def test_mc_dtypes2():
"Monte-carlo: check for correct dtypes (average_states=False)"
# set system parameters
kappa = 2.0 # mirror coupling
gamma = 0.2 # spontaneous emission rate
g = 1 # atom/cavity coupling strength
wc = 0 # cavity frequency
w0 = 0 # atom frequency
wl = 0 # driving frequency
E = 0.5 # driving amplitude
N = 5 # number of cavity energy levels (0->3 Fock states)
tlist = np.linspace(0, 10, 5) # times for expectation values
# construct Hamiltonian
ida = qeye(N)
idatom = qeye(2)
a = tensor(destroy(N), idatom)
sm = tensor(ida, sigmam())
H = (w0 - wl) * sm.dag() * sm + (wc - wl) * a.dag() * a + \
1j * g * (a.dag() * sm - sm.dag() * a) + E * (a.dag() + a)
# collapse operators
C1 = np.sqrt(2 * kappa) * a
C2 = np.sqrt(gamma) * sm
C1dC1 = C1.dag() * C1
C2dC2 = C2.dag() * C2
# intial state
psi0 = tensor(basis(N, 0), basis(2, 1))
opts = Options(average_expect=False)
data = mcsolve(
H, psi0, tlist, [C1, C2], [C1dC1, C2dC2, a], ntraj=5, options=opts)
assert_equal(isinstance(data.expect[0][0][1], float), True)
assert_equal(isinstance(data.expect[0][1][1], float), True)
assert_equal(isinstance(data.expect[0][2][1], complex), True)
def testHOFiniteTemperatureStates():
"""
brmesolve: harmonic oscillator, finite temperature, states
"""
N = 10
w0 = 1.0 * 2 * np.pi
g = 0.05 * w0
kappa = 0.25
times = np.linspace(0, 25, 1000)
a = destroy(N)
H = w0 * a.dag() * a + g * (a + a.dag())
psi0 = ket2dm((basis(N, 4) + basis(N, 2) + basis(N, 0)).unit())
n_th = 1.5
w_th = w0/np.log(1 + 1/n_th)
def S_w(w):
if w >= 0:
return (n_th + 1) * kappa
else:
return (n_th + 1) * kappa * np.exp(w / w_th)
c_ops = [np.sqrt(kappa * (n_th + 1)) * a, np.sqrt(kappa * n_th) * a.dag()]
a_ops = [a + a.dag()]
e_ops = []
res_me = mesolve(H, psi0, times, c_ops, e_ops)
res_brme = brmesolve(H, psi0, times, a_ops, e_ops, [S_w])
n_me = expect(a.dag() * a, res_me.states)
n_brme = expect(a.dag() * a, res_brme.states)
diff = abs(n_me - n_brme).max()
assert_(diff < 1e-2)
def testCase3(self):
"mesolve: cavity-qubit with interaction, decay"
use_rwa = True
N = 4 # number of cavity fock states
wc = 2 * np.pi * 1.0 # cavity frequency
wa = 2 * np.pi * 1.0 # atom frequency
g = 2 * np.pi * 0.1 # coupling strength
kappa = 0.05 # cavity dissipation rate
gamma = 0.001 # atom dissipation rate
pump = 0.25 # atom pump rate
# start with an excited atom and maximum number of photons
n = N - 2
psi0 = tensor(basis(N, n), basis(2, 1))
tlist = np.linspace(0, 200, 500)
nc, na = self.jc_integrate(
N, wc, wa, g, kappa, gamma, pump, psi0, use_rwa, tlist)
# we don't have any analytics for this parameters, so
# compare with the steady state
nc_ss, na_ss = self.jc_steadystate(
N, wc, wa, g, kappa, gamma, pump, psi0, use_rwa, tlist)
nc_ss = nc_ss * np.ones(np.shape(nc))
na_ss = na_ss * np.ones(np.shape(na))
assert_(abs(nc[-1] - nc_ss[-1]) < 0.005, True)
assert_(abs(na[-1] - na_ss[-1]) < 0.005, True)
def testHOFiniteTemperature(self):
"brmesolve: harmonic oscillator, finite temperature"
N = 10
w0 = 1.0 * 2 * np.pi
g = 0.05 * w0
kappa = 0.15
times = np.linspace(0, 25, 1000)
a = destroy(N)
H = w0 * a.dag() * a + g * (a + a.dag())
psi0 = ket2dm((basis(N, 4) + basis(N, 2) + basis(N, 0)).unit())
n_th = 1.5
w_th = w0/np.log(1 + 1/n_th)
def S_w(w):
if w >= 0:
return (n_th + 1) * kappa
else:
return (n_th + 1) * kappa * np.exp(w / w_th)
c_ops = [np.sqrt(kappa * (n_th + 1)) * a, np.sqrt(kappa * n_th) * a.dag()]
a_ops = [a + a.dag()]
e_ops = [a.dag() * a, a + a.dag()]
res_me = mesolve(H, psi0, times, c_ops, e_ops)
res_brme = brmesolve(H, psi0, times, a_ops, e_ops, [S_w])
for idx, e in enumerate(e_ops):
diff = abs(res_me.expect[idx] - res_brme.expect[idx]).max()
assert_(diff < 1e-2)
def testCase2(self):
"mesolve: cavity-qubit without interaction, decay"
use_rwa = True
N = 4 # number of cavity fock states
wc = 2 * np.pi * 1.0 # cavity frequency
wa = 2 * np.pi * 1.0 # atom frequency
g = 2 * np.pi * 0.0 # coupling strength
kappa = 0.005 # cavity dissipation rate
gamma = 0.01 # atom dissipation rate
pump = 0.0 # atom pump rate
# start with an excited atom and maximum number of photons
n = N - 2
psi0 = tensor(basis(N, n), basis(2, 1))
tlist = np.linspace(0, 1000, 2000)
nc, na = self.jc_integrate(
N, wc, wa, g, kappa, gamma, pump, psi0, use_rwa, tlist)
nc_ex = (n + 0.5 * (1 - np.cos(2 * g * np.sqrt(n + 1) * tlist))) * \
np.exp(-kappa * tlist)
na_ex = 0.5 * (1 + np.cos(2 * g * np.sqrt(n + 1) * tlist)) * \
np.exp(-gamma * tlist)
assert_(max(abs(nc - nc_ex)) < 0.005, True)
assert_(max(abs(na - na_ex)) < 0.005, True)
def testJCZeroTemperature():
"""
brmesolve: Jaynes-Cummings model, zero temperature
"""
N = 10
a = tensor(destroy(N), identity(2))
sm = tensor(identity(N), destroy(2))
psi0 = ket2dm(tensor(basis(N, 1), basis(2, 0)))
a_ops = [(a + a.dag())]
e_ops = [a.dag() * a, sm.dag() * sm]
w0 = 1.0 * 2 * np.pi
g = 0.05 * 2 * np.pi
kappa = 0.05
times = np.linspace(0, 2 * 2 * np.pi / g, 1000)
c_ops = [np.sqrt(kappa) * a]
H = w0 * a.dag() * a + w0 * sm.dag() * sm + \
g * (a + a.dag()) * (sm + sm.dag())
res_me = mesolve(H, psi0, times, c_ops, e_ops)
res_brme = brmesolve(H, psi0, times, a_ops, e_ops,
spectra_cb=[lambda w: kappa * (w >= 0)])
for idx, e in enumerate(e_ops):
diff = abs(res_me.expect[idx] - res_brme.expect[idx]).max()
assert_(diff < 5e-2) # accept 5% error
def testHOZeroTemperature():
"""
brmesolve: harmonic oscillator, zero temperature
"""
N = 10
w0 = 1.0 * 2 * np.pi
g = 0.05 * w0
kappa = 0.15
times = np.linspace(0, 25, 1000)
a = destroy(N)
H = w0 * a.dag() * a + g * (a + a.dag())
psi0 = ket2dm((basis(N, 4) + basis(N, 2) + basis(N, 0)).unit())
c_ops = [np.sqrt(kappa) * a]
a_ops = [a + a.dag()]
e_ops = [a.dag() * a, a + a.dag()]
res_me = mesolve(H, psi0, times, c_ops, e_ops)
res_brme = brmesolve(H, psi0, times, a_ops, e_ops,
spectra_cb=[lambda w: kappa * (w >= 0)])
for idx, e in enumerate(e_ops):
diff = abs(res_me.expect[idx] - res_brme.expect[idx]).max()
assert_(diff < 1e-2)
def eigen(f, a, b): if a == 1: # returns excited states return qt.basis(int(4*(f+1)), int(b+(f+1))) elif a == 2: # returns ground states return qt.basis(int(4*(f+1)), int(b+3*(f+1))) else: return "Error in function eigen"
def test_EntropyConcurrence():
"Concurrence"
# check concurrence = 1 for maximal entangled (Bell) state
bell = ket2dm(
(tensor(basis(2), basis(2)) + tensor(basis(2, 1), basis(2, 1))).unit())
assert_equal(abs(concurrence(bell) - 1.0) < 1e-15, True)
# check all concurrence values >=0
rhos = [rand_dm(4, dims=[[2, 2], [2, 2]]) for k in range(10)]
for k in rhos:
assert_equal(concurrence(k) >= 0, True)
def mcsolve(self, ntrajs=500, exps=[], initial_state=None):
"""mcsolve
Interface to qutip mcsolve for the system
:param ntrajs: number of quantum trajectories to average.
