# Source code for pennylane.templates.subroutines.approx_time_evolution

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
r"""
Contains the ApproxTimeEvolution template.
"""
# pylint: disable-msg=too-many-branches,too-many-arguments,protected-access
import pennylane as qml
from pennylane.operation import Operation, AnyWires
from pennylane.ops import PauliRot

[docs]class ApproxTimeEvolution(Operation):
r"""Applies the Trotterized time-evolution operator for an arbitrary Hamiltonian, expressed in terms
of Pauli gates.

.. note::

We recommend using :class:~.TrotterProduct as the more general operation for approximate
matrix exponentiation. One can recover the behaviour of :class:~.ApproxTimeEvolution by

The general time-evolution operator for a time-independent Hamiltonian is given by

.. math:: U(t) \ = \ e^{-i H t},

for some Hamiltonian of the form:

.. math:: H \ = \ \displaystyle\sum_{j} H_j.

Implementing this unitary with a set of quantum gates is difficult, as the terms :math:H_j don't
necessarily commute with one another. However, we are able to exploit the Trotter-Suzuki decomposition formula,

.. math:: e^{A \ + \ B} \ = \ \lim_{n \to \infty} \Big[ e^{A/n} e^{B/n} \Big]^n,

to implement an approximation of the time-evolution operator as

.. math:: U \ \approx \ \displaystyle\prod_{k \ = \ 1}^{n} \displaystyle\prod_{j} e^{-i H_j t / n},

with the approximation becoming better for larger :math:n.
The circuit implementing this unitary is of the form:

.. figure:: ../../_static/templates/subroutines/approx_time_evolution.png
:align: center
:width: 60%
:target: javascript:void(0);

It is also important to note that
this decomposition is exact for any value of :math:n when each term of the Hamiltonian
commutes with every other term.

.. warning::

The Trotter-Suzuki decomposition depends on the order of the summed observables. Two mathematically identical :class:~.Hamiltonian objects may undergo different time evolutions
due to the order in which those observables are stored.

.. note::

This template uses the :class:~.PauliRot operation in order to implement
exponentiated terms of the input Hamiltonian. This operation only takes
terms that are explicitly written in terms of products of Pauli matrices (:class:~.PauliX,
:class:~.PauliY, :class:~.PauliZ, and :class:~.Identity).
Thus, each term in the Hamiltonian must be expressed this way upon input, or else an error will be raised.

Args:
hamiltonian (.Hamiltonian): The Hamiltonian defining the
time-evolution operator.
The Hamiltonian must be explicitly written
in terms of products of Pauli gates (:class:~.PauliX, :class:~.PauliY,
:class:~.PauliZ, and :class:~.Identity).
time (int or float): The time of evolution, namely the parameter :math:t in :math:e^{- i H t}.
n (int): The number of Trotter steps used when approximating the time-evolution operator.

.. seealso:: :class:~.TrotterProduct.

.. details::
:title: Usage Details

The template is used inside a qnode:

.. code-block:: python

import pennylane as qml
from pennylane import ApproxTimeEvolution

n_wires = 2
wires = range(n_wires)

dev = qml.device('default.qubit', wires=n_wires)

coeffs = [1, 1]
obs = [qml.X(0), qml.X(1)]
hamiltonian = qml.Hamiltonian(coeffs, obs)

@qml.qnode(dev)
def circuit(time):
ApproxTimeEvolution(hamiltonian, time, 1)
return [qml.expval(qml.Z(i)) for i in wires]

>>> circuit(1)
"""

num_wires = AnyWires

def _flatten(self):
h = self.hyperparameters["hamiltonian"]
data = (h, self.data[-1])
return data, (self.hyperparameters["n"],)

@classmethod

def __init__(self, hamiltonian, time, n, id=None):
if getattr(hamiltonian, "pauli_rep", None) is None:
raise ValueError(
f"hamiltonian must be a linear combination of pauli words, got {type(hamiltonian).__name__}"
)

# extract the wires that the op acts on
wires = hamiltonian.wires

self._hyperparameters = {"hamiltonian": hamiltonian, "n": n}

# trainable parameters are passed to the base init method
super().__init__(*hamiltonian.data, time, wires=wires, id=id)

[docs]    def queue(self, context=qml.QueuingManager):
context.remove(self.hyperparameters["hamiltonian"])
context.append(self)
return self

[docs]    @staticmethod
def compute_decomposition(
*coeffs_and_time, wires, hamiltonian, n
):  # pylint: disable=arguments-differ,unused-argument
r"""Representation of the operator as a product of other operators.

.. math:: O = O_1 O_2 \dots O_n.

.. seealso:: :meth:~.ApproxTimeEvolution.decomposition.

Args:
*coeffs_and_time (TensorLike): coefficients of the Hamiltonian, appended by the time.
wires (Any or Iterable[Any]): wires that the operator acts on
hamiltonian (.Hamiltonian): The Hamiltonian defining the
time-evolution operator. The Hamiltonian must be explicitly written
in terms of products of Pauli gates (:class:~.PauliX, :class:~.PauliY,
:class:~.PauliZ, and :class:~.Identity).
n (int): The number of Trotter steps used when approximating the time-evolution operator.

Returns:
list[.Operator]: decomposition of the operator

.. code-block:: python

import pennylane as qml
from pennylane import ApproxTimeEvolution

num_qubits = 2

hamiltonian = qml.Hamiltonian(
[0.1, 0.2, 0.3], [qml.Z(0) @ qml.Z(1), qml.X(0), qml.X(1)]
)

evolution_time = 0.5
trotter_steps = 1

coeffs_and_time = [*hamiltonian.coeffs, evolution_time]

>>> ApproxTimeEvolution.compute_decomposition(
...     *coeffs_and_time, wires=range(num_qubits), n=trotter_steps, hamiltonian=hamiltonian
... )
[PauliRot(0.1, ZZ, wires=[0, 1]), PauliRot(0.2, X, wires=[0]), PauliRot(0.3, X, wires=[1])]
"""
time = coeffs_and_time[-1]

single_round = []
with qml.QueuingManager.stop_recording():
for pw, coeff in hamiltonian.pauli_rep.items():
if len(pw) == 0:
continue
theta = 2 * time * coeff / n
term_str = "".join(pw.values())
wires = qml.wires.Wires(pw.keys())
single_round.append(PauliRot(theta, term_str, wires=wires))

full_decomp = single_round * n
if qml.QueuingManager.recording():
_ = [qml.apply(op) for op in full_decomp]

return full_decomp


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