J.R. Johansson and P.D. Nation
For more information about QuTiP see http://qutip.org
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%matplotlib inline
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import numpy as np
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from qutip import *
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"""
Plot the process tomography matrices for some 1, 2, and 3-qubit qubit gates.
"""
gates = [['C-NOT', cnot()],
['SWAP', swap()],
['$i$SWAP', iswap()],
['$\sqrt{i\mathrm{SWAP}}$', sqrtiswap()],
['S-NOT', snot()],
['$\pi/2$ phase gate', phasegate(np.pi/2)],
['Toffoli', toffoli()],
['Fredkin', fredkin()]]
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def plt_qpt_gate(gate, figsize=(8,6)):
name = gate[0]
U_psi = gate[1]
N = len(U_psi.dims[0]) # number of qubits
# create a superoperator for the density matrix
# transformation rho = U_psi * rho_0 * U_psi.dag()
U_rho = spre(U_psi) * spost(U_psi.dag())
# operator basis for the process tomography
op_basis = [[qeye(2), sigmax(), sigmay(), sigmaz()] for i in range(N)]
# labels for operator basis
op_label = [["$i$", "$x$", "$y$", "$z$"] for i in range(N)]
# calculate the chi matrix
chi = qpt(U_rho, op_basis)
# visualize the chi matrix
fig, ax = qpt_plot_combined(chi, op_label, name, figsize=figsize)
ax.set_title(name)
return fig, ax
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plt_qpt_gate(gates[0]);
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plt_qpt_gate(gates[1]);
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plt_qpt_gate(gates[2]);
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plt_qpt_gate(gates[3]);
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plt_qpt_gate(gates[4]);
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plt_qpt_gate(gates[5]);
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fig, ax = plt_qpt_gate(gates[6], figsize=(16,12))
ax.axis('tight');
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fig, ax = plt_qpt_gate(gates[7], figsize=(16,12))
ax.axis('tight');
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from qutip.ipynbtools import version_table
version_table()
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