ext extend usage

本文通过一个具体的JavaScript代码示例展示了如何使用构造函数和原型链实现类的继承,并覆盖父类的方法。具体包括定义基类及其方法,通过构造函数创建子类并调用父类的构造方法,以及使用Ext.extend扩展子类以覆盖或添加新方法。

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just for fun !
var x = function(){
return {toString2 : function(){return "x";}}
}() ;

y = function(){
y.superclass.constructor.call(this);
};

Ext.extend(y,x,{
toString2 : function(){return "y";}
});

var xxx=new y();
alert(xxx.toString2());
import numpy as np from cvxpy import Variable, Parameter, Minimize, Problem, norm, bmat, semidefinite def create_ppt_symmetric_extension(rho, d_A, d_B): """ Creates a PPT symmetric extension for rho. Parameters: - rho: Density matrix of shape (d_A * d_B, d_A * d_B). - d_A: Dimension of subsystem A. - d_B: Dimension of subsystem B. Returns: - Extended density matrix of shape (d_A**2 * d_B, d_A**2 * d_B). """ # Create the extended matrix with initial guess d_ext = d_A**2 * d_B rho_ext = Variable((d_ext, d_ext), hermitian=True) # Define constraints for PPT symmetric extension constraints = [ rho_ext >> 0, # Positive semidefinite rho_ext == rho_ext.H, # Hermitian # Symmetry under exchange of first and third parties rho_ext == bmat([[rho_ext[d_A*i:d_A*(i+1), d_A*j:d_A*(j+1)] for j in range(d_A*d_B)] for i in range(d_A)]) ] # Ensure partial traces match the original state eye_d_A = np.eye(d_A) for i in range(d_B): projector = np.kron(np.eye(d_A**2), eye_d_A[:, [i]]) constraints.append(projector @ rho_ext @ projector.T == rho[i*d_A:(i+1)*d_A, :]) # Solve the optimization problem obj = Minimize(norm(rho_ext - np.kron(rho, eye_d_A))) prob = Problem(obj, constraints) prob.solve() return rho_ext.value def check_entanglement(rho, d_A, d_B, max_k=2): """ Checks if a given density matrix is entangled using the hierarchy of PPT symmetric extensions. Parameters: - rho: Density matrix of shape (d_A * d_B, d_A * d_B). - d_A: Dimension of subsystem A. - d_B: Dimension of subsystem B. - max_k: Maximum number of copies of A to extend to. Returns: - Boolean indicating whether the state is entangled. """ for k in range(1, max_k + 1): print(f"Checking extension to {k} copies...") rho_extended = create_ppt_symmetric_extension(rho, d_A, d_B) if rho_extended is None: print("State is entangled.") return True print("State is separable or undetermined within tested levels.") return False if __name__ == "__main__": # Example usage d_A = 2 d_B = 2 # Define your density matrix rho here rho = np.array([[0.5, 0, 0, 0.5], [0, 0, 0, 0], [0, 0, 0, 0], [0.5, 0, 0, 0.5]]) is_entangled = check_entanglement(rho, d_A, d_B) print(f"The state {'is' if is_entangled else 'is not'} entangled.")请把这个代码中的解释说明部分换成中文
03-20
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