def test_project_eig(self, backend):
Hl = qu.Lazy(qu.ham_heis, 4, sparse=True, shape=(16, 16))
Pl = qu.Lazy(qu.zspin_projector, 4, shape=(16, 6))
ge, gs = qu.eigh(Hl, P=Pl, k=1, backend=backend)
assert ge == pytest.approx(-2)
assert qu.expec(gs, gs) == pytest.approx(1.0)
def test_basic(self, sparse):
ownership = (0, 7)
hl = qu.Lazy(qu.ham_heis, n=4, sparse=sparse, shape=(16, 16))
print(hl)
h = 1 * hl(ownership=ownership)
h_ex = qu.ham_heis(n=4, sparse=sparse)[slice(*ownership), :]
assert_allclose(h.A, h_ex.A)
# Get some MPI information
comm = MPI.COMM_WORLD
rank, size = comm.Get_rank(), comm.Get_size()
print(f"I am worker {rank} of total {size} runnning main script...")
# setup a verbose version of the ham_heis constructor, and make it Lazy
n = 18
shape = (2**n, 2**n)
# this makes the function print some information when called
# - in order to be pickled is has to be located in the main package
ham_heis_verbose = qu.utils.Verbosify(qu.ham_heis,
highlight='ownership',
mpi=True)
H = qu.Lazy(ham_heis_verbose, n=n, sparse=True, shape=shape)
# random initial state
# - must make sure all processes have the same seed to be pure
psi0 = qu.rand_ket(2**n, seed=42)
# evolve the system, processes split 'hard' work (slepc computations)
# - should see each worker gets given a different ownership rows
# - but all end up with the results.
evo = qu.Evolution(psi0, H, method='expm', expm_backend='slepc')
evo.update_to(5)
print(f"{rank}: I have final state norm {qu.expec(evo.pt, evo.pt)}")
# Now lets demonstrate using the MPI pool construct
pool = qu.get_mpi_pool()