Is there a way to decompose a quantum circuit into layers?

Question

For example, if take the following circuit as the input (either QASM or Qiskit):

qreg q[2];
creg c[2];
x q[1];
h q[0];
h q[1];
cx q[0],q[1];
h q[0];
measure q[0] -> c[0];
measure q[1] -> c[1];

The expected output will be:

layer[0] = [H [q0], X [q1]]
layer[1] = [H [q1]]
layer[2] = [cnot [q0] [q1]]
layer[3] = [H [q0]]
layer[4] = [measure [q0]]
layer[5] = [measure [q1]]

Is there a Qiskit function to achieve this? If not, suggestions to implement this task are also welcomed.

Thanks!

Answer 1

You can decompose a quantum circuit into layers using DAGCircuit.layers() method:

from qiskit.converters import circuit_to_dag, dag_to_circuit
from IPython.display import display
dag = circuit_to_dag(circ)
for layer in dag.layers():
    layer_as_circuit = dag_to_circuit(layer['graph'])
    display(layer_as_circuit.draw('mpl'))

where circ is a QuantumCircuit. The output will be:

DAGCircuit.layers() method constructs the layers using a greedy algorithm.

You can also break down your circuit into layers based on some scheduling policy. In the following example we apply an "as late as possible" (ALAP) scheduling policy:

from qiskit.transpiler import PassManager, InstructionDurations
from qiskit.transpiler.passes import  ALAPScheduleAnalysis, PadDelay
Apply the scheduling policy:
instruction_durations = InstructionDurations(
    [
        ("h", None, 160),
        ("x", None, 160),
        ("cx", None, 800),
        ("measure", None, 1600),
    ]
)
pass_manager = PassManager(
    [
      ALAPScheduleAnalysis(instruction_durations),
      PadDelay(),
    ]
)
transpiled_circ = pass_manager.run(circ)
Use DAGCircuit.layers() method with the transpiled circuit:
dag = circuit_to_dag(transpiled_circ)
for layer in dag.layers():
    layer_as_circuit = dag_to_circuit(layer['graph'])
    # Remove the Delay instructions:
    for _inst in layer_as_circuit.data:
      if _inst.operation.name == 'delay':
        layer_as_circuit.data.remove(_inst)
display(layer_as_circuit.draw('mpl'))

The result:

Similarly, you can apply "as soon as possible" (ASAP) scheduling policy.