diff --git a/circuit_knitting/cutting/__init__.py b/circuit_knitting/cutting/__init__.py index b5ed7b54e..b8f65f9eb 100644 --- a/circuit_knitting/cutting/__init__.py +++ b/circuit_knitting/cutting/__init__.py @@ -21,7 +21,7 @@ :toctree: ../stubs/ :nosignatures: - transform_cuts_to_moves + cut_wires expand_observables partition_circuit_qubits partition_problem @@ -88,7 +88,7 @@ ) from .cutting_evaluation import execute_experiments, CuttingExperimentResults from .cutting_reconstruction import reconstruct_expectation_values -from .cut_wire_to_move import transform_cuts_to_moves, expand_observables +from .wire_cutting_transforms import cut_wires, expand_observables __all__ = [ "partition_circuit_qubits", @@ -99,6 +99,6 @@ "reconstruct_expectation_values", "PartitionedCuttingProblem", "CuttingExperimentResults", - "transform_cuts_to_moves", + "cut_wires", "expand_observables", ] diff --git a/circuit_knitting/cutting/cut_wire_to_move.py b/circuit_knitting/cutting/wire_cutting_transforms.py similarity index 80% rename from circuit_knitting/cutting/cut_wire_to_move.py rename to circuit_knitting/cutting/wire_cutting_transforms.py index bf79bd4f1..31fb841af 100644 --- a/circuit_knitting/cutting/cut_wire_to_move.py +++ b/circuit_knitting/cutting/wire_cutting_transforms.py @@ -13,17 +13,40 @@ """Function to transform a :class:`.CutWire` instruction to a :class:`.Move` instruction.""" from __future__ import annotations +from typing import Callable from itertools import groupby import numpy as np -from qiskit.circuit import Qubit, QuantumCircuit +from qiskit.circuit import Qubit, QuantumCircuit, Operation from qiskit.circuit.exceptions import CircuitError from qiskit.quantum_info import PauliList -from .instructions.move import Move +from .instructions import Move +from .qpd.instructions import TwoQubitQPDGate -def transform_cuts_to_moves(circuit: QuantumCircuit, /) -> QuantumCircuit: +def cut_wires(circuit: QuantumCircuit, /) -> QuantumCircuit: + r"""Transform all :class:`.CutWire` instructions in a circuit to :class:`.Move` instructions marked for cutting. + + The returned circuit will have one newly allocated qubit for every :class:`.CutWire` instruction. + + See Sec. 3 and Appendix A of `2302.03366v1 + `__ for more information about the two + different representations of wire cuts: single-qubit (:class:`.CutWire`) + vs. two-qubit (:class:`.Move`). + + Args: + circuit: Original circuit with :class:`.CutWire` instructions + + Returns: + circuit: New circuit with :class:`.CutWire` instructions replaced by :class:`.Move` instructions wrapped in :class:`TwoQubitQPDGate`\ s + """ + return _transform_cut_wires( + circuit, lambda: TwoQubitQPDGate.from_instruction(Move()) + ) + + +def _transform_cuts_to_moves(circuit: QuantumCircuit, /) -> QuantumCircuit: """Transform all :class:`.CutWire` instructions in a circuit to :class:`.Move` instructions. Args: @@ -32,6 +55,12 @@ def transform_cuts_to_moves(circuit: QuantumCircuit, /) -> QuantumCircuit: Returns: circuit: New circuit with :class:`.CutWire` instructions replaced by :class`.Move` instructions """ + return _transform_cut_wires(circuit, Move) + + +def _transform_cut_wires( + circuit: QuantumCircuit, factory: Callable[[], Operation], / +) -> QuantumCircuit: new_circuit, mapping = _circuit_structure_mapping(circuit) for instructions in circuit.data: @@ -40,7 +69,7 @@ def transform_cuts_to_moves(circuit: QuantumCircuit, /) -> QuantumCircuit: if instructions in circuit.get_instructions("cut_wire"): # Replace cut_wire with move instruction new_circuit.compose( - other=Move(), + other=factory(), qubits=[mapping[gate_index[0]], mapping[gate_index[0]] + 1], inplace=True, ) diff --git a/docs/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb b/docs/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb index f45c893c2..5da0f3a7a 100644 --- a/docs/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb +++ b/docs/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb @@ -29,7 +29,7 @@ ")\n", "\n", "from circuit_knitting.cutting.instructions import CutWire\n", - "from circuit_knitting.cutting import transform_cuts_to_moves, expand_observables" + "from circuit_knitting.cutting import cut_wires, expand_observables" ] }, { @@ -137,9 +137,9 @@ "source": [ "### Transform cuts to moves\n", "\n", - "The next step is to transform each `CutWire` into a `Move`. An additional qubit is added to the circuit for each `CutWire` in the input circuit.\n", + "The next step is to call `cut_wires`, which transforms each `CutWire` into a `TwoQubitQPDGate` which wraps a `Move` instruction. An additional qubit is added to the circuit for each `CutWire` in the input circuit.