def test_product(self):
np.random.seed(1034)
for N in range(1, 10):
ψ = np.random.rand(2**N, 2) - 0.5
ψ = ψ[:, 0] + 1j * ψ[:, 1]
ψ /= np.linalg.norm(ψ)
ψmps = MPS.fromvector(ψ, [2] * N)
ψ = ψmps.tovector()
ξ = np.random.rand(2**N, 2) - 0.5
ξ = ξ[:, 0] + 1j * ξ[:, 1]
ξ /= np.linalg.norm(ξ)
ξmps = MPS.fromvector(ξ, [2] * N)
ξ = ξmps.tovector()
ψξ = wavefunction_product(ψmps,
ξmps,
simplify=True,
normalize=False).tovector()
self.assertTrue(similar(ψξ, ψ * ξ))
ψcξ = wavefunction_product(ψmps,
ξmps,
conjugate=True,
simplify=False,
normalize=False).tovector()
self.assertTrue(similar(ψcξ, ψ.conj() * ξ))
def gaussian_mps(N):
x = np.linspace(-4, 4, 2**N + 1)[:-1]
ψ = np.exp(-(x**2) / 2.)
ψ /= np.linalg.norm(ψ)
return MPS.fromvector(ψ, [2] * N), ψ