def objective_function(self, params):
U_ = self.gate(params)
U = self.u
A = unitary_to_tensor(cirq.unitary(U))
A_ = unitary_to_tensor(cirq.unitary(U_))
_, r = Map(A,A_).right_fixed_point()
R = Environment(put_env_on_left_site(r), 'R')
L = Environment(put_env_on_right_site(r.conj().T), 'L')
W = Environment(W, 'W')
qbs = cirq.LineQubit.range(6)
C = cirq.Circuit.from_ops([cirq.H(qbs[3]), cirq.CNOT(*qbs[3:5]),
U(*qbs[2:4]),
U(*qbs[1:3]),
W(*qbs[2:4]),
L(*qbs[0:2]),
R(*qbs[4:]),
cirq.inverse(U_)(*qbs[1:3]),
cirq.inverse(U_)(*qbs[2:4]),
cirq.CNOT(*qbs[3:5]), cirq.H(qbs[3])])
s = cirq.Simulator(dtype=np.complex128)
return -np.abs(s.simulate(C).final_state[0])*2
def obj(p_, A, WW):
p, rs = p_[:15], p_[15:]
B = iMPS([unitary_to_tensor(cirq.unitary(gate(p)))]).left_canonicalise()[0]
U = Environment(tensor_to_unitary(A), 'U')
U_ = Environment(tensor_to_unitary(B), 'U\'')
E = Map(np.tensordot(WW, merge(A, A), [1, 0]), merge(B, B))
x,r = E.right_fixed_point()
x_,l = E.left_fixed_point()
R = Environment(put_env_on_left_site(r), 'θR')
L = Environment(put_env_on_right_site(r.conj().T), 'θL')
W = Environment(WW, 'W')
qbs = cirq.LineQubit.range(5)
C = cirq.Circuit.from_ops([R(*qbs[3:5]),
U(*qbs[2:4]),
U(*qbs[1:3]),
W(*qbs[2:4]),
L(*qbs[0:2]),
cirq.inverse(U_)(*qbs[1:3]),
cirq.inverse(U_)(*qbs[2:4]),
cirq.CNOT(*qbs[3:5]), cirq.H(qbs[3])])
s = cirq.Simulator(dtype=np.complex128)
return -np.sqrt(np.abs(np.sqrt(2)*s.simulate(C).final_state[0]))
def time_evolve_sim_state(params, U, W):
"""
Circuit which returns the overlap between 2 MPS states, defined by the unitaries U_ = U4(params) & U = tensor_to_unitary(A). The full wavefunction is returns for debugging purposes.
"""
circ = QuantumCircuit(6, 6)
U_ = self.gate(params)
A = unitary_to_tensor(cirq.unitary(U))
A_ = unitary_to_tensor(cirq.unitary(U_))
_, r = Map(A, A_).right_fixed_point()
R = Operator(put_env_on_left_site(r))
L = Operator(put_env_on_right_site(r.conj().T))
circ.h(3)
circ.cx(3, 4)
circ.unitary(U, [3, 2])
circ.unitary(U, [2, 1])
circ.unitary(L, [1, 0])
circ.unitary(R, [5, 4])
circ.unitary(H, [3, 2])
circ.unitary(target_u, [2, 1])
circ.unitary(target_u, [3, 2])
circ.cx(3, 4)
circ.h(3)
result = execute(circ, simulator).result()
ψ = result.get_statevector(circ)
return ψ
def scars_cost_fun_alternate(params, current_params, ham):
'''
This cost function doesn't use the quantum circuit parameterisation
params are formatted like: [θ1, ϕ1, ϕ2, θ2], for convenience with the classical differential eqn solver
'''
θ1, ϕ1, ϕ2, θ2 = current_params
θ1_, ϕ1_, ϕ2_, θ2_ = params
A1 = A(θ1, ϕ1)
A2 = A(θ2, ϕ2)
A1_ = A(θ1_, ϕ1_)
A2_ = A(θ2_, ϕ2_)
A12 = merge(A1, A2)
A12_ = merge(A1_, A2_)
_, r = Map(A12, A12_).right_fixed_point()
R = Environment(put_env_on_left_site(r), 'R')
L = Environment(put_env_on_right_site(r.conj().T), 'L')
U12 = Tensor(tensor_to_unitary(A12), 'U')
U12_ = Tensor(tensor_to_unitary(A12_), 'U\'')
