Source code for pennylane.transforms.dynamic_one_shot

# Copyright 2018-2024 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contains the batch dimension transform.
"""

import itertools

# pylint: disable=import-outside-toplevel
from collections import Counter
from collections.abc import Sequence

import numpy as np

import pennylane as qml
from pennylane.measurements import (
    CountsMP,
    ExpectationMP,
    MeasurementValue,
    MidMeasureMP,
    ProbabilityMP,
    SampleMP,
    VarianceMP,
)
from pennylane.tape import QuantumScript, QuantumScriptBatch
from pennylane.typing import PostprocessingFn, TensorLike

from .core import transform

fill_in_value = np.iinfo(np.int32).min


def is_mcm(operation):
    """Returns True if the operation is a mid-circuit measurement and False otherwise."""
    mcm = isinstance(operation, MidMeasureMP)
    return mcm or "MidCircuitMeasure" in str(type(operation))


def null_postprocessing(results):
    """A postprocessing function returned by a transform that only converts the batch of results
    into a result for a single ``QuantumTape``.
    """
    return results[0]


@transform
def dynamic_one_shot(tape: QuantumScript, **kwargs) -> tuple[QuantumScriptBatch, PostprocessingFn]:
    """Transform a QNode to into several one-shot tapes to support dynamic circuit execution.

    This transform enables the ``"one-shot"`` mid-circuit measurement method. The ``"one-shot"`` method prompts the
    device to perform a series of one-shot executions, where in each execution, the ``qml.measure``
    operation applies a probabilistic mid-circuit measurement to the circuit.
    This is in contrast with ``qml.defer_measurement``, which instead introduces an extra
    wire for each mid-circuit measurement. The ``"one-shot"`` method is favourable in the few-shots
    and several-mid-circuit-measurements limit, whereas ``qml.defer_measurements`` is favourable in
    the opposite limit.

    Args:
        tape (QNode or QuantumScript or Callable): a quantum circuit.

    Returns:
        qnode (QNode) or quantum function (Callable) or tuple[List[QuantumScript], function]:

        The transformed circuit as described in :func:`qml.transform <pennylane.transform>`.
        This circuit will provide the results of a dynamic execution.

    **Example**

    Most devices that support mid-circuit measurements will include this transform in its
    preprocessing automatically when applicable. When this is the case, any user-applied
    ``dynamic_one_shot`` transforms will be ignored. The recommended way to use dynamic one
    shot is to specify ``mcm_method="one-shot"`` in the ``qml.qnode`` decorator.

    .. code-block:: python

        dev = qml.device("default.qubit", shots=100)
        params = np.pi / 4 * np.ones(2)

        @qml.qnode(dev, mcm_method="one-shot", postselect_mode="fill-shots")
        def func(x, y):
            qml.RX(x, wires=0)
            m0 = qml.measure(0)
            qml.cond(m0, qml.RY)(y, wires=1)
            return qml.expval(op=m0)

    """
    if not any(is_mcm(o) for o in tape.operations):
        return (tape,), null_postprocessing

    for m in tape.measurements:
        if not isinstance(m, (CountsMP, ExpectationMP, ProbabilityMP, SampleMP, VarianceMP)):
            raise TypeError(
                f"Native mid-circuit measurement mode does not support {type(m).__name__} "
                "measurements."
            )
    _ = kwargs.get("device", None)

    if not tape.shots:
        raise qml.QuantumFunctionError("dynamic_one_shot is only supported with finite shots.")

    samples_present = any(isinstance(mp, SampleMP) for mp in tape.measurements)
    postselect_present = any(op.postselect is not None for op in tape.operations if is_mcm(op))
    if postselect_present and samples_present and tape.batch_size is not None:
        raise ValueError(
            "Returning qml.sample is not supported when postselecting mid-circuit "
            "measurements with broadcasting"
        )

    if (batch_size := tape.batch_size) is not None:
        tapes, broadcast_fn = qml.transforms.broadcast_expand(tape)
    else:
        tapes = [tape]
        broadcast_fn = None

    aux_tapes = [init_auxiliary_tape(t) for t in tapes]

    postselect_mode = kwargs.get("postselect_mode", None)

    def reshape_data(array):
        return qml.math.squeeze(qml.math.vstack(array))

    def processing_fn(results, has_partitioned_shots=None, batched_results=None):
        if batched_results is None and batch_size is not None:
            # If broadcasting, recursively process the results for each batch. For each batch
            # there are tape.shots.total_shots results. The length of the first axis of final_results
            # will be batch_size.
            final_results = []
            for result in results:
                final_results.append(processing_fn((result,), batched_results=False))
            return broadcast_fn(final_results)