Default is QuTiP
default of 500
:param exps: List of expectation values to calculate at
each timestep
"""
if initial_state is None:
initial_state = qt.tensor(qt.basis(self.N_field_levels, 0), qt.basis(2, 0))
return qt.mcsolve(self.hamiltonian()[0], initial_state, self.tlist, self._c_ops(), exps, ntraj=ntrajs)
def test_destroy():
"Destruction operator"
b4 = basis(5, 4)
d5 = destroy(5)
test1 = d5 * b4
assert_equal(np.allclose(test1.full(), 2.0 * basis(5, 3).full()), True)
d3 = destroy(3)
matrix3 = np.array([[0.00000000 + 0.j, 1.00000000 + 0.j, 0.00000000 + 0.j],
[0.00000000 + 0.j, 0.00000000 + 0.j, 1.41421356 + 0.j],
[0.00000000 + 0.j, 0.00000000 + 0.j, 0.00000000 + 0.j]])
assert_equal(np.allclose(matrix3, d3.full()), True)
def test_create():
"Creation operator"
b3 = basis(5, 3)
c5 = create(5)
test1 = c5 * b3
assert_equal(np.allclose(test1.full(), 2.0 * basis(5, 4).full()), True)
c3 = create(3)
matrix3 = np.array([[0.00000000 + 0.j, 0.00000000 + 0.j, 0.00000000 + 0.j],
[1.00000000 + 0.j, 0.00000000 + 0.j, 0.00000000 + 0.j],
[0.00000000 + 0.j, 1.41421356 + 0.j, 0.00000000 + 0.j]])
assert_equal(np.allclose(matrix3, c3.full()), True)
def _label_to_ket(self, one_pho_label):
# return qobj with correct structure of node
pol = {'H':0, 'V':1}
port = {k:v for v,k in enumerate(ascii_uppercase)}
node_info = self.get_node_info()
dof = one_pho_label.split('_')
if self._is_polarized:
# create pol vec
polarization = qt.basis(2, pol[dof[0]])
port = qt.basis(node_info[dof[1]], port[dof[2]])
else:
port = qt.basis(node_info[dof[0]], port[dof[1]])
return qt.tensor([polarization, port])
def compute_item(self, name, model):
name += "_1"
fock_dim = self.editor.fock_dim.value()
timestep = self.editor.timestep.value()
initial_alpha = self.editor.initial_alpha.value()
steps = model.get_steps(fock_dim, timestep)
res0 = coherent_dm(fock_dim, initial_alpha)
qubit0 = ket2dm((basis(2, 0) + basis(2, 1)) / np.sqrt(2))
psi0 = tensor(qubit0, res0)
def add_to_viewer(r):
item = WignerPlotter(name, r)
item.wigners_complete.connect(lambda: self.viewer.add_item(item))
win.statusBar().showMessage("Computing Wigners")
args = (steps, fock_dim, psi0, timestep)
run_in_process(to_state_list, add_to_viewer, args)
#self.thread_is_running.emit()
win.statusBar().showMessage("Computing States")
def heisenberg_weyl_operators(d=2):
w = np.exp(2 * np.pi * 1j / d)
X = qt.Qobj([
qt.basis(d, (idx + 1) % d).data.todense().view(np.ndarray)[:, 0] for idx in xrange(d)
])
Z = qt.Qobj(np.diag(w ** np.arange(d)))
return [X**i * Z**j for i in xrange(d) for j in xrange(d)]
def __init__(self, field, mass=const.MASS_OF_PROTON,
charge=const.ELECTRON_CHARGE,
initial_state=qt.basis(2,0)):
self.mass = mass
self.charge = charge
self.parent_field = field
self.gyromagnetic_ratio = 267.513e6
self.parent_field.particles.append(self)
self.state = initial_state
def testTLS(self):
"brmesolve: qubit"
delta = 0.0 * 2 * np.pi
epsilon = 0.5 * 2 * np.pi
gamma = 0.25
times = np.linspace(0, 10, 100)
H = delta/2 * sigmax() + epsilon/2 * sigmaz()
psi0 = (2 * basis(2, 0) + basis(2, 1)).unit()
c_ops = [np.sqrt(gamma) * sigmam()]
a_ops = [sigmax()]
e_ops = [sigmax(), sigmay(), sigmaz()]
res_me = mesolve(H, psi0, times, c_ops, e_ops)
res_brme = brmesolve(H, psi0, times, a_ops, e_ops,
spectra_cb=[lambda w: gamma * (w >= 0)])
for idx, e in enumerate(e_ops):
diff = abs(res_me.expect[idx] - res_brme.expect[idx]).max()
assert_(diff < 1e-2)
def test_mc_seed_noreuse():
"Monte-carlo: check not reusing seeds"
N0 = 6
N1 = 6
N2 = 6
# damping rates
gamma0 = 0.1
gamma1 = 0.4
gamma2 = 0.1
alpha = np.sqrt(2) # initial coherent state param for mode 0
tlist = np.linspace(0, 10, 2)
ntraj = 500 # number of trajectories
# define operators
a0 = tensor(destroy(N0), qeye(N1), qeye(N2))
a1 = tensor(qeye(N0), destroy(N1), qeye(N2))
a2 = tensor(qeye(N0), qeye(N1), destroy(N2))
# number operators for each mode
num0 = a0.dag() * a0
num1 = a1.dag() * a1
num2 = a2.dag() * a2
# dissipative operators for zero-temp. baths
C0 = np.sqrt(2.0 * gamma0) * a0
C1 = np.sqrt(2.0 * gamma1) * a1
C2 = np.sqrt(2.0 * gamma2) * a2
# initial state: coherent mode 0 & vacuum for modes #1 & #2
psi0 = tensor(coherent(N0, alpha), basis(N1, 0), basis(N2, 0))
# trilinear Hamiltonian
H = 1j * (a0 * a1.dag() * a2.dag() - a0.dag() * a1 * a2)
# run Monte-Carlo
data1 = mcsolve(H, psi0, tlist, [C0, C1, C2], [num0, num1, num2],
ntraj=ntraj)
data2 = mcsolve(H, psi0, tlist, [C0, C1, C2], [num0, num1, num2],
ntraj=ntraj)
diff_flag = False
for k in range(ntraj):
if len(data1.col_times[k]) != len(data2.col_times[k]):
diff_flag = 1
break
else:
if not np.allclose(data1.col_which[k],data2.col_which[k]):
diff_flag = 1
break
assert_equal(diff_flag, 1)
def testCOPS():
"""
brmesolve: c_ops alone
"""
delta = 0.0 * 2 * np.pi
epsilon = 0.5 * 2 * np.pi
gamma = 0.25
times = np.linspace(0, 10, 100)
H = delta/2 * sigmax() + epsilon/2 * sigmaz()
psi0 = (2 * basis(2, 0) + basis(2, 1)).unit()
c_ops = [np.sqrt(gamma) * sigmam()]
