cut_circuit(tape: pennylane.tape.tape.QuantumTape, auto_cutter: Union[bool, Callable] = False, use_opt_einsum: bool = False, device_wires: Optional[pennylane.wires.Wires] = None, max_depth: int = 1, **kwargs) → Tuple[Tuple[pennylane.tape.tape.QuantumTape], Callable]¶
Cut up a quantum circuit into smaller circuit fragments.
Following the approach outlined in Theorem 2 of Peng et al., strategic placement of
WireCutoperations can allow a quantum circuit to be split into disconnected circuit fragments. Each circuit fragment is then executed multiple times by varying the state preparations and measurements at incoming and outgoing cut locations, respectively, resulting in a process tensor describing the action of the fragment. The process tensors are then contracted to provide the result of the original uncut circuit.
Only circuits that return a single expectation value are supported.
tape (QuantumTape) – the tape of the full circuit to be cut
auto_cutter (Union[bool, Callable]) – Toggle for enabling automatic cutting with the default
kahypar_cut()partition method. Can also pass a graph partitioning function that takes an input graph and returns a list of edges to be cut based on a given set of constraints and objective. The default
kahypar_cut()function requires KaHyPar to be installed using
pip install kahyparfor Linux and Mac users or visiting the instructions here to compile from source for Windows users.
use_opt_einsum (bool) – Determines whether to use the opt_einsum package. This package is useful for faster tensor contractions of large networks but must be installed separately using, e.g.,
pip install opt_einsum. Both settings for
use_opt_einsumresult in a differentiable contraction.
device_wires (Wires) – Wires of the device that the cut circuits are to be run on. When transforming a QNode, this argument is optional and will be set to the QNode’s device wires. Required when transforming a tape.
max_depth (int) – The maximum depth used to expand the circuit while searching for wire cuts. Only applicable when transforming a QNode.
kwargs – Additional keyword arguments to be passed to a callable
auto_cutterargument. For the default KaHyPar cutter, please refer to the docstring of functions
kahypar_cut()for the available arguments.
Function which accepts the same arguments as the QNode. When called, this function will perform a process tomography of the partitioned circuit fragments and combine the results via tensor contractions.
- Return type
The following \(3\)-qubit circuit contains a
WireCutoperation. When decorated with
@qml.cut_circuit, we can cut the circuit into two \(2\)-qubit fragments:
dev = qml.device("default.qubit", wires=2) @qml.cut_circuit @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) qml.RY(0.9, wires=1) qml.RX(0.3, wires=2) qml.CZ(wires=[0, 1]) qml.RY(-0.4, wires=0) qml.WireCut(wires=1) qml.CZ(wires=[1, 2]) return qml.expval(qml.grouping.string_to_pauli_word("ZZZ"))
circuitwill run multiple configurations of the \(2\)-qubit fragments which are then postprocessed to give the result of the original circuit:
>>> x = np.array(0.531, requires_grad=True) >>> circuit(x) 0.47165198882111165
Futhermore, the output of the cut circuit is also differentiable:
>>> qml.grad(circuit)(x) -0.276982865449393
Alternatively, if the optimal wire-cut placement is unknown for an arbitrary circuit, the
auto_cutteroption can be enabled to make attempts in finding such an optimal cut. The following examples shows this capability on the same circuit as above but with the
@qml.cut_circuit(auto_cutter=True) @qml.qnode(dev) def circuit(x): qml.RX(x, wires=0) qml.RY(0.9, wires=1) qml.RX(0.3, wires=2) qml.CZ(wires=[0, 1]) qml.RY(-0.4, wires=0) qml.CZ(wires=[1, 2]) return qml.expval(qml.grouping.string_to_pauli_word("ZZZ"))
>>> x = np.array(0.531, requires_grad=True) >>> circuit(x) 0.47165198882111165 >>> qml.grad(circuit)(x) -0.276982865449393
WireCutoperations and decorating the QNode with the
cut_circuit()batch transform is the suggested entrypoint into circuit cutting. However, advanced users also have the option to work directly with a
QuantumTapeand manipulate the tape to perform circuit cutting using the below functionality:
Converts a quantum tape to a directed multigraph.
find_and_place_cuts(graph[, cut_method, …])
Automatically finds and places optimal
WireCutnodes into a given tape-converted graph using a customizable graph partitioning function.
WireCutnode in the graph with a
Fragments a graph into a collection of subgraphs as well as returning the communication (quotient) graph.
Converts a directed multigraph to the corresponding
Map the wires of a tape to a new set of wires.
Expands a fragment tape into a sequence of tapes for each configuration of the contained
qcut_processing_fn(results, …[, …])
Processing function for the
A circuit-cutting distribution policy for executing (large) circuits on available (comparably smaller) devices.