        if has_partitioned_shots is None and tape.shots.has_partitioned_shots:
            # If using shot vectors, recursively process the results for each shot bin. The length
            # of the first axis of final_results will be the length of the shot vector.
            results = list(results[0])
            final_results = []
            for s in tape.shots:
                final_results.append(
                    processing_fn(results[0:s], has_partitioned_shots=False, batched_results=False)
                )
                del results[0:s]
            return tuple(final_results)
        if not tape.shots.has_partitioned_shots:
            results = results[0]

        is_scalar = not isinstance(results[0], Sequence)
        if is_scalar:
            results = [reshape_data(tuple(results))]
        else:
            results = [
                reshape_data(tuple(res[i] for res in results)) for i, _ in enumerate(results[0])
            ]
        return parse_native_mid_circuit_measurements(
            tape, aux_tapes, results, postselect_mode=postselect_mode
        )

    return aux_tapes, processing_fn


def get_legacy_capabilities(dev):
    """Gets the capabilities dictionary of a device."""
    assert isinstance(dev, qml.devices.LegacyDeviceFacade)
    return dev.target_device.capabilities()


def _supports_one_shot(dev: "qml.devices.Device"):
    """Checks whether a device supports one-shot."""

    if isinstance(dev, qml.devices.LegacyDevice):
        return get_legacy_capabilities(dev).get("supports_mid_measure", False)

    return dev.name in ("default.qubit", "lightning.qubit") or (
        dev.capabilities is not None and "one-shot" in dev.capabilities.supported_mcm_methods
    )


@dynamic_one_shot.custom_qnode_transform
def _dynamic_one_shot_qnode(self, qnode, targs, tkwargs):
    """Custom qnode transform for ``dynamic_one_shot``."""
    if tkwargs.get("device", None):
        raise ValueError(
            "Cannot provide a 'device' value directly to the dynamic_one_shot decorator "
            "when transforming a QNode."
        )
    if qnode.device is not None:
        if not _supports_one_shot(qnode.device):
            raise TypeError(
                f"Device {qnode.device.name} does not support mid-circuit measurements and/or "
                "one-shot execution mode natively, and hence it does not support the "
                "dynamic_one_shot transform. 'default.qubit' and 'lightning.qubit' currently "
                "support mid-circuit measurements and the dynamic_one_shot transform."
            )
    tkwargs.setdefault("device", qnode.device)
    return self.default_qnode_transform(qnode, targs, tkwargs)


def init_auxiliary_tape(circuit: qml.tape.QuantumScript):
    """Creates an auxiliary circuit to perform one-shot mid-circuit measurement calculations.

    Measurements are replaced by SampleMP measurements on wires and observables found in the
    original measurements.

    Args:
        circuit (QuantumTape): The original QuantumScript

    Returns:
        QuantumScript: A copy of the circuit with modified measurements
    """
    new_measurements = []
    for m in circuit.measurements:
        if m.mv is None:
            if isinstance(m, VarianceMP):
                new_measurements.append(SampleMP(obs=m.obs))
            else:
                new_measurements.append(m)
    for op in circuit.operations:
        if "MidCircuitMeasure" in str(type(op)):  # pragma: no cover
            new_measurements.append(qml.sample(op.out_classical_tracers[0]))
        elif isinstance(op, MidMeasureMP):
            new_measurements.append(qml.sample(MeasurementValue([op], lambda res: res)))
    return qml.tape.QuantumScript(
        circuit.operations,
        new_measurements,
        shots=[1] * circuit.shots.total_shots,
        trainable_params=circuit.trainable_params,
    )


# pylint: disable=too-many-branches,too-many-statements
def parse_native_mid_circuit_measurements(
    circuit: qml.tape.QuantumScript,
    aux_tapes: qml.tape.QuantumScript,
    results: TensorLike,
    postselect_mode=None,
):
    """Combines, gathers and normalizes the results of native mid-circuit measurement runs.

    Args:
        circuit (QuantumTape): The original ``QuantumScript``.
        aux_tapes (List[QuantumTape]): List of auxiliary ``QuantumScript`` objects.
        results (TensorLike): Array of measurement results.

    Returns:
        tuple(TensorLike): The results of the simulation.
    """

    def measurement_with_no_shots(measurement):
        return (
            np.nan * np.ones_like(measurement.eigvals())
            if isinstance(measurement, ProbabilityMP)
            else np.nan
        )

    interface = qml.math.get_deep_interface(results)
    interface = "numpy" if interface == "builtins" else interface
    interface = "tensorflow" if interface == "tf" else interface
    active_qjit = qml.compiler.active()