e_ops = [sigmax(), sigmay(), sigmaz()]
res_me = mesolve(H, psi0, times, c_ops, e_ops)
res_brme = brmesolve(H, psi0, times, [], e_ops,
spectra_cb=[], c_ops=c_ops)
for idx, e in enumerate(e_ops):
diff = abs(res_me.expect[idx] - res_brme.expect[idx]).max()
assert_(diff < 1e-2)
def test_driven_tls(datadir):
hs = local_space('tls', namespace='sys', basis=('g', 'e'))
w = symbols(r'\omega', real=True)
pi = sympy.pi
cos = sympy.cos
t, T, E0 = symbols('t, T, E_0', real=True)
a = 0.16
blackman = 0.5 * (1 - a - cos(2*pi * t/T) + a*cos(4*pi*t/T))
H0 = Destroy(hs).dag() * Destroy(hs)
H1 = LocalSigma(hs, 'g', 'e') + LocalSigma(hs, 'e', 'g')
H = w*H0 + 0.5 * E0 * blackman * H1
circuit = SLH(identity_matrix(0), [], H)
num_vals = {w: 1.0, T:10.0, E0:1.0*2*np.pi}
# test qutip conversion
H_qutip, Ls = circuit.substitute(num_vals).HL_to_qutip(time_symbol=t)
assert len(Ls) == 0
assert len(H_qutip) == 3
times = np.linspace(0, num_vals[T], 201)
psi0 = qutip.basis(2, 1)
states = qutip.mesolve(H_qutip, psi0, times, [], []).states
pop0 = np.array(qutip_population(states, state=0))
pop1 = np.array(qutip_population(states, state=1))
datfile = os.path.join(datadir, 'pops.dat')
#print("DATFILE: %s" % datfile)
#np.savetxt(datfile, np.c_[times, pop0, pop1, pop0+pop1])
pop0_expect, pop1_expect = np.genfromtxt(datfile, unpack=True,
usecols=(1,2))
assert np.max(np.abs(pop0 - pop0_expect)) < 1e-12
assert np.max(np.abs(pop1 - pop1_expect)) < 1e-12
# Test QSD conversion
codegen = QSDCodeGen(circuit, num_vals=num_vals, time_symbol=t)
codegen.add_observable(LocalSigma(hs, 'e', 'e'), name='P_e')
psi0 = BasisKet(hs, 'e')
codegen.set_trajectories(psi_initial=psi0,
stepper='AdaptiveStep', dt=0.01,
nt_plot_step=5, n_plot_steps=200, n_trajectories=1)
scode = codegen.generate_code()
compile_cmd = _cmd_list_to_str(codegen._build_compile_cmd(
qsd_lib='$HOME/local/lib/libqsd.a',
qsd_headers='$HOME/local/include/qsd/',
executable='test_driven_tls', path='$HOME/bin',
compiler='mpiCC', compile_options='-g -O0'))
print(compile_cmd)
codefile = os.path.join(datadir, "test_driven_tls.cc")
#print("CODEFILE: %s" % codefile)
#with(open(codefile, 'w')) as out_fh:
#out_fh.write(scode)
#out_fh.write("\n")
with open(codefile) as in_fh:
scode_expected = in_fh.read()
assert scode.strip() == scode_expected.strip()
def test_EntropyVN():
"von-Neumann entropy"
# verify that entropy_vn gives correct binary entropy
a = np.linspace(0, 1, 20)
for k in range(len(a)):
# a*|0><0|
x = a[k] * ket2dm(basis(2, 0))
# (1-a)*|1><1|
y = (1 - a[k]) * ket2dm(basis(2, 1))
rho = x + y
# Von-Neumann entropy (base 2) of rho
out = entropy_vn(rho, 2)
if k == 0 or k == 19:
assert_equal(out, -0.0)
else:
assert_(abs(-out - a[k] * np.log2(a[k])
- (1. - a[k]) * np.log2((1. - a[k]))) < 1e-12)
# test_ entropy_vn = 0 for pure state
psi = rand_ket(10)
assert_equal(abs(entropy_vn(psi)) <= 1e-13, True)
def temporal_basis_vector(waveguide_emission_indices, n_time_bins):
"""
Generate a temporal basis vector for emissions at specified time bins into
specified waveguides.
Parameters
----------
waveguide_emission_indices : list or tuple
List of indices where photon emission occurs for each waveguide,
e.g. [[t1_wg1], [t1_wg2, t2_wg2], [], [t1_wg4, t2_wg4, t3_wg4]].
n_time_bins : int
Number of time bins; the range over which each index can vary.
Returns
-------
temporal_basis_vector : :class: qutip.Qobj
A basis vector representing photon scattering at the specified indices.
If there are W waveguides, T times, and N photon emissions, then the
basis vector has dimensionality (W*T)^N.
"""
# Cast waveguide_emission_indices to list for mutability
waveguide_emission_indices = [list(i) for i in waveguide_emission_indices]
# Calculate total number of waveguides
W = len(waveguide_emission_indices)
# Calculate total number of emissions
num_emissions = sum([len(waveguide_indices) for waveguide_indices in
waveguide_emission_indices])
if num_emissions == 0:
return basis(W * n_time_bins, 0)
# Pad the emission indices with zeros
offset_indices = []
for i, wg_indices in enumerate(waveguide_emission_indices):
offset_indices += [index + (i * n_time_bins) for index in wg_indices]
# Return an appropriate tensor product state
return tensor([basis(n_time_bins * W, i) for i in offset_indices])
def test_MCCollapseTimesOperators():
"Monte-carlo: Check for stored collapse operators and times"
N = 10
kappa = 5.0
times = np.linspace(0, 10, 100)
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9)
c_ops = [np.sqrt(kappa) * a, np.sqrt(kappa) * a]
result = mcsolve(H, psi0, times, c_ops, [], ntraj=1)
assert_(len(result.col_times[0]) > 0)
assert_(len(result.col_which) == len(result.col_times))
assert_(set(result.col_which[0]) == {0, 1})
def trajectory(self, exps=None, initial_state=None, draw=False):
"""for convenience. Calculates the trajectory of an
observable for one montecarlo run. Default expectation is
cavity amplitude, default initial state is bipartite
vacuum. todo: draw: draw trajectory on bloch sphere.