\n", "\n", - "Notice that, unlike in the [wire cutting tutorial](../tutorials/03_wire_cutting_via_move_instruction.ipynb), this function does not result in the _re_-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse)." + "Notice that, unlike in the [wire cutting tutorial](../tutorials/03_wire_cutting_via_move_instruction.ipynb), where `Move` operations were placed manually, this function does not result in the _re_-use of a qubit. Because any method for qubit re-use is based on heuristics, this function naively allocates an additional qubit for each cut. Users wishing to re-use qubits might wish to experiment with [qiskit-qubit-reuse](https://github.com/qiskit-community/qiskit-qubit-reuse)." ] }, { @@ -150,7 +150,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -161,7 +161,7 @@ } ], "source": [ - "qc_1 = transform_cuts_to_moves(qc_0)\n", + "qc_1 = cut_wires(qc_0)\n", "qc_1.draw(\"mpl\")" ] }, @@ -206,7 +206,7 @@ "source": [ "### Separate the circuit and observables\n", "\n", - "In order to partition the circuit, we must specify `partition_labels` based on the connectivity of the circuit. In the future, we expect to provide a way for this to be determined automatically, as it is technically redundant with the information contained by the original circuit with `CutWire` instructions (see PR [#367](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/367))." + "In this case, `partition_labels` need not be passed to `partition_problem`, as the labels can be determined automatically from the connectivity of the circuit." ] }, { @@ -216,9 +216,7 @@ "metadata": {}, "outputs": [], "source": [ - "partitioned_problem = partition_problem(\n", - " circuit=qc_1, partition_labels=\"AAAABABBB\", observables=observables_1\n", - ")\n", + "partitioned_problem = partition_problem(circuit=qc_1, observables=observables_1)\n", "subcircuits = partitioned_problem.subcircuits\n", "subobservables = partitioned_problem.subobservables" ] @@ -250,8 +248,8 @@ { "data": { "text/plain": [ - "{'A': PauliList(['IIIII', 'ZIIII', 'IIIIZ']),\n", - " 'B': PauliList(['ZIII', 'IIII', 'IIII'])}" + "{0: PauliList(['IIIII', 'ZIIII', 'IIIIZ']),\n", + " 1: PauliList(['ZIII', 'IIII', 'IIII'])}" ] }, "execution_count": 8, @@ -282,7 +280,7 @@ } ], "source": [ - "subcircuits[\"A\"].draw(\"mpl\")" + "subcircuits[0].draw(\"mpl\")" ] }, { @@ -304,7 +302,7 @@ } ], "source": [ - "subcircuits[\"B\"].draw(\"mpl\")" + "subcircuits[1].draw(\"mpl\")" ] }, { @@ -354,10 +352,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reconstructed expectation values: [0.16293341, 0.69796044, 0.71675336]\n", + "Reconstructed expectation values: [0.17901284, 0.70971423, 0.68885177]\n", "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n", - "Errors in estimation: [-0.01384329, -0.00914634, 0.00964658]\n", - "Relative errors in estimation: [-0.07830945, -0.01293488, 0.01364233]\n" + "Errors in estimation: [0.00223614, 0.00260745, -0.01825501]\n", + "Relative errors in estimation: [0.01264952, 0.00368749, -0.02581648]\n" ] } ], diff --git a/docs/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb b/docs/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb index 758852220..659cc27e9 100644 --- a/docs/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb +++ b/docs/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb @@ -127,9 +127,11 @@ "source": [ "### Create a new circuit where `Move` instructions have been placed at the desired cut locations\n", "\n", - "Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this (currently, the only way) is to place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed.\n", + "Given the above circuit, we would like to place two wire cuts on the middle qubit line, so that the circuit can separate into two circuits of four qubits each. One way to do this is to manually place two-qubit `Move` instructions that move the state from one qubit wire to another. A `Move` instruction is conceptually equivalent to a reset operation on the second qubit, followed by a SWAP gate. The effect of this instruction is to transfer the state of the first (source) qubit to the second (detination) qubit, while discarding the incoming state of the second qubit. For this to work as intended, it is important that the second (destination) qubit share no entanglement with the remainder of the system; otherwise, the reset operation will cause the state of the remainder of the system to be partially collapsed.\n", "\n", - "Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation." + "Here, we build a new circuit with one additional qubit and the `Move` operations in place. In this example, we are able to reuse a qubit: the source qubit of the first `Move` becomes the destination qubit of the second `Move` operation.