# Measurements and post-selection¶

In this tutorial, we will walk through the use of measurement operators in Strawberry Fields, and how they may be used to perform post-selection. Be sure to read through the introductory teleportation tutorial before attempting this tutorial.

## Measurement operators¶

The Blackbird programming language supports the following measurement operations:

Measurement

Operation

Shortcuts

Backend Support

Homodyne detection of quadrature angle $$\phi$$

MeasureHomodyne(phi=0)

MeasureX MeasureP

"fock", "tf", "gaussian"

Heterodyne detection

MeasureHeterodyne()

MeasureHD

gaussian

Photon-counting

MeasureFock()

None

"fock", "tf", "gaussian"

Note

While all backends support homodyne detection, the Gaussian backend is the only backend to support heterodyne detection. On the other hand, Fock-basis measurements are supported in all backends, though the Gaussian backend does not update the post-measurement quantum state, which would be non-Gaussian.

The measurement operators are used in the same manner as all other quantum transformation operations in Blackbird:

MeasurementOperator | (q[0], q[1], q[2], ...)

where the left-hand side represents the measurement operator (along with any required or optional arguments), and the right-hand side signifies the modes which are to be measured.

To see how this works in practice, consider the following circuit, where two incident Fock states $$\ket{n}$$ and $$\ket{m}$$ are directed on a beamsplitter, with two photon detectors at the output modes.

Due to the definition of the beamsplitter, we know that it preserves the photon number of the system; thus, the two output states $$\ket{n'}$$ and $$\ket{m'}$$ must be such that $$n+m=n'+m'$$.

Constructing this circuit in Strawberry Fields with $$n=2,~m=3$$, let’s perform only the first Fock measurement.

import numpy as np

# set the random seed
np.random.seed(42)

import strawberryfields as sf
from strawberryfields.ops import *

prog = sf.Program(2)
eng = sf.Engine("fock", backend_options={"cutoff_dim": 6})

with prog.context as q:
Fock(2)       | q[0]
Fock(3)       | q[1]
BSgate()      | (q[0], q[1])
MeasureFock() | q[0]

results = eng.run(prog)


Note

If the BSgate parameters are not specified, by default a 50-50 beamsplitter BSgate(pi/4,0) is applied.

The default action after every measurement is to reset the measured modes to the vacuum state. However, we can extract the measured value of mode q[0] via the results object returned by the engine after it has finished execution:

print(results.samples[0][0])


Out:

1


Note

Since measurement is a stochastic process, your results might differ when executing this code.

Since no measurement has yet been applied to the second mode, results.samples[0][1] will return None.

print(results.samples)


Out:

[[1 None]]


Therefore, we know that, to preserve the photon number, q[1] must be in the state $$\ket{m+n-k}$$, where $$m$$ and $$n$$ are the photon numbers of the initial states and $$k$$ is value returned in result.samples. Executing the backend again, and this time applying the second Fock measurement:

prog2 = sf.Program(2)
with prog2.context as q:
MeasureFock() | q[1]

results = eng.run(prog2)


We will find that the second measurement yields $$m+n-k$$. In this case, we get

print(results.samples[0][1])


Out:

4


## Post-selection¶

In addition, StrawberryFields also allows the specification or post-selection of a required measurement output, and will condition the remaining unmeasured modes based on this post-selected value. When applying the measurement operators, the optional keyword argument select can be passed to the operator. The value should be an integer (or list of integers) for MeasureFock, a float for MeasureHomodyne, and a complex value for MeasureHeterodyne.

For example, we can rewrite the example above using post-selection:

prog = sf.Program(2)
eng = sf.Engine("fock", backend_options={"cutoff_dim": 6})

with prog.context as q:
Fock(2) | q[0]
Fock(3) | q[1]
BSgate() | (q[0], q[1])
MeasureFock(select=0) | q[0]
MeasureFock() | q[1]

result = eng.run(prog)


Since we are post-selecting a measurement of 0 photons in mode q[0], we expect result.samples[0] to be 0 and result.samples[1] to be 5. Indeed,

print(result.samples)


Out:

[[0 5]]


Warning

If we attempt to post-select on Fock measurement results that have zero probability given the circuit/state of the simulation, the Fock backend returns a ZeroDivisionError. For example, in the previous code snippet, if we instead attempt to post-select two values that do not preserve the photon number,

>>> eng.run("fock", cutoff_dim=6, select=[1,2])
ZeroDivisionError: Measurement has zero probability.


This check is provided for convenience, but the user should always be aware of post-selecting on zero-probability events. The current implementation of homodyne measurements in the Fock backend does not currently perform this check.

## Example¶

Consider the following circuit:

Here, we have two vacuum states incident on a two-mode squeezed gate. Homodyne detection in the $$x$$ quadrature of the first output mode is then performed; as a result, the output mode q[1] is conditionally displaced depending on the measured value.

We can simulate this conditional displacement using post-selection. Utilizing the Gaussian backend, the above circuit can be simulated in Strawberry Fields as follows:

prog = sf.Program(2)
eng = sf.Engine("gaussian")

with prog.context as q:
S2gate(1)                    | (q[0], q[1])
MeasureHomodyne(0, select=1)  | q[0]

state = eng.run(prog).state


To check the displacement of the second output mode, we can use the reduced_gaussian() state method to extract the vector of means and the covariance matrix:

mu, cov = state.reduced_gaussian([1])


The vector of means contains the mean quadrature displacements, and for a single mode is of the form $$\bar{\mathbf{r}} = (\bar{\mathbf{x}}, \bar{\mathbf{p}})$$. Therefore, looking at the first index of the vector of means for q[1]:

print(mu[0])


Out:

0.9640275698261913


The $$x$$ quadrature displacement of the second mode is conditional to the post-selected value in the circuit construction above.

## Measurement control and processing¶

In addition to the features already explored above, Strawberry Fields also allows the measurement results of qumodes to be used as subsequent gate parameters. This is simple and intuitive as well - simply pass the register referencing the measured mode as the gate argument, for example like

MeasureX | q[0]

Rgate(q[0]) | q[1]

and the Strawberry Fields engine will, in the background, ensure that the measured value of that mode is used as the gate parameter during the circuit simulation.

Note that, the return type of the measurement determines the parameter type, potentially restricting the resulting gates which can be measurement-controlled.

Measurement

Return type

Gates with matching parameter type

MeasureHomodyne(phi=0)

Real number

All

MeasureHeterodyne()

Complex number

MeasureFock()

Integer

All

## Classical processing¶

Sometimes, additional classical processing needs to be performed on the measured value before using it as a gate parameter; Strawberry Fields provides the ability to perform simple classical processing by simply accessing the .par attribute of the measured parameter.

These only need to be used when passing a measured mode value as a gate parameter. For example, if we wish to perform a Fock measurement on a mode, and then use the measured value to perform a controlled displacement on other modes, we could do the following:

prog = sf.Program(2)
with prog.context as q:
MeasureFock()        | q[0]
Dgate(q[0].par ** 2) | q[1]


In addition, the Strawberry Fields maths module can be used to transform measured mode values within gate arguments:

prog = sf.Program(2)
with prog.context as q:
MeasureFock()        | q[0]
Dgate(sf.math.cos(q[0].par) + 2) | q[1]


Note

Only use the functions with sf.math within the Blackbird code/program context. Outside of the program context, you may use standard Python/NumPy/TensorFlow math functions.

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