# --------------------------
initial_guess = tt.ones(operator.col_dims, [1] * order,
ranks=tt_rank).ortho_right()
# solve Markovian master equation in TT format
# --------------------------------------------
print('TT approach:\n')
solution = ode.trapezoidal_rule(operator, initial_distribution, initial_guess,
step_sizes)
# operator in QTT format
# ----------------------
operator = TT.tt2qtt(operator, qtt_modes, qtt_modes, threshold=threshold)
# initial distribution in QTT format
# ----------------------------------
initial_distribution = tt.zeros(operator.col_dims, [1] * operator.order)
for p in range(initial_distribution.order):
initial_distribution.cores[p][0, 0, 0, 0] = 1
# initial guess in QTT format
# ---------------------------
initial_guess = tt.ones(operator.col_dims, [1] * operator.order,
ranks=qtt_rank).ortho_right()
# solve Markovian master equation in QTT format
operator = mdl.two_step_destruction(
1, 2, 1, m).tt2qtt([[2] * m] + [[2] * (m + 1)] + [[2] * m] + [[2] * m],
[[2] * m] + [[2] * (m + 1)] + [[2] * m] + [[2] * m],
threshold=10**-14)
# initial distribution in TT format and convert to QTT format
# -----------------------------------------------------------
initial_distribution = tt.zeros([2**m, 2**(m + 1), 2**m, 2**m], [1] * 4)
initial_distribution.cores[0][0, -1, 0, 0] = 1
initial_distribution.cores[1][0, -2, 0, 0] = 1
initial_distribution.cores[2][0, 0, 0, 0] = 1
initial_distribution.cores[3][0, 0, 0, 0] = 1
initial_distribution = TT.tt2qtt(
initial_distribution, [[2] * m] + [[2] * (m + 1)] + [[2] * m] + [[2] * m],
[[1] * m] + [[1] * (m + 1)] + [[1] * m] + [[1] * m],
threshold=0)
# initial guess in QTT format
# ---------------------------
initial_guess = tt.uniform([2] * (4 * m + 1), ranks=qtt_rank).ortho_right()
# solve Markovian master equation in QTT format
# ---------------------------------------------
solution = ode.implicit_euler(operator,
initial_distribution,
initial_guess,
step_sizes,
tt_solver='mals',
