def test_cont_evo_complete(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1j*1./sqrt(3)]]),1./4.,-1./2.) L0 = np.matrix([[0.,0.],[1.,0.]]) L1 = np.matrix([[0.,1.],[0.,0.]]) clas_pos_derivs = lambda q,p : q clas_mom_derivs = lambda q,p : p delta_time = 2. tau = 4. QHamlitonian = lambda q,p : q * np.matrix([[0.,1.],[1.,0.]]) test_cont = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, clas_pos_derivs, clas_mom_derivs, tau, delta_time, 0, 0, 0) test_cont._evolution_one_step(-1, 1.) obt_state = test_cont.CQstate.state obt_position = test_cont.CQstate.position obt_momentum = test_cont.CQstate.momentum obt_time = test_cont.CQstate.time unnorm_exp_state = np.matrix([[(sqrt(3)/sqrt(2) + 1./sqrt(3)) / 2.] ,[1j * (sqrt(3)/4. - 1/sqrt(6)) ]]) exp_state = unnorm_exp_state/np.linalg.norm(unnorm_exp_state) exp_position = -3./4. exp_momentum = -1. exp_time = 2. np.testing.assert_almost_equal( obt_state, exp_state ) np.testing.assert_almost_equal( obt_position, exp_position ) np.testing.assert_almost_equal( obt_momentum, exp_momentum ) np.testing.assert_almost_equal( obt_time, exp_time )
def test_jump_evo_L1(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1j*1./sqrt(3)]]),1./4.,2./3.) L0 = np.matrix([[0.,0.],[1.,0.]]) L1 = np.matrix([[0.,1.],[0.,0.]]) delta_time = 2. tau = 4. pos_derivs = [lambda q,p : - 1./3., lambda q,p : 1./6.] mom_derivs = [lambda q,p : 1./8., lambda q,p : - 1./4.] QHamlitonian = lambda q,p : q * np.matrix([[0.,1.],[1.,0.]]) test_cont = lib.Unravelling(CQstate, [L0,L1], pos_derivs, mom_derivs, QHamlitonian, 0, 0, tau, delta_time, 0, 0, 0) test_cont._evolution_one_step(1, 1.) obt_state = test_cont.CQstate.state obt_position = test_cont.CQstate.position obt_momentum = test_cont.CQstate.momentum obt_time = test_cont.CQstate.time exp_state = np.matrix([[1j*1./sqrt(3)],[0.]]) exp_position = - 3./4. exp_momentum = 0. exp_time = 2. np.testing.assert_almost_equal( obt_state, exp_state ) np.testing.assert_almost_equal( obt_position, exp_position ) np.testing.assert_almost_equal( obt_momentum, exp_momentum ) np.testing.assert_almost_equal( obt_time, exp_time )
def test_cont_evo_non_diagonal_Hamiltonian_only(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1j*1./sqrt(3)]]),1./2.,1.) L0 = np.matrix([[0.,0.],[0.,0.]]) # set L0 to be zero everywhere so we can check contribution of H only clas_pos_derivs = lambda q,p : q clas_mom_derivs = lambda q,p : p delta_time = 2. tau = 8. QHamlitonian = lambda q,p : q * np.matrix([[0.,1.],[1.,0.]]) test_cont = lib.Unravelling(CQstate, [L0], 0, 0, QHamlitonian, clas_pos_derivs, clas_mom_derivs, tau, delta_time, 0, 0, 0) test_cont._evolution_one_step(-1, 1.) obt_state = test_cont.CQstate.state obt_position = test_cont.CQstate.position obt_momentum = test_cont.CQstate.momentum obt_time = test_cont.CQstate.time exp_state = 1./sqrt(2.) * np.matrix([[(1. + sqrt(2))/sqrt(3)] ,[1j * (1. - sqrt(2))/sqrt(3)]]) exp_position = 5./2. exp_momentum = 0. exp_time = 2. np.testing.assert_almost_equal( obt_state, exp_state ) np.testing.assert_almost_equal( obt_position, exp_position ) np.testing.assert_almost_equal( obt_momentum, exp_momentum ) np.testing.assert_almost_equal( obt_time, exp_time )
def test_cont_evo_Lindblad_only(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1j*1./sqrt(3)]]),0,0) L0 = np.matrix([[0.,0.],[1.,0.]]) L1 = np.matrix([[0.,1.],[0.,0.]]) clas_pos_derivs = lambda q,p : q clas_mom_derivs = lambda q,p : p delta_time = 2. tau = 8. QHamlitonian = lambda q,p : q * np.matrix([[0.,1.],[1.,0.]]) test_cont = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, clas_pos_derivs, clas_mom_derivs, tau, delta_time, 0, 0, 0) test_cont._evolution_one_step(-1, 1.) obt_state = test_cont.CQstate.state obt_position = test_cont.CQstate.position obt_momentum = test_cont.CQstate.momentum obt_time = test_cont.CQstate.time exp_state = np.matrix([[sqrt(2)/sqrt(3)],[1j*1./sqrt(3)]]) exp_position = 0. exp_momentum = 0. exp_time = 2. np.testing.assert_almost_equal( obt_state, exp_state ) np.testing.assert_almost_equal( obt_position, exp_position ) np.testing.assert_almost_equal( obt_momentum, exp_momentum ) np.testing.assert_almost_equal( obt_time, exp_time )