Write in terms of mcsolve??"""
if exps is None or draw is True:
exps = []
if initial_state is None:
initial_state = qt.tensor(qt.basis(self.N_field_levels, 0), qt.basis(2, 0))
self.one_traj_soln = qt.mcsolve(self.hamiltonian(), initial_state, self.tlist, self._c_ops(), exps, ntraj=1)
if self.noisy:
print(self.one_traj_soln.states[0][2].ptrace(1))
if not draw:
return self.one_traj_soln
else:
self.b_sphere = qt.Bloch()
self.b_sphere.add_states([state.ptrace(1) for state in self.one_traj_soln.states[0]], "point")
self.b_sphere.point_markers = ["o"]
self.b_sphere.size = (10, 10)
self.b_sphere.show()
def fock_state(p, qudit_n, mode_n_entries=[], density_matrix_form=True):
"""
Creates specific fock states
p: DictExtended object with sizes of qudit and mode Hilbert spaces
qubit_n: fock level of the qudit
mode_n_entries: list of lists, each with an index and a corresponding (nonground) fock state
For example:
mode_n_entries=[[0, 2], [4, 1]]
would mean we want to have the mode with zero index be in the 2nd excited state, and mode with index 4
be in the 1st excited state. All other modes should be in their ground states.
"""
mode_states = [q.basis(p.N_m, 0)] * p.mode_count
for entry in mode_n_entries:
mode_states[entry[0]]=q.basis(p.N_m, entry[1])
state=q.tensor(q.basis(p.N_q, qudit_n), *mode_states)
if density_matrix_form:
return q.ket2dm(state)
return state
def test_MCSimpleSingleExpect():
"""Monte-carlo: Constant H with single expect operator"""
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
kappa = 0.2 # coupling to oscillator
c_op_list = [np.sqrt(kappa) * a]
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve(H, psi0, tlist, c_op_list, a.dag() * a, ntraj=ntraj)
expt = mcdata.expect[0]
actual_answer = 9.0 * np.exp(-kappa * tlist)
avg_diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(avg_diff < mc_error, True)
def test_MCNoCollExpt():
"Monte-carlo: Constant H with no collapse ops (expect)"
error = 1e-8
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
c_op_list = []
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve(H, psi0, tlist, c_op_list, [a.dag() * a], ntraj=ntraj)
expt = mcdata.expect[0]
actual_answer = 9.0 * np.ones(len(tlist))
diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(diff < error, True)
def test_MCSimpleConst():
"mcsolve_f90: Constant H with constant collapse"
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
kappa = 0.2 # coupling to oscillator
c_op_list = [np.sqrt(kappa) * a]
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve_f90(H, psi0, tlist, c_op_list, [a.dag() * a])
expt = mcdata.expect[0]
actual_answer = 9.0 * np.exp(-kappa * tlist)
avg_diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(avg_diff < mc_error, True)
def test_MCSimpleConstFunc():
"Monte-carlo: Collapse terms constant (func format)"
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
kappa = 0.2 # coupling to oscillator
c_op_list = [[a, sqrt_kappa]]
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve(H, psi0, tlist, c_op_list, [a.dag() * a], ntraj=ntraj)
expt = mcdata.expect[0]
actual_answer = 9.0 * np.exp(-kappa * tlist)
avg_diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(avg_diff < mc_error, True)
def testSuperJC(self):
"mesolve: super vs. density matrix as initial condition"
me_error = 1e-6
use_rwa = True
N = 4 # number of cavity fock states
wc = 2 * np.pi * 1.0 # cavity frequency
wa = 2 * np.pi * 1.0 # atom frequency
g = 2 * np.pi * 0.1 # coupling strength
kappa = 0.05 # cavity dissipation rate
gamma = 0.001 # atom dissipation rate
pump = 0.25 # atom pump rate
# start with an excited atom and maximum number of photons
n = N - 2
psi0 = tensor(basis(N, n), basis(2, 1))
rho0vec = operator_to_vector(psi0 * psi0.dag())
tlist = np.linspace(0, 100, 50)
out1, out2 = self.jc_integrate(N, wc, wa, g, kappa, gamma, pump, psi0, use_rwa, tlist)
fid = self.fidelitycheck(out1, out2, rho0vec)
assert_(max(abs(1.0 - fid)) < me_error, True)
def multi_index_2_vector(d, num_modes, fock_trunc):
return tensor(
[basis(fock_trunc, d.get(i, 0)) for i in range(num_modes)])
'''this function creates a vector representation a given fock state given the data for excitations per
def prep_K_qutip():
q = qutip.basis(2)
K = 1 / np.sqrt(2) * (qutip.sigmay() + qutip.sigmaz())
return K * q
def createEffects(eigenvectorsPerInput):
return list(
map(
lambda eigenvectors: list(
map(lambda eigvector: eigvector * eigvector.dag(), eigenvectors
)), eigenvectorsPerInput))
if __name__ == '__main__':
outputsAlice = [2, 2]
outputsBob = [2, 2]
qzero = qt.basis(2, 0)
qone = qt.basis(2, 1)
plus = 1 / np.sqrt(2) * (qzero + qone)
minus = 1 / np.sqrt(2) * (-qzero + qone)
psi = 2 / np.sqrt(3) * (qt.tensor(qzero, qzero) -
1 / 2 * qt.tensor(plus, plus))
aliceEffects = [[qzero * qzero.dag(), qone * qone.dag()],
[plus * plus.dag(), minus * minus.dag()]]
bobEffects = [[qzero * qzero.dag(), qone * qone.dag()],
[plus * plus.dag(), minus * minus.dag()]]
bobPostMeasurmentStates = [[
1 / ((qt.tensor(effect, qt.qeye(2)) * psi * psi.dag()).tr()) *
(qt.tensor(effect, qt.qeye(2)) * psi).ptrace(1)
def create_data(time_per_count, num_samples, num_counts, gate_list, time_unit, noise_type=None, walking_amp=None, telegraph_amp=None, \
res=None, freq_list=None, amp_list=None, phase_list=None, start_f=None, stop_f=None, fluctuators=None, plot_noise=False, \
add_noise=False, noise_object=None, dc_angle_offset=0, constant_linear_drift=0):
#time_per_shot: time in seconds for a single (prep-gate-measure+delay)
#num_samples: how many timestamps and strings of counts you want to have
#num_counts: how many data points to create (how many ones and zeros) per sample (i.e. per timestamp) --> affects both time of a sample and precision
#num_shots: how many times (shots) you apply (prep-gate_list-meas) to get one count (a single 0 or 1) --> determines the time of one count, but won't affect precision
#gate_list: gates you want to do for your operation, entered as a list of strings
#xerr,yerr,zerr: 2D tuples with overrotation amplitude in radians and frequency in Hz
#constant linear drift: enter in rads/second
rho0 = _qt.operator_to_vector(_qt.ket2dm(_qt.basis(2,0)))
rho1 = _qt.operator_to_vector(_qt.ket2dm(_qt.basis(2,1)))
zero_counts = []
one_counts = []
timestep = num_counts*time_per_count #the time to get a full bitstring of zeros and ones for one sample, i.e. one timestamp
timestamps = np.arange(timestep, num_samples*timestep, timestep) #array of 1*timestep, 2*timestep,....(num_samples)*timestep
probs = []
total_time = (time_per_count*num_counts*num_samples)/time_unit
sig = 0
if noise_type == "Sine":
#this function returns the noise object so you can enter it back in as a parameter
# in the event that you call the function repeatedly for an identical set of parameters
if noise_object != None:
sig = noise_object
#print("REUSING NOISE OBJECT")
while total_time > sig.times[-1] + timestep:
sig.next_interval()
#print("Doing a NEXT INTERVAL")
else:
#print("INITIALIZING NEW NOISE")
sig = _ns.NoiseSignalSine(time_unit=time_unit)
sig.configure_noise(resolution_factor=res, freq_list=freq_list, amp_list=amp_list, phase_list=phase_list, total_time=total_time)
sig.init()
if add_noise != None:
sig.add_random_noise(add_noise) #add normal noise with specified std deviation if requested
elif noise_type == "Random Walk":
sig = _ns.NoiseSignalRandomWalk(initial_seed=1234, time_unit=time_unit)
sig.configure_noise(walking_amp, res, total_time)
sig.init()
elif noise_type == "Telegraph":
sig = _ns.NoiseSignalTelegraph(initial_seed=1234, time_unit=time_unit)
sig.configure_noise(exponent=1, amplitude=telegraph_amp, total_fluctuators=fluctuators, start_freq=start_f, stop_freq=stop_f, total_time=total_time)
sig.init()
sig.interpolation_settings(do_interpolation=True, resolution_factor=res)
if plot_noise==True:
sig.plot_noise_signal()
angle_list = []
expected_angle_list = []
compressed_gate_list = compress_gate_list(gate_list)
for time in timestamps:
noise_at_time = 0
if noise_type != None:
#print(time/time_unit)
noise_at_time = sig[time/time_unit]
rho = rho0
total_angle = 0
total_ideal_angle = 0
for gate in compressed_gate_list:
gate_name = gate[0]
gate_repetitions = gate[1]
'''
Next step: calculate change in rotation error within each shot. Currently takes the time at the start of the experiment shot
and applies that to all gates in one shot. Depending on the timescale of the error and time per shot, this simplification may need
to be addressed so that each gate, say each Gx in (Gx)^11, has an error associated with its specific time, not the same error for
all 11 Gx gates.
