"""
\********************************************************************************
* Copyright (c) 2023 the Qrisp authors
*
* This program and the accompanying materials are made available under the
* terms of the Eclipse Public License 2.0 which is available at
* http://www.eclipse.org/legal/epl-2.0.
*
* This Source Code may also be made available under the following Secondary
* Licenses when the conditions for such availability set forth in the Eclipse
* Public License, v. 2.0 are satisfied: GNU General Public License, version 2
* with the GNU Classpath Exception which is
* available at https://www.gnu.org/software/classpath/license.html.
*
* SPDX-License-Identifier: EPL-2.0 OR GPL-2.0 WITH Classpath-exception-2.0
********************************************************************************/
"""
from qrisp.alg_primitives.qpe import QPE
from qrisp.alg_primitives.amplitude_amplification import amplitude_amplification
[docs]
def QAE(args, state_function, oracle_function, kwargs_oracle={}, precision=None, target=None):
r"""
This method implements the canonical quantum amplitude estimation (QAE) algorithm by `Brassard et al. <https://arxiv.org/abs/quant-ph/0005055>`_.
The problem of quantum amplitude estimation is described as follows:
* Given a unitary operator :math:`\mathcal{A}`, let :math:`\ket{\Psi}=\mathcal{A}\ket{0}`.
* Write :math:`\ket{\Psi}=\ket{\Psi_1}+\ket{\Psi_0}` as a superposition of the orthogonal good and bad components of :math:`\ket{\Psi}`.
* Find an estimate for :math:`a=\langle\Psi_1|\Psi_1\rangle`, the probability that a measurement of $\ket{\Psi}$ yields a good state.
Parameters
----------
args : QuantumVariable or list[QuantumVariable]
The (list of) QuantumVariables which represent the state,
the quantum amplitude estimation is performed on.
state_function : function
A Python function preparing the state :math:`\ket{\Psi}`.
This function will receive the variables in the list ``args`` as arguments in the
course of this algorithm.
oracle_function : function
A Python function tagging the good state :math:`\ket{\Psi_1}`.
This function will receive the variables in the list ``args`` as arguments in the
course of this algorithm.
kwargs_oracle : dict, optional
A dictionary containing keyword arguments for the oracle. The default is {}.
precision : int, optional
The precision of the estimation. The default is None.
target : QuantumFloat, optional
A target QuantumFloat to perform the estimation into. The default is None.
If given neither a precision nor a target, an Exception will be raised.
Returns
-------
res : QuantumFloat
A QuantumFloat encoding the angle :math:`\theta` as a fraction of :math:`\pi`,
such that :math:`\tilde{a}=\sin^2(\theta)` is an estimate for :math:`a`.
More precisely, we have :math:`\theta=\pi\frac{y}{M}` for :math:`y\in\{0,\dotsc,M-1\}` and :math:`M=2^{\text{precision}}`.
After measurement, the estimate :math:`\tilde{a}=\sin^2(\theta)` satisfies
.. math::
|a-\tilde{a}|\leq\frac{2\pi}{M}+\frac{\pi^2}{M^2}
with probability of at least :math:`8/\pi^2`.
Examples
--------
We define a function that prepares the state :math:`\ket{\Psi}=\cos(\frac{\pi}{8})\ket{0}+\sin(\frac{\pi}{8})\ket{1}`
and an oracle that tags the good state :math:`\ket{1}`. In this case, we have :math:`a=\sin^2(\frac{\pi}{8})`.
::
from qrisp import z, ry, QuantumBool, QAE
import numpy as np
def state_function(qb):
ry(np.pi/4,qb)
def oracle_function(qb):
z(qb)
qb = QuantumBool()
res = QAE([qb], state_function, oracle_function, precision=3)
>>> res.get_measurement()
{0.125: 0.5, 0.875: 0.5}
That is, after measurement we find $\theta=\frac{\pi}{8}$ or $\theta=\frac{7\pi}{8}$ with probability $\frac12$, respectively.
Therefore, we obtain the estimate $\tilde{a}=\sin^2(\frac{\pi}{8})$ or $\tilde{a}=\sin^2(\frac{7\pi}{8})$.
In this case, both results coincide with the exact value $a$.
**Numerical integration**
Here, we demonstarate how to use QAE for numerical integration.
Consider a continuous function $f\colon[0,1]\rightarrow[0,1]$. We wish to evaluate
.. math::
A=\int_0^1f(x)\mathrm dx.
For this, we set up the corresponding ``state_function`` acting on the ``input_list``:
::
from qrisp import QuantumFloat, QuantumBool, control, z, h, ry, QAE
import numpy as np
n = 6
inp = QuantumFloat(n,-n)
tar = QuantumBool()
input_list = [inp, tar]
Here, $N=2^n$ is the number of sampling points the function $f$ is evaluated on. The ``state_function`` acts as
.. math::
\ket{0}\ket{0}\rightarrow\frac{1}{\sqrt{N}}\sum\limits_{x=0}^{N-1}\ket{x}\left(\sqrt{1-f(x/N)}\ket{0}+\sqrt{f(x/N)}\ket{1}\right).
Then the probability of measuring $1$ in the target state ``tar`` is
.. math::
p(1)=\frac{1}{N}\sum\limits_{x=0}^{N-1}f(x/N),
which acts as an approximation for the value of the integral $A$.
The ``oracle_function``, therefore, tags the $\ket{1}$ state of the target state:
::
def oracle_function(inp, tar):
z(tar)
For example, if $f(x)=\sin^2(x)$ the ``state_function`` can be implemented as follows:
::
def state_function(inp, tar):
h(inp)
N = 2**inp.size
for k in range(inp.size):
with control(inp[k]):
ry(2**(k+1)/N,tar)
Finally, we apply QAE and obtain an estimate $a$ for the value of the integral $A=0.27268$.
::
prec = 6
res = QAE(input_list, state_function, oracle_function, precision=prec)
meas_res = res.get_measurement()
theta = np.pi*max(meas_res, key=meas_res.get)
a = np.sin(theta)**2
>>> a
0.26430
"""
state_function(*args)
res = QPE(args, amplitude_amplification, precision, target, iter_spec=True,
kwargs={'state_function':state_function, 'oracle_function':oracle_function, 'kwargs_oracle':kwargs_oracle})
return res