q = cirq.LineQubit.range(8)
circuit = cirq.Circuit.from_ops([
cirq.H(q[5]),
cirq.CNOT(q[5], q[6]),
U12(*q[3:6]),
U12(*q[1:4]),
L(*q[0:2]),
ham(*q[2:6]),
R(*q[6:8]),
cirq.inverse(U12_(*q[1:4])),
cirq.inverse(U12_(*q[3:6])),
cirq.CNOT(q[5], q[6]),
cirq.H(q[5])
])
# print(circuit.to_text_diagram(transpose = True))
sim = cirq.Simulator()
ψ = sim.simulate(circuit).final_state[0]
return -np.abs(ψ) * 2
def scars_time_evolve_cost_function(params, current_params, ham):
'''
params are formatted like: [θ1, ϕ1, ϕ2, θ2], for convenience with the classical differential eqn solver
'''
θ1, ϕ1, ϕ2, θ2 = current_params
θ1_, ϕ1_, ϕ2_, θ2_ = params
A1 = A(θ1, ϕ1)
A2 = A(θ2, ϕ2)
A1_ = A(θ1_, ϕ1_)
A2_ = A(θ2_, ϕ2_)
_, r = Map(merge(A1, A2), merge(A1_, A2_)).right_fixed_point()
R = Environment(put_env_on_left_site(r), 'R')
L = Environment(put_env_on_right_site(r.conj().T), 'L')
U12 = ScarGate(current_params)
U12_ = ScarGate(params)
q = cirq.LineQubit.range(8)
circuit = cirq.Circuit.from_ops([
cirq.H(q[5]),
cirq.CNOT(q[5], q[6]),
U12(*q[3:6]),
U12(*q[1:4]),
L(*q[0:2]),
ham(*q[2:6]),
R(*q[6:8]),
cirq.inverse(U12_(*q[1:4])),
cirq.inverse(U12_(*q[3:6])),
cirq.CNOT(q[5], q[6]),
cirq.H(q[5])
])
# print(circuit.to_text_diagram(transpose = True))
sim = cirq.Simulator()
ψ = sim.simulate(circuit).final_state[0]
return -np.abs(ψ) * 2
def run_tests(N):
"""run_tests: just a whole bunch of tests.
:param N: number of iterations to run
"""
for _ in range(N):
q = np.random.randn(2, 2)+1j*np.random.randn(2, 2)
assert np.allclose(get_env_off_left_site(np.prod(put_env_on_left_site(q, ret_n=True))), q)
assert np.allclose(get_env_off_right_site(np.prod(put_env_on_right_site(q, ret_n=True))), q)
U = put_env_on_left_site(q)
V = put_env_on_right_site(q)
assert np.allclose(V.conj().T@V, np.eye(U.shape[0]))
assert np.allclose(U.conj().T@U, np.eye(U.shape[0]))
A = iMPS().random(2, 2).left_canonicalise()[0]
B = iMPS().random(2, 2).left_canonicalise()[0]#np.tensordot(expm(-1j*Z*dt), A, [1, 0])
U = Environment(tensor_to_unitary(A), 'U')
U_ = Environment(tensor_to_unitary(B), 'U\'')
x, r = Map(merge(A, A), merge(B, B)).right_fixed_point()
x_, l = Map(merge(A, A), merge(B, B)).left_fixed_point()
L = put_env_on_right_site(l)
R = put_env_on_left_site(r)
assert np.allclose(get_env_off_left_site(put_env_on_left_site(r)), r)
assert np.allclose(get_env_off_right_site(put_env_on_right_site(l)), l)
U = put_env_on_left_site(r)
V = put_env_on_right_site(l)
assert np.allclose(V.conj().T@V, np.eye(U.shape[0]))
assert np.allclose(U.conj().T@U, np.eye(U.shape[0]))
A = iMPS().random(2, 2).left_canonicalise()[0]
B = iMPS().random(2, 2).left_canonicalise()[0]#np.tensordot(expm(-1j*Z*dt), A, [1, 0])
E = Map(A, B)
x, r = E.right_fixed_point()
x_, l = E.left_fixed_point()
U = Environment(tensor_to_unitary(A), 'U')
U_ = Environment(tensor_to_unitary(B), 'U\'')