'''
if gate_name == 'Gx':
angle = (np.pi/2 + noise_at_time)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
ideal_angle = (np.pi/2)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
rho = (_qt.to_super(_qt.rx(angle))) * rho
#this section just keeps the angle between 0 and pi
angle = angle % (2*np.pi)
ideal_angle = ideal_angle % (2*np.pi)
if angle > np.pi:
angle = 2*np.pi - angle
if angle < 0:
angle = 0 + abs(angle)
if ideal_angle > np.pi:
ideal_angle = 2*np.pi - ideal_angle
if ideal_angle < 0:
ideal_angle = 0 + abs(ideal_angle)
elif gate_name == 'Gy':
angle = (np.pi/2 + noise_at_time)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
ideal_angle = (np.pi/2)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
rho = (_qt.to_super(_qt.rx(angle))) * rho
#this section just keeps the angle between 0 and pi
angle = angle % (2*np.pi)
ideal_angle = ideal_angle % (2*np.pi)
if angle > np.pi:
angle = 2*np.pi - angle
if angle < 0:
angle = 0 + abs(angle)
if ideal_angle > np.pi:
ideal_angle = 2*np.pi - ideal_angle
if ideal_angle < 0:
ideal_angle = 0 + abs(ideal_angle)
elif gate_name == 'Gz':
angle = (np.pi/2 + noise_at_time)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
ideal_angle = (np.pi/2)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
rho = (_qt.to_super(_qt.rx(angle))) * rho
#this section just keeps the angle between 0 and pi
angle = angle % (2*np.pi)
ideal_angle = ideal_angle % (2*np.pi)
if angle > np.pi:
angle = 2*np.pi - angle
if angle < 0:
angle = 0 + abs(angle)
if ideal_angle > np.pi:
ideal_angle = 2*np.pi - ideal_angle
if ideal_angle < 0:
ideal_angle = 0 + abs(ideal_angle)
elif gate_name == "Gi":
#apply only the oscillating drift angle, don't add it to pi/2
angle = (noise_at_time)*gate_repetitions + dc_angle_offset + constant_linear_drift*time
ideal_angle = 0 + dc_angle_offset + constant_linear_drift*time
rho = (_qt.to_super(_qt.rx(angle))) * rho
angle = angle % (2*np.pi)
ideal_angle = ideal_angle % (2*np.pi)
if angle > np.pi:
angle = 2*np.pi - angle
if angle < 0:
angle = 0 + abs(angle)
if ideal_angle > np.pi:
ideal_angle = 2*np.pi - ideal_angle
if ideal_angle < 0:
ideal_angle = 0 + abs(ideal_angle)
total_angle += angle
total_ideal_angle += ideal_angle
#append the total rotation angle after timestamp 'time'
angle_list.append(total_angle)
expected_angle_list.append(total_ideal_angle)
#calculate probabilities of being in 1 after the experiment has been applied
p1 = (rho.dag()*rho1).norm()
#fix the p1 if it exceeds 1 due to rounding error
if p1 > 1:
#print("p1 exceeds 1 for time {}".format(time))
#print("prob is {}".format(p1))
#print("Resetting to {}".format(2-p1))
p1 = 2 - p1
probs.append(p1)
one_count = np.random.binomial(num_counts, p1) #simulates a summation of the number of 1-counts you get in one bitstring sample
zero_count = num_counts - one_count #simulates summation of the number of 0-counts in one bitstring sample
one_counts.append(one_count)
zero_counts.append(zero_count)
if plot_noise == True:
plt.plot(timestamps, np.asarray(expected_angle_list),label="Ideal Angle",ls='dashed',color='orange')
plt.plot(timestamps, np.asarray(angle_list), label="Drifting Angle")
plt.legend()
plt.xlabel("Time, seconds")
plt.yticks(np.linspace(0, np.pi, 5), ['0', '$\pi/4$', '$\pi/2$', '$3\pi/4$', '$\pi$'])
plt.ylabel("Angle, radians\n(Displayed between 0 and $\pi$)")
plt.title("Time-Dependent Rotation Angle after each {}".format(gate_list_to_string(gate_list)))
plt.grid()
plt.show()
plt.plot(timestamps, probs)
plt.ylim(0,1)
plt.xlabel("Time, seconds")
plt.ylabel("Probability of Measuring State {1}")
plt.title("Simulated {} with {} Noise".format(gate_list_to_string(gate_list), noise_type))
plt.grid()
plt.show()
return (np.asarray(one_counts), np.asarray(zero_counts), np.asarray(timestamps), probs,np.asarray(expected_angle_list), np.asarray(angle_list), sig)
def get_average_gate_fidelity(self, runs=500, target=None):
"""
returns average gate fidelity
Args
runs (int) : number of runs to compute the average gate fidelity
target (4x4 numpy array) : target unitary to compute fidelity
"""
self.solver_obj.calculate_evolution(self.init, self.total_time,
10000, 1)
U_ideal = self.solver_obj.get_unitary()[0]
self.solver_obj.add_noise_static(2 * np.pi * self.H_zeeman,
18 * 1e-6)
self.solver_obj.add_noise_generic(2 * np.pi * self.H_heisenberg,
oneoverfnoise,
self.exchange_dc / 100)
self.solver_obj.calculate_evolution(self.init, self.total_time,
10000, int(runs))
U_list = self.solver_obj.get_unitary()
# Calculate the averaged super operator in the Lioville superoperator form using column convention
basis = [qt.basis(4, it) for it in range(4)]
superoperator_basis = [
basis_it1 * basis_it2.dag() for basis_it2 in basis
for basis_it1 in basis
]
averaged_map = np.zeros([16, 16], dtype=np.complex)
for u in U_list:
temp_U = qt.Qobj(u)
output_density = list()
for it in range(len(superoperator_basis)):
temp_vec = np.array(
qt.operator_to_vector(
temp_U * superoperator_basis[it] * temp_U.dag() /
float(runs)).full()).flatten()
output_density.append(np.array(temp_vec))
averaged_map = np.add(averaged_map, np.array(output_density))
# Define the target unitary operation
if target is None:
target = sp.linalg.expm(
-np.pi / 2. * 1j *
sp.sparse.diags([0., -1., -1., 0.]).todense())
# get phase from optimizing noiseless unitary evolution
def to_minimize_fidelity(theta):
temp_z_gate = np.matmul(
sp.linalg.expm(-2 * np.pi * 1j * theta * self.H_zeeman),
U_ideal)
temp_m = np.matmul(sp.conjugate(sp.transpose(target)),
temp_z_gate)
return np.real(1. - (sp.trace(
np.matmul(temp_m, sp.conjugate(sp.transpose(temp_m)))) +
np.abs(sp.trace(temp_m))**2.) / 20.)
