Source code for qrisp.jasp.program_control.ev
"""
\********************************************************************************
* 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
********************************************************************************/
"""
import jax
import jax.numpy as jnp
from qrisp.jasp.tracing_logic import quantum_kernel
# The following function implements the expectation_value feature.
# The basic functionality would be relatively straightforward to implement,
# however there are some complications. The reason for that is that the resulting
# jaxpr should be "readable" by the terminal sampling interpreter.
# Terminal sampling means that instead of performing the simulations "shots"-times
# it is performed once and the shots are then sampled from that distribution.
# Naturally this implies a massive performance increase, which is why a lot
# of effort is spent to realize a smooth implementation.
# The underlying idea to make the feature easily "readable" by the terminal
# sampling interpreter is to structure one iteration of sampling into three
# steps.
# 1. Evaluating the user function, which generates the distribution.
# 2. Sampling from that distribution via the "measure" function.
# 3. Decoding and postprocessing the measurement results.
# For the final two steps we deploy some custom logic to realize the terminal
# sampling behavior. To simplify the automatic processing of these steps,
# we capture each into individual pjit calls.
# The terminal sampling interpreter then identifies each steps via the
# eqn.params["name"] attribute and executes the custom logic.
[docs]
def expectation_value(state_prep, shots, return_dict = False, post_processor = None):
r"""
The ``expectation_value`` function allows to estimate the expectation value
from a state that is specified by a preparation procedure. This preparation
procedure can be supplied via a Python function that returns one or
more :ref:`QuantumVariables <QuantumVariable>`.
Parameters
----------
state_prep : callable
A function returning one or more :ref:`QuantumVariables <QuantumVariable>`.
The expectation value from this state will be computed.
The state preparation function can only take classical values as arguments.
This is because a quantum value would need to be copied for each sampling
iteration, which is prohibited by the no-cloning theorem.
shots : int or jax.core.Tracer
The amount of samples to take to compute the expectation value.
post_processor : callable, optional
A classical Jax traceable function to apply to the results
directly after measuring. By default no post processing is applied.
Raises
------
Exception
Tried to sample from state preparation function taking a quantum value
Returns
-------
callable
A function returning a Jax array containing the expectation value.
Examples
--------
We prepare the state
.. math::
\ket{\psi_k} = \frac{1}{\sqrt{2}} \left(\ket{0}\ket{0}\ket{\text{False}} + \ket{k}\ket{k}\ket{\text{True}}\right)
::
from qrisp import *
from qrisp.jasp import *
def state_prep(k):
a = QuantumFloat(4)
b = QuantumFloat(4)
qbl = QuantumBool()
h(qbl)
with control(qbl[0]):
a[:] = k
cx(a, b)
return a, b
And compute the expectation value of the QuantumFloats
::
@jaspify
def main(k):
ev_function = expectation_value(state_prep, shots = 50)
return ev_function(k)
print(main(3))
# Yields
# [1.44 1.44]
The true value 1.5 is not reached because of `shot noise <https://en.wikipedia.org/wiki/Shot_noise>`_.
To improve the approximation, feel free to increase the shots!
To demonstrate the ``post_processor`` keyword we define a simple post processing
function
::
def post_processor(x, y):
return x*y
@jaspify
def main(k):
ev_function = expectation_value(state_prep, shots = 50)
return ev_function(k)
print(main(3))
# Yields
# 4.338
This result is expected because the inputs of ``post_processor`` are
either (0,0) or (3,3) with 50% probability, so we get
.. math::
4.5 = \frac{3\cdot 3 + 0\cdot 0}{2}
"""
from qrisp.jasp import make_tracer, qache
from qrisp.core import QuantumVariable, measure
if isinstance(shots, int):
shots = make_tracer(shots)
if post_processor is None:
def identity(*args):
return args
post_processor = identity
# Qache the user function
@qache
def user_func(*args):
return state_prep(*args)
# This function performs the logic to evaluate the expectation value
def expectation_value_eval_function(*args):
for arg in args:
if isinstance(arg, QuantumVariable):
raise Exception("Tried to sample from state preparation function taking a quantum value")
# We now construct a loop to evaluate the expectation value via adding
# the decoded and postprocessed measurement result into an accumulator.
# The following function is the loop body, which is kernelized.
@quantum_kernel
def sampling_body_func(i, args):
# Evaluate the user function
acc = args[0]
qv_tuple = user_func(*args[1:])
if not isinstance(qv_tuple, tuple):
qv_tuple = (qv_tuple,)
# Ensure all results are QuantumVariables
for qv in qv_tuple:
if not isinstance(qv, QuantumVariable):
raise Exception("Tried to sample from function not returning a QuantumVariable")
# Trace the DynamicQubitArray measurements
# Since we execute the measurements on the .reg attribute, no decoding
# is applied. The decoding happens in sampling_helper_2
@qache
def sampling_helper_1(*args):
res_list = []
for reg in args:
res_list.append(measure(reg))
return tuple(res_list)
measurement_ints = sampling_helper_1(*[qv.reg for qv in qv_tuple])
# Trace the decoding
@jax.jit
def sampling_helper_2(*meas_ints):
res_list = []
for i in range(len(qv_tuple)):
res_list.append(qv_tuple[i].jdecoder(meas_ints[i]))
# Apply the post processing
return post_processor(*res_list)
decoded_values = sampling_helper_2(*(list(measurement_ints)))
if isinstance(decoded_values, tuple) and len(decoded_values) != 1:
# Save the return amount (for more details check the comment of the)
# initialization command of return_amount
return_amount.append(len(decoded_values))
if acc.shape[0] == 1:
raise AuxException()
# Turn into jax array and add to the accumulator
meas_res = jnp.array(decoded_values)
acc += meas_res
# Return the updated accumulator for the next loop iteration.
return (acc, *args[1:])
# This list captures the amount of return values. The strategy here is
# to initially assume only one QuantumVariable is returned, which is then
# added to the expectation value accumulator. If more than one is returned,
# the amount is saved in this list and an exception is raised, which
# subsequently causes another call but this time with the correct accumulator
# dimension.
return_amount = []
try:
loop_res = jax.lax.fori_loop(0, shots, sampling_body_func, (jnp.array([0.]), *args))
return loop_res[0][0]/shots
except AuxException:
loop_res = jax.lax.fori_loop(0, shots, sampling_body_func, (jnp.array([0.]*return_amount[0]), *args))
return loop_res[0]/shots
if return_dict:
expectation_value_eval_function.__name__ = "dict_sampling_eval_function"
return jax.jit(expectation_value_eval_function)
class AuxException(Exception):
pass