R = Environment(put_env_on_left_site(r), 'R')
L = Environment(put_env_on_right_site(l.conj().T), 'L')
qbs = cirq.LineQubit.range(4)
for g in zip([cirq.I, cirq.X, cirq.Y, cirq.Z], [I, X, Y, Z]):
C = cirq.Circuit.from_ops([cirq.H(qbs[1]), cirq.CNOT(*qbs[1:3]),
R(*qbs[2:]),
g[0](qbs[1]),
cirq.CNOT(*qbs[1:3]), cirq.H(qbs[1])])
s = cirq.Simulator()
assert np.allclose(2*s.simulate(C).final_state[0]-np.trace(g[1]@r), 0, 1e-6, 1e-6)
# r is the matrix on the 1st qubit
qbs = cirq.LineQubit.range(4)
for g in zip([cirq.I, cirq.X, cirq.Y, cirq.Z], [I, X, Y, Z]):
C = cirq.Circuit.from_ops([cirq.H(qbs[1]), cirq.CNOT(*qbs[1:3]),
U(*qbs[0:2]),
R(*qbs[2:]),
g[0](qbs[0]),
cirq.inverse(U_)(*qbs[0:2]),
cirq.CNOT(*qbs[1:3]), cirq.H(qbs[1])])
s = cirq.Simulator()
assert np.allclose(2*s.simulate(C).final_state[0]-x*np.trace(g[1]@r), 0, 1e-6, 1e-6)
qbs = cirq.LineQubit.range(5)
for g in zip([cirq.I, cirq.X, cirq.Y, cirq.Z], [I, X, Y, Z]):
C = cirq.Circuit.from_ops([cirq.H(qbs[2]), cirq.CNOT(*qbs[2:4]),
U(*qbs[1:3]),
U(*qbs[0:2]),
R(*qbs[3:]),
g[0](qbs[0]),
cirq.inverse(U_)(*qbs[0:2]),
cirq.inverse(U_)(*qbs[1:3]),
cirq.CNOT(*qbs[2:4]), cirq.H(qbs[2])])
s = cirq.Simulator()
#print(C.to_text_diagram(transpose=True))
#raise Exception
assert np.allclose(2*s.simulate(C).final_state[0]-x**2*np.trace(g[1]@r), 0, 1e-6, 1e-6)
qbs = cirq.LineQubit.range(3)
for g in zip([cirq.I, cirq.X, cirq.Y, cirq.Z], [I, X, Y, Z]):
C = cirq.Circuit.from_ops([cirq.H(qbs[1]), cirq.CNOT(*qbs[1:3]),
L(*qbs[:2]),
g[0](qbs[2]),
cirq.CNOT(*qbs[1:3]), cirq.H(qbs[1])])
s = cirq.Simulator()
assert np.allclose(2*s.simulate(C).final_state[0]-np.trace(g[1]@l.conj()), 0, 1e-6, 1e-6)
# r is the matrix on the 1st qubit
qbs = cirq.LineQubit.range(4)
for g in zip([cirq.I, cirq.X, cirq.Y, cirq.Z], [I, X, Y, Z]):
C = cirq.Circuit.from_ops([cirq.H(qbs[2]), cirq.CNOT(*qbs[2:4]),
U(*qbs[1:3]),
L(*qbs[:2]),
g[0](qbs[3]),
cirq.inverse(U_)(*qbs[1:3]),
cirq.CNOT(*qbs[2:4]), cirq.H(qbs[2])])
s = cirq.Simulator()
#print(C.to_text_diagram(transpose=True))
#raise Exception
assert np.allclose(2*s.simulate(C).final_state[0]-x*np.trace(g[1]@l.conj()), 0, 1e-6, 1e-6)
qbs = cirq.LineQubit.range(5)
for g in zip([cirq.I, cirq.X, cirq.Y, cirq.Z], [I, X, Y, Z]):
C = cirq.Circuit.from_ops([cirq.H(qbs[3]), cirq.CNOT(*qbs[3:5]),
U(*qbs[2:4]),
U(*qbs[1:3]),
L(*qbs[0:2]),
g[0](qbs[4]),
cirq.inverse(U_)(*qbs[1:3]),
cirq.inverse(U_)(*qbs[2:4]),
cirq.CNOT(*qbs[3:5]), cirq.H(qbs[3])])
s = cirq.Simulator()
assert np.allclose(2*s.simulate(C).final_state[0]-x**2*np.trace(g[1]@l.conj()), 0, 1e-6, 1e-6)
qbs = cirq.LineQubit.range(6)
C = cirq.Circuit.from_ops([cirq.H(qbs[3]), cirq.CNOT(*qbs[3:5]),
U(*qbs[2:4]),
U(*qbs[1:3]),
L(*qbs[0:2]),
R(*qbs[4:]),
cirq.inverse(U_)(*qbs[1:3]),
cirq.inverse(U_)(*qbs[2:4]),
cirq.CNOT(*qbs[3:5]), cirq.H(qbs[3])])
s = cirq.Simulator()
assert np.allclose(2*s.simulate(C).final_state[0], x**2*np.trace(l.conj().T@r))
