !1965 [CXX] Support I as token

预览版本

v0.9.0-rc1

d8d3fe4

2023-05-08 18:55

v0.9.0-rc1

# MindQuantum Release Notes

## MindQuantum 0.9.0 Release Notes

### 主要特性和增强

#### 数据精度

- [STABLE] `数据精度`: MindQuantum 现支持 `float32`、`float64`、`complex64`和`complex128`四种精度类型，可为各种算符、参数解析器和模拟器设置不同的精度类型。

#### Gates

- [STABLE] [`通用量子门`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#%E9%80%9A%E7%94%A8%E9%87%8F%E5%AD%90%E9%97%A8): 新增多个两比特泡利旋转门，包括：[`Rxx`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rxx.html#mindquantum.core.gates.Rxx)，[`Rxy`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rxy.html#mindquantum.core.gates.Rxy)，[`Rxz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rxz.html#mindquantum.core.gates.Rxz)，[`Ryy`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Ryy.html#mindquantum.core.gates.Ryy)，[`Ryz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Ryz.html#mindquantum.core.gates.Ryz)和[`Rzz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.Rzz.html#mindquantum.core.gates.Rzz)。
- [STABLE] [`噪声信道`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#%E9%87%8F%E5%AD%90%E4%BF%A1%E9%81%93): 噪声信道现在支持通过 `.matrix()` 接口返回噪声信道的 kraus 算符。

#### Operator

- [STABLE] [`QubitOperator`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.QubitOperator.html#mindquantum.core.operators.QubitOperator): 新增 [`relabel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.QubitOperator.html#mindquantum.core.operators.QubitOperator.relabel) 接口，支持按照新的比特编号来重排算符。[`FermionOperator`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.FermionOperator.html#mindquantum.core.operators.FermionOperator.relabel)同样支持该功能。
- [STABLE] [`基态计算`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.operators.ground_state_of_sum_zz.html#mindquantum.core.operators.ground_state_of_sum_zz): 新增接口支持计算只包含 pauli z 算符和 pauli z 算符的直积的哈密顿量的基态能量。

#### Ansatz

- [STABLE] [`Ansatz`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.html#ansatz): 新增 Arixv:[`1905.10876`](https://arxiv.org/abs/1905.10876) 中提到的19个 ansatz，先均已实现。

#### Circuit

- [STABLE] [`ChannelAdder`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.circuit.html#channel-adder): 新增 `ChannelAdder` 模块，支持定制化的将各种量子噪声信道添加量子线路中，以此构成一个噪声模型，更多教案请参考：[`ChannelAdder`](https://mindspore.cn/mindquantum/docs/zh-CN/master/noise_simulator.html)。

#### Simulator

- [STABLE] [`密度矩阵模拟器`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator): 新增密度矩阵模拟器，模拟器名称为 `mqmatrix`。支持变分量子算法、噪声模拟等，与现有 `mqvector` 全振幅模拟器功能基本对齐。
- [BETA] [`parameter shift`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation_with_grad): 量子模拟器梯度算子现支持 parameter shift 算法，更贴近于实验。
- [STABLE] [`期望计算`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation): 接口与 [`get_expectation_with_grad`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.simulator.Simulator.html#mindquantum.simulator.Simulator.get_expectation_with_grad)基本对齐，但是不会计算期望值，节省时间。

#### Device

- [STABLE] [`QubitNode`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.QubitNode.html#mindquantum.device.QubitNode): 新增量子比特拓扑接口中的比特节点对象，支持对比特的位置和颜色以及连通性进行配置。
- [STABLE] [`QubitsTopology`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.QubitsTopology.html#mindquantum.device.QubitsTopology): 量子比特拓扑结构，支持自定义拓扑结构。同时可使用预定义结构：线性拓扑结构 [`LinearQubits`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.LinearQubits.html#mindquantum.device.LinearQubits) 和方格点拓扑结构 [`GridQubits`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.device.GridQubits.html#mindquantum.device.GridQubits)

#### Algorithm

- [STABLE] [`比特映射`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.mapping.SABRE.html#mindquantum.algorithm.mapping.SABRE): 新增比特映射算法 [`SABRE`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.mapping.SABRE.html#mindquantum.algorithm.mapping.SABRE)，论文请参考 Arxiv [`1809.02573`](https://arxiv.org/abs/1809.02573)。
- [BETA] [`误差缓解`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.error_mitigation.zne.html#mindquantum.algorithm.error_mitigation.zne): 新增零噪声外推算法算法来进行量子误差缓解。
- [STABLE] [`线路折叠`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.error_mitigation.fold_at_random.html#mindquantum.algorithm.error_mitigation.fold_at_random): 新增量子线路折叠功能，支持保证量子线路等价性的同时增长量子线路。
- [BETA] [`量子线路编译`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.compiler.html#module-mindquantum.algorithm.compiler): 新增量子线路编译模块，利用 [`DAG`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.compiler.DAGCircuit.html#mindquantum.algorithm.compiler.DAGCircuit) 图对量子线路进行编译，支持门替换、门融合和门分解等量子编译算法。
- [STABLE] [`ansatz_variance`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.ansatz_variance.html#mindquantum.algorithm.nisq.ansatz_variance): 新增接口计算变分量子线路中的某个参数的梯度的方差，可用于验证变分量子线路的[`贫瘠高原`](https://www.nature.com/articles/s41467-018-07090-4)现象。