ideal_phase = sp.optimize.minimize(
to_minimize_fidelity,
[self.delta_z * (self.total_time * 1e-9)],
method='Nelder-Mead',
tol=1e-6).x[0]
target = np.matmul(
sp.linalg.expm(2 * np.pi * 1j * ideal_phase * self.H_zeeman),
target)
# Change the shape of the averaged super operator to match the definitions used in QuTip (row convention)
target = qt.Qobj(target)
averaged_map = qt.Qobj(averaged_map).trans()
averaged_map._type = 'super'
averaged_map.dims = [[[4], [4]], [[4], [4]]]
averaged_map.superrep = qt.to_super(target).superrep
# Calculate the average gate fidelity of the superoperator with the target unitary gate
fidelity = qt.average_gate_fidelity(averaged_map, target)
return fidelity
def basic_spin_states():
'''define some basic spin states'''
ket0 = qutip.basis(2, 0)
bra0 = qutip.basis(2, 0).dag()
ket1 = qutip.basis(2, 1)
bra1 = qutip.basis(2, 1).dag()
rho0 = ket0 * bra0
rho1 = ket1 * bra1
rhom = (rho0 + rho1) / 2
ketx = 1 / np.sqrt(2) * (qutip.basis(2, 0) + qutip.basis(2, 1))
brax = 1 / np.sqrt(2) * (qutip.basis(2, 0).dag() + qutip.basis(2, 1).dag())
ketmx = 1 / np.sqrt(2) * (qutip.basis(2, 0) - qutip.basis(2, 1))
bramx = 1 / np.sqrt(2) * (qutip.basis(2, 0).dag() -
qutip.basis(2, 1).dag())
kety = 1 / np.sqrt(2) * (qutip.basis(2, 0) + 1j * qutip.basis(2, 1))
bray = kety.dag()
ketmy = 1 / np.sqrt(2) * (qutip.basis(2, 0) - 1j * qutip.basis(2, 1))
bramy = ketmy.dag()
rhox = ketx * brax
rhomx = ketmx * bramx
rhoy = kety * bray
rhomy = ketmy * bramy
return ket0, bra0, ket1, bra1, rho0, rho1, rhom, ketx, brax, ketmx, bramx, rhox, rhomx, kety, bray, ketmy, bramy, rhoy, rhomy
def prep_Z_qutip():
q = qutip.basis(2)
Z = qutip.sigmaz()
return Z * q
L0 = liouvillian(H0, [np.sqrt(gamma) * Sm])
#sigma X control
LC_x = liouvillian(Sx)
#sigma Y control
LC_y = liouvillian(Sy)
#sigma Z control
LC_z = liouvillian(Sz)
E0 = sprepost(Si, Si)
# target map 1
# E_targ = sprepost(had_gate, had_gate)
# target map 2
E_targ = sprepost(Sx, Sx)
psi0 = basis(2, 1) # ground state
#psi0 = basis(2, 0) # excited state
rho0 = ket2dm(psi0)
print("rho0:\n{}\n".format(rho0))
rho0_vec = operator_to_vector(rho0)
print("rho0_vec:\n{}\n".format(rho0_vec))
# target state 1
# psi_targ = (basis(2, 0) + basis(2, 1)).unit()
# target state 2
#psi_targ = basis(2, 1) # ground state
psi_targ = basis(2, 0) # excited state
#psi_targ = psi0
rho_targ = ket2dm(psi_targ)
print("rho_targ:\n{}\n".format(rho_targ))
def prep_Y_qutip():
q = qutip.basis(2)
Y = qutip.sigmay()
return Y * q
else:
all_seeds_alpha.append(data['alpha'][:N_iter][epochs])
all_seeds_theta.append(data['theta'][:N_iter][epochs])
all_seeds_cost.append(data['cost'][:N_iter][epochs])
all_seeds_alpha = np.array(all_seeds_alpha)
all_seeds_theta = np.array(all_seeds_theta)
all_seeds_cost = np.array(all_seeds_cost)
batch_size = 1
T = 5
N = 100
target_state = qt.basis(N,fock)
reward_kwargs = {'reward_mode' : 'overlap',
'target_state' : target_state,
'postselect_0' : False}
env = env_init(control_circuit='snap_and_displacement', reward_kwargs=reward_kwargs,
init='vac', T=T, batch_size=batch_size, N=N, episode_length=T)
def avg_reward(action_script):
policy = plc.ScriptedPolicy(env.time_step_spec(), action_script)
time_step = env.reset()
policy_state = policy.get_initial_state(env.batch_size)
while not time_step.is_last()[0]:
action_step = policy.action(time_step, policy_state)
# helper functions
from helpers import are_close
# functions that generate our circuit and time evolutions
from teleportation import continuousTeleportationSimulation
from teleportation import continuousXXBraidingCorrectionSimulation
from teleportation import continuousZBraidingCorrectionSimulation
from teleportation import continuousDecodingSimulation
from teleportation import circuitTeleportationSimulation
from teleportation import circuitXXBraidingCorrectionSimulation
from teleportation import circuitZBraidingCorrectionSimulation
from teleportation import circuitDecodingSimulation
from teleportation import simulateTeleportation
z0, z1, xp, xm, yp, ym = bloch(basis(2, 0), basis(2, 1))
class TestCZandH(object):
def testHadamardUnitary(self):
H = constructHadamardH(1, [0])
U = snot()
U2 = deriveUnitary(H, np.pi)
Ucorr = constructHadamardCorr(1, [0])
assert Ucorr*U2 == U
assert H.isherm
def testHadamardUnitaryMulti(self):
H = constructHadamardH(2, [0, 1])
UH1 = snot(N=2, target=0)
UH2 = snot(N=2, target=1)
def test_wstate(self):
filename = "w-state.qasm"
filepath = Path(__file__).parent / 'qasm_files' / filename
qc = read_qasm(filepath)
rand_state = rand_ket(2)
wstate = (tensor(basis(2, 0), basis(2, 0), basis(2, 1))
+ tensor(basis(2, 0), basis(2, 1), basis(2, 0))
+ tensor(basis(2, 1), basis(2, 0), basis(2, 0))).unit()
state = tensor(tensor(basis(2, 0), basis(2, 0), basis(2, 0)),
rand_state)
fourth = Measurement("test_rand", targets=[3])