    all_mcms = [op for op in aux_tapes[0].operations if is_mcm(op)]
    n_mcms = len(all_mcms)
    mcm_samples = qml.math.hstack(
        tuple(qml.math.reshape(res, (-1, 1)) for res in results[-n_mcms:])
    )
    mcm_samples = qml.math.array(mcm_samples, like=interface)
    # Can't use boolean dtype array with tf, hence why conditionally setting items to 0 or 1
    has_postselect = qml.math.array(
        [[op.postselect is not None for op in all_mcms]],
        like=interface,
        dtype=mcm_samples.dtype,
    )
    postselect = qml.math.array(
        [[0 if op.postselect is None else op.postselect for op in all_mcms]],
        like=interface,
        dtype=mcm_samples.dtype,
    )
    is_valid = qml.math.all(mcm_samples * has_postselect == postselect, axis=1)
    has_valid = qml.math.any(is_valid)
    mid_meas = [op for op in circuit.operations if is_mcm(op)]
    mcm_samples = [mcm_samples[:, i : i + 1] for i in range(n_mcms)]
    mcm_samples = dict((k, v) for k, v in zip(mid_meas, mcm_samples))
    normalized_meas = []
    m_count = 0
    for m in circuit.measurements:
        if not isinstance(m, (CountsMP, ExpectationMP, ProbabilityMP, SampleMP, VarianceMP)):
            raise TypeError(
                f"Native mid-circuit measurement mode does not support {type(m).__name__} measurements."
            )
        if interface != "jax" and m.mv is not None and not has_valid:
            meas = measurement_with_no_shots(m)
        elif m.mv is not None and active_qjit:
            meas = gather_mcm_qjit(
                m, mcm_samples, is_valid, postselect_mode=postselect_mode
            )  # pragma: no cover
        elif m.mv is not None:
            meas = gather_mcm(m, mcm_samples, is_valid, postselect_mode=postselect_mode)
        elif interface != "jax" and not has_valid:
            meas = measurement_with_no_shots(m)
            m_count += 1
        else:
            result = results[m_count]
            if not isinstance(m, CountsMP):
                # We don't need to cast to arrays when using qml.counts. qml.math.array is not viable
                # as it assumes all elements of the input are of builtin python types and not belonging
                # to any particular interface
                result = qml.math.array(result, like=interface)
            if active_qjit:  # pragma: no cover
                # `result` contains (bases, counts) need to return (basis, sum(counts)) where `is_valid`
                # Any row of `result[0]` contains basis, so we return `result[0][0]`
                # We return the sum of counts (`result[1]`) weighting by `is_valid`, which is `0` for invalid samples
                if isinstance(m, CountsMP):
                    normalized_meas.append(
                        (
                            result[0][0],
                            qml.math.sum(result[1] * qml.math.reshape(is_valid, (-1, 1)), axis=0),
                        )
                    )
                    m_count += 1
                    continue
                result = qml.math.squeeze(result)
            meas = gather_non_mcm(m, result, is_valid, postselect_mode=postselect_mode)
            m_count += 1
        if isinstance(m, SampleMP):
            meas = qml.math.squeeze(meas)
        normalized_meas.append(meas)

    return tuple(normalized_meas) if len(normalized_meas) > 1 else normalized_meas[0]


def gather_mcm_qjit(measurement, samples, is_valid, postselect_mode=None):  # pragma: no cover
    """Process MCM measurements when the Catalyst compiler is active.

    Args:
        measurement (MeasurementProcess): measurement
        samples (dict): Mid-circuit measurement samples
        is_valid (TensorLike): Boolean array with the same shape as ``samples`` where the value at
            each index specifies whether or not the respective sample is valid.

    Returns:
        TensorLike: The combined measurement outcome
    """
    found, meas = False, None
    for k, meas in samples.items():
        if measurement.mv is k.out_classical_tracers[0]:
            found = True
            break
    if not found:
        raise LookupError("MCM not found")
    meas = qml.math.squeeze(meas)
    if isinstance(measurement, (CountsMP, ProbabilityMP)):
        interface = qml.math.get_interface(is_valid)
        sum_valid = qml.math.sum(is_valid)
        count_1 = qml.math.sum(meas * is_valid)
        if isinstance(measurement, CountsMP):
            return qml.math.array([0, 1], like=interface), qml.math.array(
                [sum_valid - count_1, count_1], like=interface
            )
        if isinstance(measurement, ProbabilityMP):
            counts = qml.math.array([sum_valid - count_1, count_1], like=interface)
            return counts / sum_valid
    return gather_non_mcm(measurement, meas, is_valid, postselect_mode=postselect_mode)


def gather_non_mcm(measurement, samples, is_valid, postselect_mode=None):
    """Combines, gathers and normalizes several measurements with trivial measurement values.

    Args:
        measurement (MeasurementProcess): measurement
        samples (TensorLike): Post-processed measurement samples
        is_valid (TensorLike): Boolean array with the same shape as ``samples`` where the value at
            each index specifies whether or not the respective sample is valid.

