def test_random_unitary(self, tol): """This test checks the rectangular decomposition for a random unitary. A random unitary is drawn from the Haar measure, then is decomposed via the rectangular decomposition of Clements et al., and the resulting beamsplitters are multiplied together. Test passes if the product matches the drawn unitary. """ # TODO: this test currently uses the T and Ti functions used to compute # Clements as the comparison. Probably should be changed. n = 20 U = haar_measure(n) tilist, tlist, diags = dec.clements(U) qrec = np.identity(n) for i in tilist: qrec = dec.T(*i) @ qrec qrec = np.diag(diags) @ qrec for i in reversed(tlist): qrec = dec.Ti(*i) @ qrec assert np.allclose(U, qrec, atol=tol, rtol=0)
def test_decomposition(self, tol): """Test that an interferometer is correctly decomposed""" n = 3 prog = sf.Program(n) U = random_interferometer(n) BS1, BS2, R = dec.clements(U) G = ops.Interferometer(U) cmds = G.decompose(prog.register) S = np.identity(2 * n) # calculating the resulting decomposed symplectic for cmd in cmds: # all operations should be BSgates or Rgates assert isinstance(cmd.op, (ops.BSgate, ops.Rgate)) # build up the symplectic transform modes = [i.ind for i in cmd.reg] if isinstance(cmd.op, ops.Rgate): S = _rotation(cmd.op.p[0].x, modes, n) @ S if isinstance(cmd.op, ops.BSgate): S = _beamsplitter(cmd.op.p[0].x, cmd.op.p[1].x, modes, n) @ S # the resulting applied unitary X1 = S[:n, :n] P1 = S[n:, :n] U_applied = X1 + 1j * P1 assert np.allclose(U, U_applied, atol=tol, rtol=0)
def test_clements_identity(self): n = 20 U = np.identity(n) (tilist, tlist, diags) = dec.clements(U) qrec = np.identity(n) for i in tilist: qrec = dec.T(*i) @ qrec qrec = np.diag(diags) @ qrec for i in reversed(tlist): qrec = dec.Ti(*i) @ qrec self.assertAllAlmostEqual(U, qrec, delta=self.tol)
def test_clements_random_unitary(self): error = np.empty(nsamples) for k in range(nsamples): n = 20 V = haar_measure(n) (tilist, tlist, diags) = dec.clements(V) qrec = np.identity(n) for i in tilist: qrec = dec.T(*i) @ qrec qrec = np.diag(diags) @ qrec for i in reversed(tlist): qrec = dec.Ti(*i) @ qrec error[k] = np.linalg.norm(V - qrec) self.assertAlmostEqual(error.mean(), 0)
def test_clements_identity(self): """This test checks the rectangular decomposition for an identity unitary. An identity unitary is decomposed via the rectangular decomposition of Clements et al. and the resulting beamsplitters are multiplied together. Test passes if the product matches identity. """ self.logTestName() n = 20 U = np.identity(n) (tilist, tlist, diags) = dec.clements(U) qrec = np.identity(n) for i in tilist: qrec = dec.T(*i) @ qrec qrec = np.diag(diags) @ qrec for i in reversed(tlist): qrec = dec.Ti(*i) @ qrec self.assertAllAlmostEqual(U, qrec, delta=self.tol)
def test_clements_random_unitary(self): """This test checks the rectangular decomposition for a random unitary. A random unitary is drawn from the Haar measure, then is decomposed via the rectangular decomposition of Clements et al., and the resulting beamsplitters are multiplied together. Test passes if the product matches the drawn unitary. """ self.logTestName() error = np.empty(nsamples) for k in range(nsamples): n = 20 V = haar_measure(n) (tilist, tlist, diags) = dec.clements(V) qrec = np.identity(n) for i in tilist: qrec = dec.T(*i) @ qrec qrec = np.diag(diags) @ qrec for i in reversed(tlist): qrec = dec.Ti(*i) @ qrec error[k] = np.linalg.norm(V - qrec) self.assertAlmostEqual(error.mean(), 0)
def test_unitary_validation(self): """Test that an exception is raised if not unitary""" A = np.random.random([5, 5]) + 1j * np.random.random([5, 5]) with pytest.raises(ValueError, match="matrix is not unitary"): dec.clements(A)