#### Framework

- [STABLE] [`QRamVecLayer`](https://mindspore.cn/mindquantum/docs/zh-CN/master/layer/mindquantum.framework.QRamVecLayer.html#mindquantum.framework.QRamVecLayer): 新增 QRam 量子编码层，支持将经典数据直接编码为全振幅量子态。对应的算子为 [`QRamVecOps`](https://mindspore.cn/mindquantum/docs/zh-CN/master/operations/mindquantum.framework.QRamVecOps.html#mindquantum.framework.QRamVecOps)。

#### IO

- [STABLE] [`OpenQASM`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.io.OpenQASM.html#mindquantum.io.OpenQASM): OpenQASM 新增 [`from_string`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.io.OpenQASM.html#mindquantum.io.OpenQASM.from_string) 接口，支持将字符串格式的 OpenQASM 转化为 MindQuantum 中的量子线路。

#### Bug fix

- [`PR1757`](https://gitee.com/mindspore/mindquantum/pulls/1757): 修复[`StronglyEntangling`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.StronglyEntangling.html#mindquantum.algorithm.nisq.StronglyEntangling)在深度大于2时的bug。
- [`PR1700`](https://gitee.com/mindspore/mindquantum/pulls/1700): 修复[`CNOT`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.CNOTGate.html#mindquantum.core.gates.CNOTGate)门矩阵表达式和[`AmplitudeDampingChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.AmplitudeDampingChannel.html#mindquantum.core.gates.AmplitudeDampingChannel)的逻辑错误。
- [`PR1523`](https://gitee.com/mindspore/mindquantum/pulls/1523): 修复[`PhaseDampingChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.PhaseDampingChannel.html#mindquantum.core.gates.PhaseDampingChannel)的逻辑错误。

### 贡献者

感谢以下开发者做出的贡献：

yufan, wengwenkang, xuxusheng, Damien Ngyuen, zhouxu, wangzidong, yangkang, lujiale, zhangzhenghai, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng, 朱祎康, dorothy20212021, dsdsdshe, buyulin, norl-corxilea, herunhong, Arapat Ablimit, NoE, panshijie, longhanlin.

欢迎以任何形式对项目提供贡献！

最后提交信息为： !1512 [CXX] compress simulator memory

v0.8.0

df4825a

2023-02-13 20:16

v0.8.0

# MindQuantum Release Notes

[查看中文](https://gitee.com/mindspore/mindquantum/blob/r0.8/RELEASE_CN.md)

## MindQuantum  0.8.0 Release Notes

### Major Feature and Improvements

#### Gates

- [STABLE] [`FSim`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html?highlight=fsim#mindquantum.core.gates.FSim): Fermionic simulation gate supported, and this gate also works in variational quantum algorithm.
- [STABLE] [`U3`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html?highlight=fsim#mindquantum.core.gates.U3): The single qubit arbitrary gate U3 now will work as a single gate but not a piece of quantum circuit. U3 gate also works in variational quantum algorithm.
- [STABLE] [`Customed parameterized gate`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantum.core.gates.gene_univ_parameterized_gate). Customed parameterized gate now will compiled to machine code by jit compiler [numba](https://numba.pydata.org), and the simulator backend can call customed parameterized gate in parallel thread.
- [STABLE] [`BarrierGate`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html?highlight=fsim#mindquantum.core.gates.BarrierGate): BarrierGate now can be acted on certain qubits.
- [STABLE] [`KrausChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html?highlight=fsim#mindquantum.core.gates.KrausChannel): Design a customed kraus channel for quantum simulator.

#### Circuit

- [STABLE] [`svg`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.svg): Now you can set the `width` to split the svg circuit, so that you can copy it into your paper.

#### Simulator

- [STABLE] **New simulator supported**. `mqvector` and `mqvector_gpu` are two mindquantum simulate that prepared for cpu and gpu. And `projectq` simulator will be deprecated. The new simulator is total compatible with old one, what you only to do is to change the backend name when you initialize the simulator.

**Note**

The attachments are **GPU** version for linux platform.

