def test_conditions() -> None:
box_c = Circuit(2, 2)
box_c.Z(0)
box_c.Y(1, condition_bits=[0, 1], condition_value=1)
box_c.Measure(0, 0, condition_bits=[0, 1], condition_value=0)
box = CircBox(box_c)
u = np.asarray([[0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0]])
ubox = Unitary2qBox(u)
c = Circuit(2, 2, name="c")
b = c.add_c_register("b", 1)
c.add_circbox(
box,
[Qubit(0), Qubit(1), Bit(0), Bit(1)],
condition_bits=[b[0]],
condition_value=1,
)
c.add_unitary2qbox(ubox,
Qubit(0),
Qubit(1),
condition_bits=[b[0]],
condition_value=0)
c2 = c.copy()
qc = tk_to_qiskit(c)
c1 = qiskit_to_tk(qc)
assert len(c1.get_commands()) == 2
DecomposeBoxes().apply(c)
DecomposeBoxes().apply(c1)
assert c == c1
c2.Z(1, condition=reg_eq(b, 1))
qc = tk_to_qiskit(c2)
c1 = qiskit_to_tk(qc)
assert len(c1.get_commands()) == 3
def test_estimates() -> None:
"""
Check that the resource estimator gives reasonable results.
"""
b = QsharpEstimatorBackend()
c = Circuit(3)
c.H(0)
c.CX(0, 1)
c.CCX(0, 1, 2)
c.Rx(0.3, 1)
c.Ry(0.4, 2)
c.Rz(1.1, 0)
c.S(1)
c.SWAP(0, 2)
c.T(1)
c.X(0)
c.Y(1)
c.Z(2)
pbox = PauliExpBox([Pauli.X, Pauli.I, Pauli.Z], 0.25)
c.add_pauliexpbox(pbox, [2, 0, 1])
b.compile_circuit(c, 0)
resources = b.get_resources(c)
assert resources["CNOT"] >= 1
assert resources["QubitClifford"] >= 1
assert resources["R"] >= 1
assert resources["T"] >= 1
assert resources["Depth"] >= 1
assert resources["Width"] == 3
assert resources["BorrowedWidth"] == 0
def test_sym_parameterised() -> None:
circ = Circuit(3, name="test")
circ.Z(1)
alpha = fresh_symbol("alpha")
circ.Rx(alpha, 0)
with pytest.raises(Exception) as excinfo:
_ = tk_to_pyzx(circ)
assert "as it contains symbolic parameters." in str(excinfo.value)
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)
n = [Node("n", i) for i in range(5)]
arc = Architecture([[n[0], n[1]], [n[1], n[2]], [n[2], n[3]], [n[3], n[4]]])
# A `Device` is a model of a physical device, which encapsulates both its `Architecture` and its gate noise characteristics. Ignoring the latter, we can construct a 'noise-free' `Device` directly from an `Architecture`:
from pytket.device import Device
dev = Device(arc)
# Suppose we have a circuit that we wish to run on this device:
circ = Circuit(5)
circ.CX(0, 1)
circ.H(0)
circ.Z(1)
circ.CX(0, 3)
circ.Rx(1.5, 3)
circ.CX(2, 4)
circ.X(2)
circ.CX(1, 4)
circ.CX(0, 4)
print(tk_to_qiskit(circ))
# A mapping pass lets us rewrite this circuit for our device:
from pytket.passes import DefaultMappingPass
mapper = DefaultMappingPass(dev)
cu = CompilationUnit(circ)
# This notebook will introduce the basic methods of analysis and visualization of circuits available in `pytket`. # # It makes use of the modules `pytket_qiskit` and `pytket_cirq` for visualization; these need to be installed (with `pip`) in addition to `pytket`. # # We'll start by generating a small circuit to use as an example, and give it a name. from pytket.circuit import Circuit, OpType c = Circuit(4, name="example") c.add_gate(OpType.CU1, 0.5, [0, 1]) c.H(0).X(1).Y(2).Z(3) c.X(0).CX(1, 2).Y(1).Z(2).H(3) c.Y(0).Z(1) c.add_gate(OpType.CU1, 0.5, [2, 3]) c.H(2).X(3) c.Z(0).H(1).X(2).Y(3).CX(3, 0) # ## Basic statistics # From the circuit we can easily read off the number of qubits ... c.n_qubits # ... the name ... c.name # ... the overall depth of the circuit ... c.depth()