The following shows how these elementary steps are combined as part of the
Consider the circuit below:
with qml.tape.QuantumTape() as tape: qml.RX(0.531, wires=0) qml.RY(0.9, wires=1) qml.RX(0.3, wires=2) qml.CZ(wires=[0, 1]) qml.RY(-0.4, wires=0) qml.WireCut(wires=1) qml.CZ(wires=[1, 2]) qml.expval(qml.grouping.string_to_pauli_word("ZZZ"))
To cut the circuit, we first convert it to its graph representation:
>>> graph = qml.transforms.qcut.tape_to_graph(tape)
If, however, the optimal location of the
WireCutis unknown, we can use
find_and_place_cuts()to make attempts in automatically finding such a cut given the device constraints. Using the same circuit as above but with the
WireCutremoved, the same (optimal) cut can be recovered with automatic cutting:
with qml.tape.QuantumTape() as uncut_tape: qml.RX(0.531, wires=0) qml.RY(0.9, wires=1) qml.RX(0.3, wires=2) qml.CZ(wires=[0, 1]) qml.RY(-0.4, wires=0) qml.CZ(wires=[1, 2]) qml.expval(qml.grouping.string_to_pauli_word("ZZZ"))
>>> cut_graph = qml.transforms.qcut.find_and_place_cuts( graph = qml.transforms.qcut.tape_to_graph(uncut_tape), cut_strategy = qml.transforms.qcut.CutStrategy(max_free_wires=2), ) >>> print(qml.transforms.qcut.graph_to_tape(cut_graph).draw()) 0: ──RX─╭●──RY────┤ ╭<[email protected]@Z> 1: ──RY─╰Z──//─╭●─┤ ├<[email protected]@Z> 2: ──RX────────╰Z─┤ ╰<[email protected]@Z>
Our next step is to remove the
WireCutnodes in the graph and replace with
PrepareNodepairs are placeholder operations that allow us to cut the circuit graph and then iterate over measurement and preparation configurations at cut locations. First, the
fragment_graph()function pulls apart the graph into disconnected components as well as returning the communication_graph detailing the connectivity between the components.
>>> fragments, communication_graph = qml.transforms.qcut.fragment_graph(graph)
We now convert the
>>> fragment_tapes = [qml.transforms.qcut.graph_to_tape(f) for f in fragments]
The circuit fragments can now be visualized:
>>> print(fragment_tapes.draw()) 0: ──RX(0.531)──╭●──RY(-0.4)─────┤ ⟨Z⟩ 1: ──RY(0.9)────╰Z──MeasureNode──┤
>>> print(fragment_tapes.draw()) 2: ──RX(0.3)──────╭Z──╭┤ ⟨Z ⊗ Z⟩ 1: ──PrepareNode──╰●──╰┤ ⟨Z ⊗ Z⟩
Additionally, we must remap the tape wires to match those available on our device.
>>> dev = qml.device("default.qubit", wires=2) >>> fragment_tapes = [ ... qml.transforms.qcut.remap_tape_wires(t, dev.wires) for t in fragment_tapes ... ]
Next, each circuit fragment is expanded over
PrepareNodeconfigurations and a flat list of tapes is created:
expanded = [qml.transforms.qcut.expand_fragment_tape(t) for t in fragment_tapes] configurations =  prepare_nodes =  measure_nodes =  for tapes, p, m in expanded: configurations.append(tapes) prepare_nodes.append(p) measure_nodes.append(m) tapes = tuple(tape for c in configurations for tape in c)
Each configuration is drawn below:
>>> for t in tapes: ... print(qml.drawer.tape_text(t))
0: ──RX(0.531)──╭●──RY(-0.4)──╭┤ ⟨Z ⊗ I⟩ ╭┤ ⟨Z ⊗ Z⟩ 1: ──RY(0.9)────╰Z────────────╰┤ ⟨Z ⊗ I⟩ ╰┤ ⟨Z ⊗ Z⟩ 0: ──RX(0.531)──╭●──RY(-0.4)──╭┤ ⟨Z ⊗ X⟩ 1: ──RY(0.9)────╰Z────────────╰┤ ⟨Z ⊗ X⟩ 0: ──RX(0.531)──╭●──RY(-0.4)──╭┤ ⟨Z ⊗ Y⟩ 1: ──RY(0.9)────╰Z────────────╰┤ ⟨Z ⊗ Y⟩ 0: ──RX(0.3)──╭Z──╭┤ ⟨Z ⊗ Z⟩ 1: ──I────────╰●──╰┤ ⟨Z ⊗ Z⟩ 0: ──RX(0.3)──╭Z──╭┤ ⟨Z ⊗ Z⟩ 1: ──X────────╰●──╰┤ ⟨Z ⊗ Z⟩ 0: ──RX(0.3)──╭Z──╭┤ ⟨Z ⊗ Z⟩ 1: ──H────────╰●──╰┤ ⟨Z ⊗ Z⟩ 0: ──RX(0.3)─────╭Z──╭┤ ⟨Z ⊗ Z⟩ 1: ──H────────S──╰●──╰┤ ⟨Z ⊗ Z⟩
The last step is to execute the tapes and postprocess the results using
qcut_processing_fn(), which processes the results to the original full circuit output via a tensor network contraction
>>> results = qml.execute(tapes, dev, gradient_fn=None) >>> qml.transforms.qcut.qcut_processing_fn( ... results, ... communication_graph, ... prepare_nodes, ... measure_nodes, ... ) 0.47165198882111165
- What is PennyLane?
- Quantum circuits
- Gradients and training
- Quantum operators
- Inspecting circuits
- Compiling circuits
- Quantum Chemistry