最后提交信息为： !1423 update api link in release

v0.7.0

931bffa

2022-07-13 09:26

v0.7.0

# MindQuantum Release Notes

[查看中文](https://gitee.com/mindspore/mindquantum/blob/r0.7/RELEASE_CN.md)

## MindQuantum 0.7.0 Release Notes

### Major Features and Improvements

#### Circuit

- [STABLE] [`as_encoder`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.as_encoder): Method of `Circuit` to mark this circuit as an encoder circuit.
- [STABLE] [`as_ansatz`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.as_ansatz): Method of `Circuit` to mark this circuit as an ansatz circuit.
- [STABLE] [`encoder_params_name`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.encoder_params_name): Method of `Circuit` to return the encoder parameters.
- [STABLE] [`ansatz_params_name`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.ansatz_params_name): Method of `Circuit` to return the ansatz parameters.
- [STABLE] [`remove_noise`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.remove_noise): Method of `Circuit` to remove all noise channel.
- [STABLE] [`with_noise`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.Circuit.with_noise): Method of `Circuit` to add a given noise channel after every gate.
- [STABLE] [`as_encoder`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.as_encoder): A decorator to wrap a function, so that it can generate an encoder circuit.
- [STABLE] [`as_ansatz`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.as_ansatz): A decorator to wrap a function, so that it can generate an ansatz circuit.
- [STABLE] [`qfi`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.qfi): A method that can calculate the quantum fisher information of a given parameterized quantum circuit.
- [STABLE] [`partial_psi_partial_psi`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.partial_psi_partial_psi): A method that can calculate the first part of quantum fisher information.
- [STABLE] [`partial_psi_psi`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.circuit.html#mindquantum.core.circuit.partial_psi_psi): A method that can calculate the second part of quantum fisher information.

#### Gates

- [STABLE] [`AmplitudeDampingChannel`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.gates.html#mindquantum.core.gates.AmplitudeDampingChannel): Amplitude damping channel express error that qubit is affected by the energy dissipation.
- [STABLE] [`PhaseDampingChannel`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.gates.html#mindquantum.core.gates.PhaseDampingChannel): Phase damping channel express error that qubit loses quantum information without exchanging energy with environment

#### FermionOperator and QubitOperator

- [STABLE] [`split`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.operators.html#mindquantum.core.operators.FermionOperator.split): A method of FermionOperator and QubitOperator that can split the coefficient with the operator.

#### ParameterResolver

- [STABLE] [`astype`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.astype): Convert the ParameterResolver to a given type, can be float or double complex
- [STABLE] [`const`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.const): Get the constant part of this ParameterResolver.
- [STABLE] [`is_const`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.is_const): Check whether this ParameterResolver is constant.
- [STABLE] [`encoder_part`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.encoder_part): Set a part of parameter to be encoder parameter.
- [STABLE] [`ansatz_part`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.ansatz_part): Set a part of parameter to be ansatz parameter.
- [STABLE] [`as_encoder`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.as_encoder): Set all parameter to encoder parameters.
- [STABLE] [`as_ansatz`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.as_ansatz): Set all parameter to ansatz parameters.
- [STABLE] [`encoder_parameters`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.encoder_parameters): Return all encoder parameters.
- [STABLE] [`ansatz_parameters`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.ansatz_parameters): Return all ansatz parameters.
- [STABLE] [`is_hermitian`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.is_hermitian): Check whether this ParameterResolver is hermitian conjugate.
- [STABLE] [`is_anti_hermitian`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.is_anti_hermitian): Check whether this ParameterResolver is anti hermitian conjugate.
- [STABLE] [`no_grad_parameters`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.no_grad_parameters): Return all parameters that do no require gradient.
- [STABLE] [`requires_grad_parameters`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.core.parameterresolver.html#mindquantum.core.parameterresolver.ParameterResolver.requires_grad_parameters): Return all parameters that require gradient.

#### Simulator

- [STABLE] [`copy`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.simulator.html#mindquantum.simulator.Simulator.copy): The simulator can now very easy to duplicate.
- [STABLE] [`apply_gate`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.simulator.html#mindquantum.simulator.Simulator.apply_gate): In this version, you can apply a gate in differential version.
- [BETA] [`inner_product`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.simulator.html#mindquantum.simulator.inner_product): Calculate the inner product of two state in two simulator.

#### IO

- [STABLE] [`BlochScene`](https://mindspore.cn/mindquantum/docs/en/master/mindquantum.io.html): Now we support display and animate a one qubit state in bloch sphere.

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, Damien Ngyuen, zhouxu, wangzidong, yangkang, lujiale, zhangzhenghai, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng, 朱祎康, dorothy20212021, dsdsdshe, buyulin, norl-corxilea, herunhong, Arapat Ablimit, NoE, panshijie, longhanlin.

Contributions of any kind are welcome!