    Returns:
        TensorLike: The combined measurement outcome
    """
    if isinstance(measurement, CountsMP):
        tmp = Counter()

        if measurement.all_outcomes:
            if isinstance(measurement.mv, Sequence):
                values = [list(m.branches.values()) for m in measurement.mv]
                values = list(itertools.product(*values))
                tmp = Counter({"".join(map(str, v)): 0 for v in values})
            else:
                values = [list(measurement.mv.branches.values())]
                values = list(itertools.product(*values))
                tmp = Counter({float(*v): 0 for v in values})

        for i, d in enumerate(samples):
            tmp.update(
                {k if isinstance(k, str) else float(k): v * is_valid[i] for k, v in d.items()}
            )

        if not measurement.all_outcomes:
            tmp = Counter({k: v for k, v in tmp.items() if v > 0})
        return dict(sorted(tmp.items()))

    if isinstance(measurement, SampleMP):
        if postselect_mode == "pad-invalid-samples" and samples.ndim == 2:
            is_valid = qml.math.reshape(is_valid, (-1, 1))
        if postselect_mode == "pad-invalid-samples":
            return qml.math.where(is_valid, samples, fill_in_value)
        if qml.math.shape(samples) == ():  # single shot case
            samples = qml.math.reshape(samples, (-1, 1))
        return samples[is_valid]

    if (interface := qml.math.get_interface(is_valid)) == "tensorflow":
        # Tensorflow requires arrays that are used for arithmetic with each other to have the
        # same dtype. We don't cast if measuring samples as float tf.Tensors cannot be used to
        # index other tf.Tensors (is_valid is used to index valid samples).
        is_valid = qml.math.cast_like(is_valid, samples)

    if isinstance(measurement, ExpectationMP):
        return qml.math.sum(samples * is_valid) / qml.math.sum(is_valid)
    if isinstance(measurement, ProbabilityMP):
        return qml.math.sum(samples * qml.math.reshape(is_valid, (-1, 1)), axis=0) / qml.math.sum(
            is_valid
        )

    # VarianceMP
    expval = qml.math.sum(samples * is_valid) / qml.math.sum(is_valid)
    if interface == "tensorflow":
        # Casting needed for tensorflow
        samples = qml.math.cast_like(samples, expval)
        is_valid = qml.math.cast_like(is_valid, expval)
    return qml.math.sum((samples - expval) ** 2 * is_valid) / qml.math.sum(is_valid)


def gather_mcm(measurement, samples, is_valid, postselect_mode=None):
    """Combines, gathers and normalizes several measurements with non-trivial measurement values.

    Args:
        measurement (MeasurementProcess): measurement
        samples (List[dict]): Mid-circuit measurement samples
        is_valid (TensorLike): Boolean array with the same shape as ``samples`` where the value at
            each index specifies whether or not the respective sample is valid.

    Returns:
        TensorLike: The combined measurement outcome
    """
    interface = qml.math.get_deep_interface(is_valid)
    mv = measurement.mv
    # The following block handles measurement value lists, like ``qml.counts(op=[mcm0, mcm1, mcm2])``.
    if isinstance(measurement, (CountsMP, ProbabilityMP, SampleMP)) and isinstance(mv, Sequence):
        mcm_samples = [m.concretize(samples) for m in mv]
        mcm_samples = qml.math.concatenate(mcm_samples, axis=1)
        if isinstance(measurement, ProbabilityMP):
            values = [list(m.branches.values()) for m in mv]
            values = list(itertools.product(*values))
            values = [qml.math.array([v], like=interface, dtype=mcm_samples.dtype) for v in values]
            # Need to use boolean functions explicitly as Tensorflow does not allow integer math
            # on boolean arrays
            counts = [
                qml.math.count_nonzero(
                    qml.math.logical_and(qml.math.all(mcm_samples == v, axis=1), is_valid)
                )
                for v in values
            ]
            counts = qml.math.array(counts, like=interface)
            return counts / qml.math.sum(counts)
        if isinstance(measurement, CountsMP):
            mcm_samples = [{"".join(str(int(v)) for v in tuple(s)): 1} for s in mcm_samples]
        return gather_non_mcm(measurement, mcm_samples, is_valid, postselect_mode=postselect_mode)
    mcm_samples = qml.math.ravel(qml.math.array(mv.concretize(samples), like=interface))
    if isinstance(measurement, ProbabilityMP):
        # Need to use boolean functions explicitly as Tensorflow does not allow integer math
        # on boolean arrays
        counts = [
            qml.math.count_nonzero(qml.math.logical_and((mcm_samples == v), is_valid))
            for v in list(mv.branches.values())
        ]
        counts = qml.math.array(counts, like=interface)
        return counts / qml.math.sum(counts)
    if isinstance(measurement, CountsMP):
        mcm_samples = [{float(s): 1} for s in mcm_samples]
    return gather_non_mcm(measurement, mcm_samples, is_valid, postselect_mode=postselect_mode)

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