コード例 #1
0
ファイル: quantumprocess.py プロジェクト: sxwang/TIQC-SPICE
def Unitary2Chi(U, B = None):
    """
    map unitary process U onto the according chi matrix
    option: provide an orthogonal basis B instead of the standard
    Pauli basis for the chi matrix
    """

    NumberOfQubits  = int(np.log2(U.shape[0]))
    chidim = 4**NumberOfQubits

    a = proctom.getopbase()

    A = a

    for k in xrange(NumberOfQubits-1):
        A = proctom.baseappend(A,a)

    lam = np.zeros(chidim, dtype=complex)
    for k in xrange(chidim):
        lam[k] = np.trace(np.dot(A[k].transpose().conjugate(),U))/2**NumberOfQubits

    chi = np.zeros((chidim,chidim), dtype=complex)
    for k in xrange(chidim):
        for l in xrange(chidim):
            chi[k,l]=lam[k]*lam[l].conjugate()
    return chi
コード例 #2
0
ファイル: quantumprocess.py プロジェクト: knsmith/TIQC-SPICE
def Unitary2Chi(U, B=None):
    """
    map unitary process U onto the according chi matrix
    option: provide an orthogonal basis B instead of the standard
    Pauli basis for the chi matrix
    """

    NumberOfQubits = int(np.log2(U.shape[0]))
    chidim = 4**NumberOfQubits

    a = proctom.getopbase()

    A = a

    for k in xrange(NumberOfQubits - 1):
        A = proctom.baseappend(A, a)

    lam = np.zeros(chidim, dtype=complex)
    for k in xrange(chidim):
        lam[k] = np.trace(np.dot(A[k].transpose().conjugate(),
                                 U)) / 2**NumberOfQubits

    chi = np.zeros((chidim, chidim), dtype=complex)
    for k in xrange(chidim):
        for l in xrange(chidim):
            chi[k, l] = lam[k] * lam[l].conjugate()
    return chi
コード例 #3
0
ファイル: optimizechi.py プロジェクト: knsmith/TIQC-SPICE
import time
import scipy.optimize as optimize

import proctom

expm = lnlg.expm
from math import pi

X = np.array([[0,1],[1,0]])
Y = np.array([[0,-1j],[1j,0]])
Z = np.array([[1,0],[0,-1]])
Id = np.array([[1,0],[0,1]])

#Paulis=[Id, X, Y, Z]

Paulis = proctom.getopbase() # added 2010-07-26 by TM: to solve operator definition hickups

def findoptimizedrot(ideal_chi, my_chi, return_chi=False, use_random=True, verbose = True):
    """Finds single qubit operations that maximize the overlap
    between an experimental chi matrix and an ideal chi matrix"""
    try:
        if ideal_chi.shape != my_chi.shape:
            raise RuntimeError("Matrices don't have the same dimensions")
    except AttributeError:
        my_chi = my_chi.chi
    nr_of_ions = np.log2(my_chi.shape[0]) / 2
    # if nr_of_ions > 1:
    #     raise NotImplementedError("Can only optimize one qubit by now")
    if use_random:
        p0 = np.random.random_sample(3*nr_of_ions)
    else: