qml.transforms.transpile

transpile(tape, coupling_map, device=None)

Transpile a circuit according to a desired coupling map

Warning

This transform does not yet support measurements of Hamiltonians or tensor products of observables. If a circuit is passed which contains these types of measurements, a NotImplementedError will be raised.

Parameters

• tape (QNode or QuantumTape or Callable) – A quantum tape.

• coupling_map – Data specifying the couplings between different qubits. This data can be any format accepted by nx.to_networkx_graph(), currently including edge list, dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy sparse matrix, or PyGraphviz graph.

Returns

The transformed circuit as described in qml.transform.

Return type

qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]

Example

Consider the following example circuit

def circuit():
    qml.CNOT(wires=[0, 1])
    qml.CNOT(wires=[2, 3])
    qml.CNOT(wires=[1, 3])
    qml.CNOT(wires=[1, 2])
    qml.CNOT(wires=[2, 3])
    qml.CNOT(wires=[0, 3])
    return qml.probs(wires=[0, 1, 2, 3])

which, before transpiling it looks like this:

0: ──╭●──────────────╭●──╭┤ Probs
1: ──╰X──╭●──╭●──────│───├┤ Probs
2: ──╭●──│───╰X──╭●──│───├┤ Probs
3: ──╰X──╰X──────╰X──╰X──╰┤ Probs

Suppose we have a device which has connectivity constraints according to the graph:

0 --- 1
|     |
2 --- 3

We encode this in a coupling map as a list of the edges which are present in the graph, and then pass this, together with the circuit, to the transpile function to get a circuit which can be executed for the specified coupling map:

>>> dev = qml.device('default.qubit', wires=[0, 1, 2, 3])
>>> transpiled_circuit = qml.transforms.transpile(circuit, coupling_map=[(0, 1), (1, 3), (3, 2), (2, 0)])
>>> transpiled_qnode = qml.QNode(transpiled_circuit, dev)
>>> print(qml.draw(transpiled_qnode)())
0: ─╭●────────────────╭●─┤ ╭Probs
1: ─╰X─╭●───────╭●────│──┤ ├Probs
2: ─╭●─│──╭SWAP─│──╭X─╰X─┤ ├Probs
3: ─╰X─╰X─╰SWAP─╰X─╰●────┤ ╰Probs

A swap gate has been applied to wires 2 and 3, and the remaining gates have been adapted accordingly

code/api/pennylane.transforms.transpile

 

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