核心内容摘要
PowerPaint-V1实战：手把手教你智能填充照片缺失部分
项目初始化# 创建项目结构mkdir-p quantum_lab/{core,experiments,ui}cdquantum_lab# 创建requirements.txtcatrequirements.txt'EOF' streamlit=
1.
2
0 numpy=
1.
2
0 scipy=
1.
1
0 matplotlib=
3.
0 pandas=
2.
0 plotly=
5.
1
0 seaborn=
0.
1
0 tqdm=
4.
6
0 joblib=
1.
0 EOF# 创建主文件touch__init__.pytouchmain.py
核心后端实现
1 量子态模拟器基类# core/backend.pyfromabcimportABC,abstractmethodfromtypingimportList,Tuple,Dict,AnyimportnumpyasnpclassQuantumBackend(ABC):"""量子计算后端抽象基类"""@abstractmethoddefinitialize(self,n_qubits:int)-None:"""初始化量子寄存器"""pass@abstractmethoddefapply_gate(self,gate_name:str,targets:List[int],controls:List[int]=None,params:Dict=None)-None:"""应用量子门"""pass@abstractmethoddefget_statevector(self)-np.ndarray:"""获取当前量子态"""pass@abstractmethoddefmeasure(self,shots:int=
-Dict[str,int]:"""测量量子态"""pass@abstractmethoddefreset(self)-None:"""重置到|0态"""pass@abstractmethoddefexpectation_value(self,pauli_string:str)-float:"""计算Pauli算符的期望值"""pass
2 Statevector模拟器实现# core/statevector_backend.pyimportnumpyasnpfromtypingimportList,Dict,Anyimportitertoolsfrom.backendimportQuantumBackendclassStatevectorBackend(QuantumBackend):"""纯Python实现的量子态矢量模拟器"""# Pauli矩阵定义PAULI_MATRICES={'I':np.array([[1,0],[0,1]],dtype=complex),'X':np.array([[0,1],[1,0]],dtype=complex),'Y':np.array([[0,-1j],[1j,0]],dtype=complex),'Z':np.array([[1,0],[0,-1]],dtype=complex),}# 基础量子门矩阵GATE_MATRICES={'H':np.array([[1,1],[1,-1]],dtype=complex)/np.sqrt(
,'X':np.array([[0,1],[1,0]],dtype=complex),'Y':np.array([[0,-1j],[1j,0]],dtype=complex),'Z':np.array([[1,0],[0,-1]],dtype=complex),'S':np.array([[1,0],[0,1j]],dtype=complex),'T':np.array([[1,0],[0,np.exp(1j*np.pi/
]],dtype=complex),'CNOT':None,# 特殊处理'SWAP':None,# 特殊处理}def__init__(self,n_qubits:int):self.n_qubits=n_qubits self.state=np.zeros(2**n_qubits,dtype=complex)self.state[0]=
0# |
..0⟩self.gate_history=[]definitialize(self,n_qubits:int=None)-None:"""初始化量子寄存器"""ifn_qubits:self.n_qubits=n_qubits self.state=np.zeros(2**self.n_qubits,dtype=complex)self.state[0]=
0self.gate_history=[]def_tensor_product(self,matrices:List[np.ndarray])-np.ndarray:"""计算张量积"""result=matrices[0]formatinmatrices[1:]:result=np.kron(result,mat)returnresultdef_apply_single_qubit_gate(self,gate:np.ndarray,target:int)-None:"""应用单量子比特门"""# 构造完整的变换矩阵matrices=[self.PAULI_MATRICES['I']]*self.n_qubits matrices[target]=gate full_matrix=self._tensor_product(matrices)# 应用变换self.state=full_matrix @ self.statedef_apply_controlled_gate(self,gate:np.ndarray,control:int,target:int)-None:"""应用受控门（CNOT, CZ等）"""# 构建受控门的矩阵表示dim=2**self.n_qubits matrix=np.eye(dim,dtype=complex)# 遍历所有基态foriinrange(dim):# 将索引转换为二进制字符串bits=format(i,f'0{self.n_qubits}b')# 检查控制位是否为1ifbits[control]=='1':# 计算目标位翻转后的索引target_bit=bits[target]new_target_bit='0'iftarget_bit=='1'else'1'# 构造新的二进制字符串new_bits=list(bits)new_bits[target]=new_target_bit new_bits_str=''.join(new_bits)# 计算新索引j=int(new_bits_str,
# 对于CNOT，就是简单的交换振幅matrix[i,i]=0matrix[i,j]=1# 应用变换self.state=matrix @ self.statedef_rotation_gate(self,axis:str,angle:float)-np.ndarray:"""生成旋转门矩阵"""ifaxis=='x':returnnp.array([[np.cos(angle/
,-1j*np.sin(angle/
],[-1j*np.sin(angle/
,np.cos(angle/
]],dtype=complex)elifaxis=='y':returnnp.array([[np.cos(angle/
,-np.sin(angle/
],[np.sin(angle/
,np.cos(angle/
]],dtype=complex)elifaxis=='z':returnnp.array([[np.exp(-1j*angle/
,0],[0,np.exp(1j*angle/
]],dtype=complex)else:raiseValueError(f"Unknown rotation axis:{axis}")defapply_gate(self,gate_name:str,targets:List[int],controls:List[int]=None,params:Dict=None)-None:"""应用量子门"""controls=controlsor[]params=paramsor{}# 记录门操作self.gate_history.append({'gate':gate_name,'targets':targets,'controls':controls,'params':params})# 处理旋转门ifgate_name.startswith('R'):axis=gate_name[1].lower()angle=params.get('theta',
0.
