def _optimize(self, c):
c_tket = pyzx_to_tk(c)
cost = lambda c : c.n_gates_of_type(OpType.CX)
comp = RepeatWithMetricPass(SequencePass([CommuteThroughMultis(), RemoveRedundancies()]), cost)
comp.apply(c_tket)
c_opt = tk_to_pyzx(c_tket)
return c_opt
def _optimize(self, c):
c_tket = pyzx_to_tk(c)
FullPeepholeOptimise().apply(c_tket)
# FIXME: Failing equality!
# RebasePyZX().apply(c_tket)
# c_opt = tk_to_pyzx(c_tket)
# FIXME: Failing equality!
qasm_str = circuit_to_qasm_str(c_tket)
c_opt = zx.Circuit.from_qasm(qasm_str)
return c_opt
def tket_pass_base(c, g, _pass):
orig_tcount = c.tcount()
orig_2qubitcount = c.twoqubitcount()
c_tket = pyzx_to_tk(c)
_pass.apply(c_tket)
RebasePyZX().apply(c_tket)
c_opt = tk_to_pyzx(c_tket)
opt_tcount = c_opt.tcount()
opt_2qubitcount = c_opt.twoqubitcount()
# Quick and dirty. In theory, should depend on score function
if orig_tcount == opt_tcount and orig_2qubitcount == opt_2qubitcount:
return False, (c, g)
return True, (c_opt, c_opt.to_graph())
def test_statevector() -> None:
b = AerStateBackend()
circ = Circuit(3, name="test")
circ.H(2)
circ.X(0)
circ.H(0)
circ.CX(0, 1)
circ.CZ(1, 2)
circ.Sdg(0)
circ.Tdg(1)
circ.Z(1)
circ.T(2)
circ.Rx(0.3333, 1)
circ.Rz(0.3333, 1)
zxcirc = tk_to_pyzx(circ)
assert zxcirc.name == circ.name
b.compile_circuit(circ)
state = b.get_state(circ)
circ2 = pyzx_to_tk(zxcirc)
assert circ2.name == circ.name
b.compile_circuit(circ2)
state2 = b.get_state(circ2)
assert np.allclose(state, state2, atol=1e-10)
def _optimize(self, c):
c_tket = pyzx_to_tk(c)
RemoveRedundancies().apply(c_tket)
c_opt = tk_to_pyzx(c_tket)
return c_opt