最后提交信息为： !1088 Add pydocstyle to pre-commit hooks + update list of flake8 pl...

v0.6.0

6047e03

2022-04-07 17:05

v0.6.0

# MindQuantum 0.6.0

## MindQuantum 0.6.0 Release Notes

### Major Features and Improvements

#### Better iteration supported for `QubitOperator` and `FermionOperator`

> The following example will be demonstrated with `QubitOperator`

- Iter multiple terms `QubitOperator`

```python
>>> from mindquantum.core.operators import QubitOperator
>>> ops = QubitOperator('X0 Y1', 1) + QubitOperator('Z2 X3', {'a': 3})

>>> for idx, o in enumerate(ops):
>>>     print(f'Term {idx}: {o}')
```

You will get each term of this operator,

```bash
Term 0: 1 [X0 Y1]
Term 1: 3*a [Z2 X3]
```

- Iter single term `QubitOperator`

```python
>>> ops = QubitOperator('X0 Y1', 2)

>>> for idx, o in enumerate(ops.singlet()):
>>>     print(f'Word {idx}: {o}')
```

You will get each word of this operator with coefficient set to identity,

```bash
Word 0: 1 [X0]
Word 1: 1 [Y1]
```

### More built-in circuit supported

- [**general_w_state**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibrarygeneral-w-state): circuit that can prepare a w state.
- [**general_ghz_state**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibrarygeneral-ghz-state): circuit that can prepare a ghz state.
- [**bitphaseflip_operator**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibrarybitphaseflip-operator): circuit that can flip the sign of one or multiple calculation base.
- [**amplitude_encoder**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibraryamplitude-encoder): circuit that can encode classical number into quantum amplitude.

### Richer circuit operation supported

For origin circuit,

```python
>>> from mindquantum.core.circuit import Circuit
>>> circuit = Circuit().z(0).rx('a', 1, 0).y(1)
```

```bash
q0: ──Z──────●─────────
             │
q1: ───────RX(a)────Y──
```

- `shift` operator will shift the qubit index.

```python
from mindquantum.core.circuit import shift
>>> shift(circuit, 2)
```

```bash
q2: ──Z──────●─────────
             │
q3: ───────RX(a)────Y──
```

- Reverse circuit qubits, the circuit will be flipped upside down.

```python
>>> circuit.reverse_qubits()
```

```bash
q0: ───────RX(a)────Y──
             │
q1: ──Z──────●─────────
```

### Feature enhancement

- `MaxCutAnsatz`: [**get_partition**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.html#mindquantumalgorithmnisqmaxcutansatzget-partition)
- `MaxCutAnsatz`: [**get_cut_value**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.html#mindquantumalgorithmnisqmaxcutansatzget-cut-value)
- `Circuit`: [**is_measure_end**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.circuit.html#mindquantumcorecircuitcircuitis-measure-end)

### SVG supported

The quantum circuit build by mindquantum now can be showd by SVG in jupyter notebook, just call `svg()` of any `Circuit`.

```python
>>> from mindquantum import *

>>> circuit = (qft(range(3)) + BarrierGate(True)).measure_all()
>>> circuit.svg()
```

### Noise simulator supported

In This version, we can simulate a quantum circuit in noise simulator just by adding different noise channels. The following are supported channels:

- [`PauliChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatespaulichannel)
- [`BitFlipChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesbitflipchannel)
- [`PhaseFlipChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesphaseflipchannel)
- [`BitPhaseFlipChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesbitphaseflipchannel)
- [`DepolarizingChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesdepolarizingchannel)

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, wangzidong, yangkang, lujiale, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Damien Ngyuen, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

最后提交信息为： !457 update to version 0.6.0

v0.6.0rc1

f61a8ef

2022-03-31 16:12

Release 0.6.0-rc1

# MindQuantum 0.6.0

## MindQuantum 0.6.0 Release Notes

### Major Features and Improvements

#### Better iteration supported for `QubitOperator` and `FermionOperator`

> The following example will be demonstrated with `QubitOperator`

- Iter multiple terms `QubitOperator`

```python
>>> from mindquantum.core.operators import QubitOperator
>>> ops = QubitOperator('X0 Y1', 1) + QubitOperator('Z2 X3', {'a': 3})

>>> for idx, o in enumerate(ops):
>>>     print(f'Term {idx}: {o}')
```

You will get each term of this operator,

```bash
Term 0: 1 [X0 Y1]
Term 1: 3*a [Z2 X3]
```

- Iter single term `QubitOperator`

```python
>>> ops = QubitOperator('X0 Y1', 2)

>>> for idx, o in enumerate(ops.singlet()):
>>>     print(f'Word {idx}: {o}')
```

You will get each word of this operator with coefficient set to identity,

```bash
Word 0: 1 [X0]
Word 1: 1 [Y1]
```

### More built-in circuit supported

- [**general_w_state**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibrarygeneral-w-state): circuit that can prepare a w state.
- [**general_ghz_state**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibrarygeneral-ghz-state): circuit that can prepare a ghz state.
- [**bitphaseflip_operator**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibrarybitphaseflip-operator): circuit that can flip the sign of one or multiple calculation base.
- [**amplitude_encoder**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.library.html#mindquantumalgorithmlibraryamplitude-encoder): circuit that can encode classical number into quantum amplitude.