gate_matrix=self._rotation_gate(axis,angle)fortargetintargets:self._apply_single_qubit_gate(gate_matrix,target)# 处理标准单量子比特门elifgate_nameinself.GATE_MATRICESandself.GATE_MATRICES[gate_name]isnotNone:gate_matrix=self.GATE_MATRICES[gate_name]fortargetintargets:self._apply_single_qubit_gate(gate_matrix,target)# 处理CNOT门elifgate_name=='CNOT':iflen(controls)!=1orlen(targets)!=1:raiseValueError("CNOT gate requires exactly one control and one target")self._apply_controlled_gate(self.GATE_MATRICES['X'],controls[0],targets[0])# 处理CZ门elifgate_name=='CZ':iflen(controls)!=1orlen(targets)!=1:raiseValueError("CZ gate requires exactly one control and one target")self._apply_controlled_gate(self.PAULI_MATRICES['Z'],controls[0],targets[0])else:raiseValueError(f"Unsupported gate:{gate_name}")defget_statevector(self)-np.ndarray:"""获取当前量子态"""returnself.state.copy()defget_probabilities(self)-np.ndarray:"""获取测量概率分布"""returnnp.abs(self.state)**2defmeasure(self,shots:int=
-Dict[str,int]:"""测量量子态"""probs=self.get_probabilities()counts={}# 概率采样samples=np.random.choice(len(probs),size=shots,p=probs)# 统计结果forsampleinsamples:bitstring=format(sample,f'0{self.n_qubits}b')counts[bitstring]=counts.get(bitstring,
+1returncountsdefexpectation_value(self,pauli_string:str)-float:"""计算Pauli算符的期望值"""# 解析Pauli字符串，如 "X0 Z1"terms=pauli_string.split()coeff=
0iflen(terms)==0:return
0# 检查是否有系数ifterms[0].replace('.','').replace('-','').isdigit():coeff=float(terms[0])terms=terms[1:]# 构建Pauli算符的矩阵表示matrices=[self.PAULI_MATRICES['I']]*self.n_qubitsforterminterms:pauli=term[0]qubit=int(term[1:])matrices[qubit]=self.PAULI_MATRICES[pauli]# 计算张量积pauli_matrix=self._tensor_product(matrices)# 计算期望值expectation=np.real(np.vdot(self.state,pauli_matrix @ self.state))returncoeff*expectationdefreset(self)-None:"""重置到|0态"""self.initialize(self.n_qubits)def__str__(self)-str:"""字符串表示"""returnf"StatevectorBackend(n_qubits={self.n_qubits})"
3 可视化工具# core/visualization.pyimportnumpyasnpimportmatplotlib.pyplotaspltfrommatplotlib.patchesimportFancyArrowPatchfrommpl_toolkits.mplot3dimportproj3dimportplotly.graph_objectsasgoclassQuantumVisualizer:"""量子态可视化工具"""@staticmethoddefplot_statevector(statevector:np.ndarray,title:str="Quantum State"):"""绘制量子态振幅和相位"""fig,(ax1,ax
=plt.subplots(1,2,figsize=(12,
)n_qubits=int(np.log2(len(statevector)))basis_states=[format(i,f'0{n_qubits}b')foriinrange(len(statevector))]# 振幅图amplitudes=np.abs(statevector)ax
bar(basis_states,amplitudes**2,alpha=
7,color='blue')ax
set_xlabel('Basis State')ax
set_ylabel('Probability')ax
set_title(f'{title}- Probabilities')ax
tick_params(axis='x',rotation=
# 相位图phases=np.angle(statevector)ax
bar(basis_states,phases,alpha=
7,color='red')ax
set_xlabel('Basis State')ax
set_ylabel('Phase (radians)')ax
set_title(f'{title}- Phases')ax
tick_params(axis='x',rotation=
plt.tight_layout()returnfig@staticmethoddefplot_bloch_vector(theta:float,phi:float,title:str="Bloch Sphere"):"""在Bloch球上绘制量子态"""# 计算笛卡尔坐标x=np.sin(theta)*np.cos(phi)y=np.sin(theta)*np.sin(phi)z=np.cos(theta)# 创建Bloch球fig=plt.figure(figsize=(8,
)ax=fig.add_subplot(111,projection='3d')# 绘制球体u=np.linspace(0,2*np.pi,
v=np.linspace(0,np.pi,
xs=np.outer(np.cos(u),np.sin(v))ys=np.outer(np.sin(u),np.sin(v))zs=np.outer(np.ones(np.size(u)),np.cos(v))ax.plot_surface(xs,ys,zs,color='b',alpha=
0.