"""
********************************************************************************
* Copyright (c) 2025 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.core import h
from qrisp.alg_primitives import QPE
[docs]
def quantum_counting(qv, oracle, precision):
"""
This algorithm estimates the amount of solutions for a given Grover oracle.
Parameters
----------
qv : QuantumVariable
The QuantumVariable on which to evaluate.
oracle : function
The oracle function.
precision : int
The precision to perform the quantum phase estimation with.
Returns
-------
M : float
An estimate of the amount of solutions.
Examples
--------
We create an oracle, which performs a simple phase flip on the last qubit. ::
from qrisp import quantum_counting, z, QuantumVariable
def oracle(qv):
z(qv[-1])
We expect half of the state-space of the input to be a solution.
For 3 qubits, the state space is $2^3 = 8$ dimensional.
>>> quantum_counting(QuantumVariable(3), oracle, 3)
3.999999999999999
For 4 qubits, the state space is $2^4 = 16$ dimensional.
>>> quantum_counting(QuantumVariable(4), oracle, 3)
7.999999999999998
"""
from qrisp import gate_wrap, measure
from qrisp.grover import diffuser
from qrisp.jasp import check_for_tracing_mode
@gate_wrap
def grover_operator(qv):
oracle(qv)
diffuser(qv)
h(qv)
res = QPE(qv, grover_operator, precision=precision)
if check_for_tracing_mode():
mes_res = measure(res)
import jax.numpy as jnp
else:
mes_res = list(res.get_measurement().keys())[0]
import numpy as jnp
theta = mes_res * jnp.pi
N = 2**qv.size
M = N * jnp.sin(theta) ** 2
return M
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