_, probs_initial = fourth.measurement_comp_basis(state)
simulators = _simulators_sv(qc)
for simulator in simulators:
result = simulator.run_statistics(state)
final_states = result.get_final_states()
result_cbits = result.get_cbits()
for i, final_state in enumerate(final_states):
_, probs_final = fourth.measurement_comp_basis(final_state)
np.testing.assert_allclose(probs_initial, probs_final)
assert sum(result_cbits[i]) == 1
def fock_state_on(d):
return qutip.tensor(*[
qutip.basis(fock_trunc, d.get(i, 0)) for i in range(N)
]) # give me the value d[i] or 0 if d[i] does not exist
# Declare Channels
seq.declare_channel('ryd', 'rydberg_local', 'control1')
seq.declare_channel('raman', 'raman_local', 'control1')
d = 0 # Pulse Duration
# Prepare state 'hhh':
seq.add(pi_Y, 'raman')
seq.target('target', 'raman')
seq.add(pi_Y, 'raman')
seq.target('control2', 'raman')
seq.add(pi_Y, 'raman')
d += 3
prep_state = qutip.tensor([qutip.basis(3, 2) for _ in range(3)])
# Write CCZ sequence:
seq.add(pi, 'ryd', protocol='wait-for-all')
seq.target('control2', 'ryd')
seq.add(pi, 'ryd')
seq.target('target', 'ryd')
seq.add(twopi, 'ryd')
seq.target('control2', 'ryd')
seq.add(pi, 'ryd')
seq.target('control1', 'ryd')
seq.add(pi, 'ryd')
d += 5
def test_initialization_and_construction_of_hamiltonian():
def prep_X_qutip():
q = qutip.basis(2)
X = qutip.sigmax()
return X * q
def prep_T_qutip(amount):
q = qutip.basis(2)
T = qutip.Qobj([[1, 0], [0, np.exp(amount * np.pi / 4)]], dims=[[2], [2]])
return T * q
def test_building_basis_and_projection_operators():
# All three levels:
sim = Simulation(seq, sampling_rate=0.01)
assert sim.basis_name == 'all'
assert sim.dim == 3
assert sim.basis == {
'r': qutip.basis(3, 0),
'g': qutip.basis(3, 1),
'h': qutip.basis(3, 2)
}
assert (sim.op_matrix['sigma_rr'] == qutip.basis(3, 0) *
qutip.basis(3, 0).dag())
assert (sim.op_matrix['sigma_gr'] == qutip.basis(3, 1) *
qutip.basis(3, 0).dag())
assert (sim.op_matrix['sigma_hg'] == qutip.basis(3, 2) *
qutip.basis(3, 1).dag())
# Check local operator building method:
with pytest.raises(ValueError, match="Duplicate atom"):
sim._build_operator('sigma_gg', "target", "target")
# Global ground-rydberg
seq2 = Sequence(reg, Chadoq2)
seq2.declare_channel('global', 'rydberg_global')
seq2.add(pi, 'global')
sim2 = Simulation(seq2, sampling_rate=0.01)
assert sim2.basis_name == 'ground-rydberg'
assert sim2.dim == 2
assert sim2.basis == {'r': qutip.basis(2, 0), 'g': qutip.basis(2, 1)}
assert (sim2.op_matrix['sigma_rr'] == qutip.basis(2, 0) *
qutip.basis(2, 0).dag())
assert (sim2.op_matrix['sigma_gr'] == qutip.basis(2, 1) *
qutip.basis(2, 0).dag())
# Digital
seq2b = Sequence(reg, Chadoq2)
seq2b.declare_channel('local', 'raman_local', 'target')
seq2b.add(pi, 'local')
sim2b = Simulation(seq2b, sampling_rate=0.01)
assert sim2b.basis_name == 'digital'
assert sim2b.dim == 2
assert sim2b.basis == {'g': qutip.basis(2, 0), 'h': qutip.basis(2, 1)}
assert (sim2b.op_matrix['sigma_gg'] == qutip.basis(2, 0) *
qutip.basis(2, 0).dag())
assert (sim2b.op_matrix['sigma_hg'] == qutip.basis(2, 1) *
qutip.basis(2, 0).dag())
# Local ground-rydberg
seq2c = Sequence(reg, Chadoq2)
seq2c.declare_channel('local_ryd', 'rydberg_local', 'target')
seq2c.add(pi, 'local_ryd')
sim2c = Simulation(seq2c, sampling_rate=0.01)
assert sim2c.basis_name == 'ground-rydberg'
assert sim2c.dim == 2
assert sim2c.basis == {'r': qutip.basis(2, 0), 'g': qutip.basis(2, 1)}
assert (sim2c.op_matrix['sigma_rr'] == qutip.basis(2, 0) *
qutip.basis(2, 0).dag())
assert (sim2c.op_matrix['sigma_gr'] == qutip.basis(2, 1) *
qutip.basis(2, 0).dag())
Created on Mon Apr 1 14:48:35 2019
@author: Eduardo Villasenor
"""
import qutip as qt
import matplotlib.pyplot as plt
import numpy as np
r = 3
print("Mean photon number:", np.sinh(r)**2)
# N = 10
N = 10
a = qt.basis(N, 0)
Sa = qt.squeeze(N, r)
# N = 20
N = 20
b = qt.basis(N, 0)
Sb = qt.squeeze(N, r)
# N = 30
N = 50
c = qt.basis(N, 0)
Sc = qt.squeeze(N, r)
fig, axes = plt.subplots(1, 3, figsize=(12, 3))
qt.plot_fock_distribution(Sa * a,
fig=fig,
noise_operator_meas_op = unitary_channel(4, 0.8, 0.01)
noise_operator_state_prep.dims = [[[2, 2], [2, 2]], [[2, 2], [2, 2]]]
noise_operator_meas_op.dims = [[[2, 2], [2, 2]], [[2, 2], [2, 2]]]
# Set the number of runs
number_of_seq = 5000
seq_length = 25
# Set observable Q
Q = qt.tensor(qt.sigmay() - qt.sigmaz(), qt.sigmay() - qt.sigmaz())
Q.dims = [[2, 2], [2, 2]]
Q = qt.operator_to_vector(Q)
Q = noise_operator_meas_op * Q
# Set initial state
rho = qt.basis(4, 0) * qt.basis(4, 0).dag()
rho.dims = [[2, 2], [2, 2]]
rho = qt.operator_to_vector(rho)
rho = noise_operator_state_prep * rho
def fixed_func(m, c0, c1, c2, c3, e1):
return (c0 + c1 * e1**(m - 1.) + c2 *
(sub_unitarity_A_to_A(two_qubit_noise))**(m - 1.) + c3 *
(sub_unitarity_B_to_B(two_qubit_noise))**(m - 1.))