### Richer circuit operation supported

For origin circuit,

```python
>>> from mindquantum.core.circuit import Circuit
>>> circuit = Circuit().z(0).rx('a', 1, 0).y(1)
```

```bash
q0: ──Z──────●─────────
             │
q1: ───────RX(a)────Y──
```

- `shift` operator will shift the qubit index.

```python
from mindquantum.core.circuit import shift
>>> shift(circuit, 2)
```

```bash
q2: ──Z──────●─────────
             │
q3: ───────RX(a)────Y──
```

- Reverse circuit qubits, the circuit will be flipped upside down.

```python
>>> circuit.reverse_qubits()
```

```bash
q0: ───────RX(a)────Y──
             │
q1: ──Z──────●─────────
```

### Feature enhancement

- `MaxCutAnsatz`: [**get_partition**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.html#mindquantumalgorithmnisqmaxcutansatzget-partition)
- `MaxCutAnsatz`: [**get_cut_value**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.algorithm.nisq.html#mindquantumalgorithmnisqmaxcutansatzget-cut-value)
- `Circuit`: [**is_measure_end**](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.circuit.html#mindquantumcorecircuitcircuitis-measure-end)

### SVG supported

The quantum circuit build by mindquantum now can be showd by SVG in jupyter notebook, just call `svg()` of any `Circuit`.

```python
>>> from mindquantum import *

>>> circuit = (qft(range(3)) + BarrierGate(True)).measure_all()
>>> circuit.svg()
```

![circuit_svg](https://gitee.com/mindspore/mindquantum/raw/master/docs/circuit_svg.png)

### Noise simulator supported

In This version, we can simulate a quantum circuit in noise simulator just by adding different noise channels. The following are supported channels:

- [`PauliChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatespaulichannel)
- [`BitFlipChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesbitflipchannel)
- [`PhaseFlipChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesphaseflipchannel)
- [`BitPhaseFlipChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesbitphaseflipchannel)
- [`DepolarizingChannel`](https://mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.gates.html#mindquantumcoregatesdepolarizingchannel)

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, wangzidong, yangkang, lujiale, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Damien Ngyuen, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

最后提交信息为： !430 update readme

v0.5.0

0b53cfe

2022-02-28 16:29

v0.5.0

## MindQuantum 0.5.0 Release Notes

### Major Features and Improvements

### API Change

#### Backwards Incompatible Change

We unified the abbreviations of some nouns in MindQuantum.

- `isparameter` property of gate changes to `parameterized`

```python
>>> from mindquantum import RX
>>> gate = RX('a').on(0)
>>> gate.isparameter
True
```

</td>
<td>

```python
>>> from mindquantum import RX
>>> gate = RX('a').on(0)
>>> gate.parameterized
True
```

</td>
</tr>
</table>

- `para_name` of a quantum circuit changes to `params_name`

```python
>>> from mindquantum import Circuit
>>> circ = Circuit().rx('a', 0)
>>> circ.para_name
['a']
```

</td>
<td>

```python
>>> from mindquantum import Circuit
>>> circ = Circuit().rx('a', 0)
>>> circ.params_name
['a']
```

</td>
</tr>
</table>

The quantum neural network API was redesigned in this version. From now on, we can easily build a hybrid quantum neural network with the help of `Simulator` in `PYNATIVE_MODE`.

The following API was removed.

1. `generate_pqc_operator`
2. `PQC`
3. `MindQuantumLayer`
4. `generate_evolution_operator`
5. `Evolution`
6. `MindQuantumAnsatzOnlyLayer`
7. `MindQuantumAnsatzOnlyOperator`

The new API was shown as below.

1. `MQOps`
2. `MQN2Ops`
3. `MQAnsatzOnlyOps`
4. `MQN2AnsatzOnlyOps`
5. `MQEncoderOnlyOps`
6. `MQN2EncoderOnlyOps`
7. `MQLayer`
8. `MQN2Layer`
9. `MQAnsatzOnlyLayer`
10. `MQN2AnsatzOnlyLayer`

The above modules are placed in `mindquantum.framework`.

#### Removed

Due to the duplication of functions, we deleted some APIs.

- `mindquantum.circuit.StateEvolution`

The following APIs have been remoted.

- `mindquantum.core.operators.Hamiltonian.mindspore_data`
- `mindquantum.core.operators.Projector.mindspore_data`
- `mindquantum.core.circuit.Circuit.mindspore_data`
- `mindquantum.core.parameterresolver.ParameterResolver.mindspore_data`

#### New feature

New gates are shown as below.