def test_choose_evo_jump_0(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1./sqrt(3)]]),0,0) L0 = np.matrix([[0.,0.],[1.,0.]]) L1 = np.matrix([[0.,1.],[0.,0.]]) delta_time = 1. tau = 2. seed = 10 rn.seed(seed) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) test_evo = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, tau, delta_time, 0, seed, 0) obt_type, obt_norm = test_evo._choose_evolution() obt_randomness_first = test_evo.random_list[0] obt_randomness_second = test_evo.random_list[1] exp_type = 0 exp_norm = 2./3. exp_randomness_first = 0.5714025946899135 exp_randomness_second = 0.4288890546751146 assert_equal( obt_type, exp_type, "Different type returned." ) np.testing.assert_almost_equal( obt_norm, exp_norm ) np.testing.assert_almost_equal( obt_randomness_first, exp_randomness_first ) np.testing.assert_almost_equal( obt_randomness_second, exp_randomness_second )
def test_choose_evo_jump_1(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1./sqrt(3)]]),0,0) L0 = np.matrix([[0.,0.],[1.,0.]]) L1 = np.matrix([[0.,1.],[0.,0.]]) delta_time = 1. tau = 2. seed = 2 rn.seed(seed) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) test_evo = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, tau, delta_time, 0, seed, 0) obt_type, obt_norm = test_evo._choose_evolution() obt_randomness_first = test_evo.random_list[0] obt_randomness_second = test_evo.random_list[1] exp_type = 1 exp_norm = 1./3. exp_randomness_first = 0.9560342718892494 exp_randomness_second = 0.9478274870593494 assert_equal( obt_type, exp_type, "Different type returned." ) np.testing.assert_almost_equal( obt_norm, exp_norm ) np.testing.assert_almost_equal( obt_randomness_first, exp_randomness_first ) np.testing.assert_almost_equal( obt_randomness_second, exp_randomness_second )
def test_inconsistent_state_lindblad(): CQstate = lib.CQState(np.matrix([[1.],[1.],[1.]]),0,0) L0 = np.matrix([[0.,1.],[0.,0.]]) L1 = np.matrix([[0.,0.],[0.,1.]]) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) with assert_raises(RuntimeError): lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, 0, 0, 0, 0, 0)
def test_non_squared_lindblad(): CQstate = lib.CQState(np.matrix([[1.],[1.]]),0,0) L0 = np.matrix([[0.,1.,0.],[0.,0.,3.]]) L1 = np.matrix([[0.,0.,0.],[2.,0.,1.]]) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) with assert_raises(TypeError): lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, 0, 0, 0, 0, 0)
def test_different_shape(): CQstate = lib.CQState(np.matrix([[1.],[1.]]),0,0) L0 = np.matrix([[0.,1.],[0.,0.]]) L1 = np.matrix([[0.,0.,0.],[2.,0.,1.]]) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) with assert_raises(TypeError): lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, 0, 0, 0, 0, 0)
def test_non_self_adjoint_hamiltonian(): CQstate = lib.CQState(np.matrix([[1.],[1.]]),0,0) L0 = np.matrix([[0.,1.],[0.,0.]]) L1 = np.matrix([[0.,0.],[0.,1.]]) QHamlitonian = lambda q,p : q * np.matrix([[1.,1j],[-2*1j,-3.]]) with assert_raises(TypeError): lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, 0, 0, 0, 0, 0)
def test_probability_continuous(): CQstate = lib.CQState(np.matrix([[1/sqrt(2)],[1j/sqrt(2)]]),0,0) L = np.matrix([[0.,1.],[0.,0.]]) delta_time = 4. tau = 3. QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) test_prob = lib.Unravelling(CQstate, [L], 0, 0, QHamlitonian, 0, 0, tau, delta_time, 0, 0, 0) obt_prob = test_prob._probability_continuous() exp_prob = 1./3. np.testing.assert_almost_equal(obt_prob,exp_prob)