# Run!
reference_standard_data = protocol(noisy_cliffords, rho, Q, seq_length,
number_of_seq)
reference_optimal_standard = opt.curve_fit(fixed_func,
np.arange(1, seq_length),
def basis(self, *ns):
return q.tensor(
*[q.basis(N, n) for N, n in zip(self.modes_levels, ns)])
spin_n = 2
clock_n = 7
################################################################################
spin_initial = qt.rand_ket(spin_n)
spin_H = qt.rand_herm(spin_n)
dt = (2 * np.pi) / clock_n
spin_U = (-1j * spin_H * dt).expm()
psi_t = lambda t: (-1j * spin_H * t).expm() * spin_initial
spin_history = [psi_t(t * dt) for t in range(clock_n)]
################################################################################
H = sum([-qt.tensor(spin_U, qt.basis(clock_n, t+1)*qt.basis(clock_n, t).dag())\
-qt.tensor(spin_U.dag(), qt.basis(clock_n, t)*qt.basis(clock_n, t+1).dag())\
+qt.tensor(qt.identity(spin_n), qt.basis(clock_n, t)*qt.basis(clock_n, t).dag())\
+qt.tensor(qt.identity(spin_n), qt.basis(clock_n, t+1)*qt.basis(clock_n, t+1).dag())\
+qt.tensor(qt.identity(spin_n) - spin_initial*spin_initial.dag(), qt.basis(clock_n, 0)*qt.basis(clock_n,0).dag())\
for t in range(clock_n-1)])
HL, HV = H.eigenstates()
H_projs = [v * v.dag() for v in HV]
Z = qt.sigmaz()
ZL, ZV = Z.eigenstates()
Z_projs = [qt.tensor(v * v.dag(), qt.identity(clock_n)) for v in ZV]
TIME_projs = [
qt.basis(clock_n, i) * qt.basis(clock_n, i).dag() for i in range(clock_n)
]
def prep_I_qutip():
q = qutip.basis(2)
return q
def basis(self, n):
return q.basis(self.levels, n)
def plot_expect(self):
XI = np.array(
list(
qt.basis(4, 1) * qt.basis(4, 3).dag() +
qt.basis(4, 0) * qt.basis(4, 2).dag() +
qt.basis(4, 2) * qt.basis(4, 0).dag() +
qt.basis(4, 3) * qt.basis(4, 1).dag()))[:, 0]
IX = np.array(
list(
qt.basis(4, 0) * qt.basis(4, 1).dag() +
qt.basis(4, 1) * qt.basis(4, 0).dag() +
qt.basis(4, 2) * qt.basis(4, 3).dag() +
qt.basis(4, 3) * qt.basis(4, 2).dag()))[:, 0]
XX = np.array(
list(
qt.basis(4, 0) * qt.basis(4, 3).dag() +
qt.basis(4, 1) * qt.basis(4, 2).dag() +
qt.basis(4, 2) * qt.basis(4, 1).dag() +
qt.basis(4, 3) * qt.basis(4, 0).dag()))[:, 0]
ZZ = np.array(
list(
qt.basis(4, 0) * qt.basis(4, 0).dag() -
qt.basis(4, 1) * qt.basis(4, 1).dag() -
qt.basis(4, 2) * qt.basis(4, 2).dag() +
qt.basis(4, 3) * qt.basis(4, 3).dag()))[:, 0]
ZI = np.array(
list(
qt.basis(4, 0) * qt.basis(4, 0).dag() +
qt.basis(4, 1) * qt.basis(4, 1).dag() -
qt.basis(4, 2) * qt.basis(4, 2).dag() -
qt.basis(4, 3) * qt.basis(4, 3).dag()))[:, 0]
IZ = np.array(
list(
qt.basis(4, 0) * qt.basis(4, 0).dag() -
qt.basis(4, 1) * qt.basis(4, 1).dag() +
qt.basis(4, 2) * qt.basis(4, 2).dag() -
qt.basis(4, 3) * qt.basis(4, 3).dag()))[:, 0]
YY = qt.tensor(qt.sigmay(), qt.sigmay())[:, :]
operators = np.array([ZI, IZ, ZZ, XI, IX, XX, YY], dtype=complex)
label = ["ZI", "IZ", "ZZ", "XI", "IX", "XX", "YY"]
self.solver_obj.plot_expectation(operators, label, 2)
def get_expect(self):
XI = np.array(
list(
qt.basis(4, 1) * qt.basis(4, 3).dag() +
qt.basis(4, 0) * qt.basis(4, 2).dag() +
qt.basis(4, 2) * qt.basis(4, 0).dag() +
qt.basis(4, 3) * qt.basis(4, 1).dag()))[:, 0]
IX = np.array(
list(
qt.basis(4, 0) * qt.basis(4, 1).dag() +
qt.basis(4, 1) * qt.basis(4, 0).dag() +
qt.basis(4, 2) * qt.basis(4, 3).dag() +
qt.basis(4, 3) * qt.basis(4, 2).dag()))[:, 0]
return self.solver_obj.return_expectation_values(
np.array([XI, IX], dtype=complex))
#For anaharmonicities
N=50
omega=1
Xbb=0.09
g=0.2*omega
F=0.1*omega
HA=omega/2*(-qt.sigmaz()+qt.qeye(2))
HB=omega*qt.create(N)*qt.destroy(N)- Xbb/2*(qt.create(N)*qt.destroy(N)*qt.create(N)*qt.destroy(N))
HAB=g*(qt.tensor(qt.create(2),qt.destroy(N))+qt.tensor(qt.destroy(2), qt.create(N)))
H1=qt.tensor(F*qt.create(2),qt.qeye(N))
H2=qt.tensor(F*qt.destroy(2),qt.qeye(N))
H0=qt.tensor(HA, qt.qeye(N))+qt.tensor(qt.qeye(2),HB)+HAB
H=[H0, [H1, H1_coeff], [H2, H2_coeff]]
t=np.linspace(0,30*math.pi/g,10000)
psi0=qt.basis(2*N,0)
output = qt.mesolve(H, psi0, t, [qt.tensor(np.sqrt(0.05*omega)*qt.destroy(2),qt.qeye(N))], [])
Energy=np.zeros(10000)
Ergotropy=np.zeros(10000)
v=eigenvectors(HB)
EntropyB=np.zeros(10000)
for i in range(0,10000):
A=np.array(output.states[i])
FinalRho=np.trace(A.reshape(2,N,2,N), axis1=0, axis2=2)
Rho_f=np.zeros((N,N))
for j in range(0,N):
Rho_f=eigenvalues(FinalRho)[N-1-j]*v[:,j]+Rho_f
Energy[i-1]=np.real(np.matrix.trace(omega*np.dot(np.array(HB),FinalRho)))
Ergotropy[i-1]=-np.real(np.matrix.trace(omega*np.dot(np.array(HB),(Rho_f-FinalRho))))
EntropyB[i-1]=qt.entropy_vn(qt.Qobj(FinalRho),2)
'control_circuit': 'ECD_control_remote',
'init': 'vac',
'T': 8,
'N': 100
}
# Evaluation environment params
eval_env_kwargs = {
'control_circuit': 'ECD_control_remote',
'init': 'vac',
'T': 8,
'N': 100
}
# Params for reward function
target_state = qt.tensor(qt.basis(2, 0), qt.basis(100, 4))
reward_kwargs = {
'reward_mode': 'tomography_remote',
'tomography': 'wigner',
'target_state': target_state,
'window_size': 16,
'server_socket': server_socket,
'amplitude_type': 'displacement',
'epoch_type': 'training',
'N_alpha': 100,
'N_msmt': 10,
'sampling_type': 'abs'
}
reward_kwargs_eval = {
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import qutip as qt
import tensorflow as tf
import numpy as np
from math import sqrt, pi
from gkp.agents import PPO
from tf_agents.networks import actor_distribution_network
from gkp.agents import actor_distribution_network_gkp
from gkp.gkp_tf_env import helper_functions as hf
root_dir = r'E:\VladGoogleDrive\Qulab\GKP\sims\PPO\tomography_reward'
root_dir = os.path.join(root_dir, 'fock2_beta3_N40_B100_tomo10_lr3e-4_avg')
N = 40
target_state = qt.tensor(qt.basis(2, 0), qt.basis(N, 2))
reward_kwargs = {
'reward_mode': 'tomography',
'target_state': target_state,
'window_size': 12
}
kwargs = {'N': N}
PPO.train_eval(
root_dir=root_dir,
random_seed=0,
# Params for collect
num_iterations=100000,
train_batch_size=100,
replay_buffer_capacity=15000,
#math related packages
import scipy
import scipy as sc
from scipy import stats
import qutip as qt
#further packages
from time import time
from random import sample
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import random
#ket states
qubit0 = qt.basis(2, 0)
qubit1 = qt.basis(2, 1)
#density matrices
qubit0mat = qubit0 * qubit0.dag()
qubit1mat = qubit1 * qubit1.dag()
def partialTraceRem(obj, rem):
#prepare keep list
rem.sort(reverse=True)
keep = list(range(len(obj.dims[0])))
for x in rem:
keep.pop(x)
res = obj
#return partial trace:
if len(keep) != len(obj.dims[0]):
def prep_H_qutip():
q = qutip.basis(2)
H = 1 / np.sqrt(2) * (qutip.sigmax() + qutip.sigmaz())
return H * q