- `mindquantum.core.gates.SGate`
- `mindquantum.core.gates.TGate`

Measurement on certain qubits are now supported. The related APIs are shown as below.

- `mindquantum.core.gates.Measure`
- `mindquantum.core.gates.MeasureResult`

QASM is now supported.

- `mindquantum.io.OpenQASM`
- `mindquantum.io.random_hiqasm`
- `mindquantum.io.HiQASM`

Simulator is now separated from MindSpore backend. Now you can easily to use a simulator.

- `mindquantum.simulator.Simulator`

### Refactoring

For improving MindQuantum's package structure, we did some refactoring on MindQuantum.

`mindquantum.gate.Hamiltonian`
</td><td>

`mindquantum.core.operators.Hamiltonian`
</td></tr>
<tr><td>

`mindquantum.gate.Projector`
</td><td>

`mindquantum.core.operators.Projector`
</td></tr>
<tr><td>

`mindquantum.circuit.qft`
</td><td>

`mindquantum.algorithm.library.qft`
</td></tr>
<tr><td>

`mindquantum.circuit.generate_uccsd`
</td><td>

`mindquantum.algorithm.nisq.chem.generate_uccsd`
</td></tr>
<tr><td>

`mindquantum.circuit.TimeEvolution`
</td><td>

`mindquantum.core.operators.TimeEvolution`
</td></tr>
<tr><td>

`mindquantum.utils.count_qubits`
</td><td>

`mindquantum.core.operators.count_qubits`
</td></tr>
<tr><td>

`mindquantum.utils.commutator`
</td><td>

`mindquantum.core.operators.commutator`
</td></tr><tr><td>

`mindquantum.utils.normal_ordered`
</td><td>

`mindquantum.core.operators.normal_ordered`
</td></tr><tr><td>

`mindquantum.utils.get_fermion_operator`
</td><td>

`mindquantum.core.operators.get_fermion_operator`
</td></tr><tr><td>

`mindquantum.utils.number_operator`
</td><td>

`mindquantum.core.operators.number_operator`
</td></tr><tr><td>

`mindquantum.utils.hermitian_conjugated`
</td><td>

`mindquantum.core.operators.hermitian_conjugated`
</td></tr><tr><td>

`mindquantum.utils.up_index`
</td><td>

`mindquantum.core.operators.up_index`
</td></tr><tr><td>

`mindquantum.utils.down_index`
</td><td>

`mindquantum.core.operators.down_index`
</td></tr><tr><td>

`mindquantum.utils.sz_operator`
</td><td>

`mindquantum.core.operators.sz_operator`
</td></tr>
<tr><td>

`mindquantum.ansatz.Ansatz`</td><td>

`mindquantum.algorithm.nisq.Ansatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.MaxCutAnsatz`
</td><td>

`mindquantum.algorithm.nisq.qaoa.MaxCutAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.Max2SATAnsatz`
</td><td>

`mindquantum.algorithm.nisq.qaoa.Max2SATAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.HardwareEfficientAnsatz`
</td><td>

`mindquantum.algorithm.nisq.chem.HardwareEfficientAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.QubitUCCAnsatz`
</td><td>

`mindquantum.algorithm.nisq.chem.QubitUCCAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.UCCAnsatz`
</td><td>

`mindquantum.algorithm.nisq.chem.UCCAnsatz`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.Transform`
</td><td>

`mindquantum.algorithm.nisq.chem.Transform`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.get_qubit_hamiltonian`
</td><td>

`mindquantum.algorithm.nisq.chem.get_qubit_hamiltonian`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.uccsd_singlet_generator`
</td><td>

`mindquantum.algorithm.nisq.chem.uccsd_singlet_generator`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.uccsd_singlet_get_packed_amplitudes`
</td><td>

`mindquantum.algorithm.nisq.chem.uccsd_singlet_get_packed_amplitudes`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.uccsd0_singlet_generator`
</td><td>

`mindquantum.algorithm.nisq.chem.uccsd0_singlet_generator`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.quccsd_generator`
</td><td>

`mindquantum.algorithm.nisq.chem.quccsd_generator`
</td></tr>
<tr><td>

`mindquantum.utils.bprint`
</td><td>

`mindquantum.io.bprint`
</td></tr>
<tr><td>

`mindquantum.circuit`
</td><td>

`mindquantum.core.circuit`
</td></tr>
<tr><td>

`mindquantum.gate`
</td><td>

`mindquantum.core.gates`
</td></tr>
<tr><td>

`mindquantum.ops`
</td><td>

`mindquantum.core.operators`
</td></tr>
<tr><td>

`mindquantum.parameterresolver`
</td><td>

`mindquantum.core.parameterresolver`
</td></tr>
<tr><td></td><td></td></tr>
</table>

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, Damien Ngyuen, zhouxu, wangzidong, yangkang, lujiale, zhangzhenghai, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

最后提交信息为： version 0.5.0

v0.5.0rc1

ab07ee3

2022-02-10 19:12

Release 0.5.0-rc1

# MindQuantum 0.5.0-rc1

## MindQuantum 0.5.0 Release Notes

### Major Features and Improvements

### API Change

#### Backwards Incompatible Change

We unified the abbreviations of some nouns in MindQuantum.