\n", + "\n", + "Note: As an alternative to working directly with `Move` instructions, one may choose to mark wire cuts using a single-qubit `CutWire` instruction. The `cut_wires` function exists to transform `CutWire`s to `Move` instructions on newly allocated qubits. However, in contrast to the manual method, this automatic method does not allow for the re-use of qubit wires. See the `CutWire` [how-to guide](../how-tos/how_to_specify_cut_wires.ipynb) for details." ] }, { diff --git a/test/cutting/test_cut_wire_to_move.py b/test/cutting/test_wire_cutting_transforms.py similarity index 92% rename from test/cutting/test_cut_wire_to_move.py rename to test/cutting/test_wire_cutting_transforms.py index 851a20726..2869dad8f 100644 --- a/test/cutting/test_cut_wire_to_move.py +++ b/test/cutting/test_wire_cutting_transforms.py @@ -17,7 +17,9 @@ from qiskit.circuit import QuantumCircuit, QuantumRegister, Qubit, ClassicalRegister from qiskit.quantum_info import PauliList from circuit_knitting.cutting.instructions import Move, CutWire -from circuit_knitting.cutting import transform_cuts_to_moves, expand_observables +from circuit_knitting.cutting.qpd.instructions import TwoQubitQPDGate +from circuit_knitting.cutting import cut_wires, expand_observables +from circuit_knitting.cutting.wire_cutting_transforms import _transform_cuts_to_moves @fixture @@ -207,7 +209,7 @@ def resulting_circuit4() -> tuple[QuantumCircuit, list[int]]: ) def test_transform_cuts_to_moves(request, sample_circuit, resulting_circuit): """Tests the transformation of CutWire to Move instruction.""" - assert request.getfixturevalue(resulting_circuit)[0] == transform_cuts_to_moves( + assert request.getfixturevalue(resulting_circuit)[0] == _transform_cuts_to_moves( request.getfixturevalue(sample_circuit) ) @@ -226,7 +228,7 @@ def test_circuit_mapping(request, sample_circuit, resulting_circuit): sample_circuit = request.getfixturevalue(sample_circuit) resulting_mapping = request.getfixturevalue(resulting_circuit)[1] - final_circuit = transform_cuts_to_moves(sample_circuit) + final_circuit = _transform_cuts_to_moves(sample_circuit) final_mapping = [ final_circuit.find_bit(qubit).index for qubit in sample_circuit.qubits ] @@ -249,7 +251,7 @@ def test_circuit_mapping(request, sample_circuit, resulting_circuit): def test_qreg_name_num(request, sample_circuit): """Tests the number and name of qregs in initial and final circuits.""" sample_circuit = request.getfixturevalue(sample_circuit) - final_circuit = transform_cuts_to_moves(sample_circuit) + final_circuit = _transform_cuts_to_moves(sample_circuit) # Tests number of qregs in initial and final circuits assert len(sample_circuit.qregs) == len(final_circuit.qregs) @@ -273,7 +275,7 @@ def test_qreg_name_num(request, sample_circuit): def test_qreg_size(request, sample_circuit): """Tests the size of qregs in initial and final circuits.""" sample_circuit = request.getfixturevalue(sample_circuit) - final_circuit = transform_cuts_to_moves(sample_circuit) + final_circuit = _transform_cuts_to_moves(sample_circuit) # Tests size of qregs in initial and final circuits for sample_qreg, final_qreg in zip( @@ -295,7 +297,7 @@ def test_qreg_size(request, sample_circuit): def test_circuit_width(request, sample_circuit): """Tests the width of the initial and final circuits.""" sample_circuit = request.getfixturevalue(sample_circuit) - final_circuit = transform_cuts_to_moves(sample_circuit) + final_circuit = _transform_cuts_to_moves(sample_circuit) total_cut_wire = len(sample_circuit.get_instructions("cut_wire")) # Tests width of initial and final circuit @@ -314,7 +316,7 @@ def test_circuit_width(request, sample_circuit): def test_creg(request, sample_circuit): """Tests the number and size of cregs in the initial and final circuits.""" sample_circuit = request.getfixturevalue(sample_circuit) - final_circuit = transform_cuts_to_moves(sample_circuit) + final_circuit = _transform_cuts_to_moves(sample_circuit) # Tests number of cregs in initial and final circuits assert len(sample_circuit.cregs) == len(final_circuit.cregs) @@ -326,6 +328,19 @@ def test_creg(request, sample_circuit): assert sample_creg.size == final_creg.size +def test_cut_wires(): + qc = QuantumCircuit(2) + qc.h(0) + qc.h(1) + qc.append(CutWire(), [1]) + qc.s(0) + qc.s(1) + qc_out = cut_wires(qc) + qpd_gate = qc_out.data[2].operation + assert isinstance(qpd_gate, TwoQubitQPDGate) + assert qpd_gate.label == "cut_move" + + class TestExpandObservables: def test_expand_observables(self): qc0 = QuantumCircuit(3)