def test_probability_jump(): CQstate = lib.CQState(np.matrix([[1/sqrt(3)],[1j*sqrt(2)/sqrt(3)]]),0,0) L0 = np.matrix([[0.,1.],[0.,0.]]) L1 = np.matrix([[1j,0.],[0.,0.]]) delta_time = 4. tau = 3. QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) test_prob = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, tau, delta_time, 0, 0, 0) obt_prob1 = test_prob._probability_jump(0) exp_prob1 = 2./3. obt_prob2 = test_prob._probability_jump(1) exp_prob2 = 1./3. np.testing.assert_almost_equal(obt_prob1,exp_prob1) np.testing.assert_almost_equal(obt_prob2,exp_prob2)
def test_sum_lindblad(): CQstate = lib.CQState(np.matrix([[1.],[1.]]),0,0) L0 = np.matrix([[0.,1.],[0.,0.]]) L1 = np.matrix([[0.,0.],[1j,0.]]) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) test_lindblad = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, 0, 0, 0, 0, 0) obt1 = test_lindblad.double_lindblad_ops[0] obt2 = test_lindblad.double_lindblad_ops[1] obt3 = test_lindblad.sum_lindblad_ops exp1 = np.matrix([[0.,0.],[0.,1.]]) exp2 = np.matrix([[1.,0.],[0.,0.]]) exp3 = np.matrix([[1.,0.],[0.,1.]]) np.testing.assert_allclose( np.asarray(obt1), np.asarray(exp1) ) np.testing.assert_allclose( np.asarray(obt2), np.asarray(exp2) ) np.testing.assert_allclose( np.asarray(obt3), np.asarray(exp3) )
def test_choose_evo_continuous(): CQstate = lib.CQState(np.matrix([[1./sqrt(2)],[1./sqrt(2)]]),0,0) L0 = np.matrix([[0.,0.],[1.,0.]]) L1 = np.matrix([[0.,1.],[0.,0.]]) delta_time = 1. tau = 2. seed = 1 rn.seed(seed) QHamlitonian = lambda q,p : q * np.matrix([[1.,0.],[0.,-1.]]) test_evo = lib.Unravelling(CQstate, [L0,L1], 0, 0, QHamlitonian, 0, 0, tau, delta_time, 0, seed, 0) obt_type, obt_norm = test_evo._choose_evolution() obt_randomness = test_evo.random_list[0] exp_type = -1 exp_norm = 0.5 exp_randomness = 0.13436424411240122 assert_equal( obt_type, exp_type, "Different type returned." ) np.testing.assert_almost_equal( obt_norm, exp_norm ) np.testing.assert_almost_equal( obt_randomness, exp_randomness )
def test_jump_classical_degrees_of_freedom(): CQstate = lib.CQState(np.matrix([[sqrt(2)/sqrt(3)],[1j*1./sqrt(3)]]),1./4.,2./3.) L0 = np.matrix([[1.,0.],[0.,0.]]) L1 = np.matrix([[0.,0.],[0.,1.]]) delta_time = 2. tau = 4. pos_derivs = [lambda q,p : - 1./3. * p, lambda q,p : 1./6. * p] mom_derivs = [lambda q,p : 1./8. * q, lambda q,p : - 1./4. * q] QHamlitonian = lambda q,p : q * np.matrix([[0.,1.],[1.,0.]]) test_cont = lib.Unravelling(CQstate, [L0,L1], pos_derivs, mom_derivs, QHamlitonian, 0, 0, tau, delta_time, 0, 0, 0) test_cont._evolution_one_step(0, 1.) obt_position = test_cont.CQstate.position obt_momentum = test_cont.CQstate.momentum exp_position = 3./8. exp_momentum = 14./9. np.testing.assert_almost_equal( obt_position, exp_position ) np.testing.assert_almost_equal( obt_momentum, exp_momentum )
qu_mom_derivs = [lambda q, p: 0., lambda q, p: 0.]
clas_pos_derivs = lambda q, p: 0.
clas_mom_derivs = lambda q, p: p
# Measure time before simulation
tstart = datetime.now()
for iteration in range(iterations):
# Check if directory exists, otherwise create it
output_directory = Path('./output/')
output_directory.mkdir(parents=True, exist_ok=True)
# Filename for output
filename = './output/trajectory_{0}.dat'.format(str(iteration))
# Create unravelling and evolve
stern_gerlach = lib.Unravelling(copy(CQstate), [L0, L1], qu_pos_derivs,
qu_mom_derivs, QHamlitonian,
clas_pos_derivs, clas_mom_derivs, tau,
delta_time, final_time, seed, filename)
stern_gerlach.evolution()
del stern_gerlach
# Measure time after simulation
tfinal = datetime.now()
# Print time for loading and for averaging
tsim = tfinal - tstart
print('Simulation time is {}\n'.format(tsim))