- `isparameter` property of gate changes to `parameterized`

```python
>>> from mindquantum import RX
>>> gate = RX('a').on(0)
>>> gate.isparameter
True
```

</td>
<td>

```python
>>> from mindquantum import RX
>>> gate = RX('a').on(0)
>>> gate.parameterized
True
```

</td>
</tr>
</table>

- `para_name` of a quantum circuit changes to `params_name`

```python
>>> from mindquantum import Circuit
>>> circ = Circuit().rx('a', 0)
>>> circ.para_name
['a']
```

</td>
<td>

```python
>>> from mindquantum import Circuit
>>> circ = Circuit().rx('a', 0)
>>> circ.params_name
['a']
```

</td>
</tr>
</table>

The quantum neural network API was redesigned in this version. From now on, we can easily build a hybrid quantum neural network with the help of `Simulator` in `PYNATIVE_MODE`.

The following API was removed.

1. `generate_pqc_operator`
2. `PQC`
3. `MindQuantumLayer`
4. `generate_evolution_operator`
5. `Evolution`
6. `MindQuantumAnsatzOnlyLayer`
7. `MindQuantumAnsatzOnlyOperator`

The new API was shown as below.

1. `MQOps`
2. `MQN2Ops`
3. `MQAnsatzOnlyOps`
4. `MQN2AnsatzOnlyOps`
5. `MQEncoderOnlyOps`
6. `MQN2EncoderOnlyOps`
7. `MQLayer`
8. `MQN2Layer`
9. `MQAnsatzOnlyLayer`
10. `MQN2AnsatzOnlyLayer`

The above modules are placed in `mindquantum.framework`.

#### Removed

Due to the duplication of functions, we deleted some APIs.

- `mindquantum.circuit.StateEvolution`

The following APIs have been remoted.

- `mindquantum.core.operators.Hamiltonian.mindspore_data`
- `mindquantum.core.operators.Projector.mindspore_data`
- `mindquantum.core.circuit.Circuit.mindspore_data`
- `mindquantum.core.parameterresolver.ParameterResolver.mindspore_data`

#### New feature

New gates are shown as below.

- `mindquantum.core.gates.SGate`
- `mindquantum.core.gates.TGate`

Measurement on certain qubits are now supported. The related APIs are shown as below.

- `mindquantum.core.gates.Measure`
- `mindquantum.core.gates.MeasureResult`

QASM is now supported.

- `mindquantum.io.OpenQASM`
- `mindquantum.io.random_hiqasm`
- `mindquantum.io.HiQASM`

Simulator is now separated from MindSpore backend. Now you can easily to use a simulator.

- `mindquantum.simulator.Simulator`

### Refactoring

For improving MindQuantum's package structure, we did some refactoring on MindQuantum.

`mindquantum.gate.Hamiltonian`
</td><td>

`mindquantum.core.operators.Hamiltonian`
</td></tr>
<tr><td>

`mindquantum.gate.Projector`
</td><td>

`mindquantum.core.operators.Projector`
</td></tr>
<tr><td>

`mindquantum.circuit.qft`
</td><td>

`mindquantum.algorithm.library.qft`
</td></tr>
<tr><td>

`mindquantum.circuit.generate_uccsd`
</td><td>

`mindquantum.algorithm.nisq.chem.generate_uccsd`
</td></tr>
<tr><td>

`mindquantum.circuit.TimeEvolution`
</td><td>

`mindquantum.core.operators.TimeEvolution`
</td></tr>
<tr><td>

`mindquantum.utils.count_qubits`
</td><td>

`mindquantum.core.operators.count_qubits`
</td></tr>
<tr><td>

`mindquantum.utils.commutator`
</td><td>

`mindquantum.core.operators.commutator`
</td></tr><tr><td>

`mindquantum.utils.normal_ordered`
</td><td>

`mindquantum.core.operators.normal_ordered`
</td></tr><tr><td>

`mindquantum.utils.get_fermion_operator`
</td><td>

`mindquantum.core.operators.get_fermion_operator`
</td></tr><tr><td>

`mindquantum.utils.number_operator`
</td><td>

`mindquantum.core.operators.number_operator`
</td></tr><tr><td>

`mindquantum.utils.hermitian_conjugated`
</td><td>

`mindquantum.core.operators.hermitian_conjugated`
</td></tr><tr><td>

`mindquantum.utils.up_index`
</td><td>

`mindquantum.core.operators.up_index`
</td></tr><tr><td>

`mindquantum.utils.down_index`
</td><td>

`mindquantum.core.operators.down_index`
</td></tr><tr><td>

`mindquantum.utils.sz_operator`
</td><td>

`mindquantum.core.operators.sz_operator`
</td></tr>
<tr><td>

`mindquantum.ansatz.Ansatz`</td><td>

`mindquantum.algorithm.nisq.Ansatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.MaxCutAnsatz`
</td><td>

`mindquantum.algorithm.nisq.qaoa.MaxCutAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.Max2SATAnsatz`
</td><td>

`mindquantum.algorithm.nisq.qaoa.Max2SATAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.HardwareEfficientAnsatz`
</td><td>

`mindquantum.algorithm.nisq.chem.HardwareEfficientAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.QubitUCCAnsatz`
</td><td>

`mindquantum.algorithm.nisq.chem.QubitUCCAnsatz`
</td></tr>
<tr><td>

`mindquantum.ansatz.UCCAnsatz`
</td><td>

`mindquantum.algorithm.nisq.chem.UCCAnsatz`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.Transform`
</td><td>

`mindquantum.algorithm.nisq.chem.Transform`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.get_qubit_hamiltonian`
</td><td>

`mindquantum.algorithm.nisq.chem.get_qubit_hamiltonian`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.uccsd_singlet_generator`
</td><td>

`mindquantum.algorithm.nisq.chem.uccsd_singlet_generator`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.uccsd_singlet_get_packed_amplitudes`
</td><td>

`mindquantum.algorithm.nisq.chem.uccsd_singlet_get_packed_amplitudes`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.uccsd0_singlet_generator`
</td><td>

`mindquantum.algorithm.nisq.chem.uccsd0_singlet_generator`
</td></tr>
<tr><td>

`mindquantum.hiqfermion.quccsd_generator`
</td><td>

`mindquantum.algorithm.nisq.chem.quccsd_generator`
</td></tr>
<tr><td>

`mindquantum.utils.bprint`
</td><td>

`mindquantum.io.bprint`
</td></tr>
<tr><td>

`mindquantum.circuit`
</td><td>

`mindquantum.core.circuit`
</td></tr>
<tr><td>

`mindquantum.gate`
</td><td>

`mindquantum.core.gates`
</td></tr>
<tr><td>

`mindquantum.ops`
</td><td>

`mindquantum.core.operators`
</td></tr>
<tr><td>

`mindquantum.parameterresolver`
</td><td>

`mindquantum.core.parameterresolver`
</td></tr>
<tr><td></td><td></td></tr>
</table>

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, Damien Ngyuen, zhouxu, wangzidong, yangkang, lujiale, zhangzhenghai, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

最后提交信息为： !337 0.5.0rc1

v0.3.1-rc1

e7111ce

2021-09-26 09:23

Release 0.3.1-rc1

# MindQuantum 0.3.1-rc1

## MindQuantum 0.3.1 Release Notes

### Major Features and Improvements

- Three tutorials have been rewritten to make them easier to read
- Circuit information such as qubit number, parameters will update immediately after you add gate
- The UN operator now support parameterized gate
- New ansatz that solving max 2 sat problem now are supported

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, wangzidong, yangkang, lujiale, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Damien Ngyuen, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

# MindQuantum 0.2.0

## MindQuantum 0.2.0 Release Notes

### Major Features and Improvements

* Parameterized FermionOperator and QubitOperator for quantum chemistry
* Different kinds of transformation between FermionOperator and QubitOperator
* UCCSD, QAOA and hardware efficient ansatz supported
* MindQuantumAnsatzOnlyLayer for simulating circuit with ansatz only circuit
* TimeEvolution with first order Trotter decomposition
* High level operations for modifying quantum circuit

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, wanzidong, yankang, lujiale, fanyi, zhangwengang, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Damien Ngyuen, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

# MindQuantum 0.1.0

## MindQuantum 0.1.0 Release Notes

Initial release of MindQuantum.

### Major Features and Improvements

* Easily build parameterized quantum circuit.
* Effectively simulate quantum circuit.
* Calculating the gradient of parameters of quantum circuit.
* PQC (parameterized quantum circuit) operator that naturally compatible with other operators in mindspore framework.
* Evolution operator that evaluate a quantum circuit and return the quantum state.
* Data parallelization for PQC operator.

### Contributors

Thanks goes to these wonderful people:

yufan, wengwenkang, xuxusheng, wanzidong, yankang, lujiale, wangkaisheng, zhoufeng, wangsiyuan, gongxiaoqing, chengxianbin, sunxiyin, wenwenkang, lvdingshun, cuijiangyu, chendiqing, zhangkai, Damien Ngyuen, Zotov Yuriy, liqin, zengjinglin, cuixiaopeng.

Contributions of any kind are welcome!

最后提交信息为： update mindquantum/version.py.