StrawberryFields 0.20.0 on Python PyPI

New features since last release

The generic multimode Gaussian gate Ggate is now available in the sf.ops module with the backend choice of tf. The N mode Ggate can be parametrized by a real symplectic matrix S (size 2N * 2N) and a diplacement vector d (size N). You can also obtain the gradients of the Ggate gate via TensorFlow's tape.gradient (#599) (#606)

from thewalrus.random import random_symplectic

num_mode = 2
cutoff = 10
S = tf.Variable(random_symplectic(num_mode))
d = tf.Variable(np.random.random(2 * num_mode))

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

with prog.context as q:
    sf.ops.Ggate(S, d) | (q[0], q[1])

state_out = eng.run(prog).state.ket()

Note that in order to update the parameter S by using its gradient, you cannot use gradient descent directly (as the unitary would not be symplectic after the update). Please use the function sf.backends.tfbackend.update_symplectic which is designed specifically for this purpose.

def overlap_loss(state, objective):
    return -tf.abs(tf.reduce_sum(tf.math.conj(state) * objective)) ** 2

def norm_loss(state):
    return -tf.abs(tf.linalg.norm(state)) ** 2

def loss(state, objective):
    return overlap_loss(state, objective) + norm_loss(state)

num_mode = 1
cutoff = 10

S = tf.Variable(random_symplectic(num_mode))
d = tf.Variable(np.random.random(2 * num_mode))
kappa = tf.Variable(0.3)
objective = tf.Variable(np.eye(cutoff)[1], dtype=tf.complex64)

adam = tf.keras.optimizers.Adam(learning_rate=0.01)
eng = sf.Engine("tf", backend_options={"cutoff_dim": cutoff})
prog = sf.Program(1)

with prog.context as q:
    sf.ops.Ggate(S, d) | q
    sf.ops.Kgate(kappa) | q

loss_vals = []
for _ in range(200):
    with tf.GradientTape() as tape:
        state_out = eng.run(prog).state.ket()
        loss_val = loss(state_out, objective)

    eng.reset()
    grad_S, gradients_d, gradients_kappa = tape.gradient(loss_val, [S, d, kappa])
    adam.apply_gradients(zip([gradients_d, gradients_kappa], [d, kappa]))
    update_symplectic(S, grad_S, lr=0.1)  # update S here
    loss_vals.append(loss_val)

Breaking Changes

Complex parameters of the Catstate operation are expected in polar form as two separate real parameters. (#441)

The sf CLI has been removed in favour of the Xanadu Cloud Client. (#642)

Configuring account credentials using:

Strawberry Fields v0.19.0
```
$ sf configure --token "foo"
```

Strawberry Fields v0.20.0

$ xcc config set REFRESH_TOKEN "foo"
Successfully updated REFRESH_TOKEN setting to 'foo'.

Verifying your connection to the Xanadu Cloud using:

Strawberry Fields v0.19.0

$ sf --ping
You have successfully authenticated to the platform!

Strawberry Fields v0.20.0

$ xcc ping
Successfully connected to the Xanadu Cloud.

Submitting a Blackbird circuit to the Xanadu Cloud using:

Strawberry Fields v0.19.0

$ # Version 0.19.0
$ sf run "foo.xbb"
Executing program on remote hardware...
2021-11-02 03:04:05,06 - INFO - The device spec X8_01 has been successfully retrieved.
2021-11-02 03:04:05,07 - INFO - Compiling program for device X8_01 using compiler Xunitary.
2021-11-02 03:04:05,08 - INFO - Job b185a63c-f302-4adb-acf8-b6e4e413c11d was successfully submitted.
2021-11-02 03:04:05,09 - INFO - The remote job b185a63c-f302-4adb-acf8-b6e4e413c11d has been completed.
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]

Strawberry Fields v0.20.0

$ xcc job submit --name "bar" --target "X8_01" --circuit "$(cat foo.xbb)"
{
    "id": "0b0f5a46-46d8-4157-8005-45a4764361ba",  # Use this ID below.
    "name": "bar",
    "status": "open",
    "target": "X8_01",
    "language": "blackbird:1.0",
    "created_at": "2021-11-02 03:04:05,10",
    "finished_at": null,
    "running_time": null,
    "metadata": {}
}
$ xcc job get 0b0f5a46-46d8-4157-8005-45a4764361ba --result
{
    "output": [
        "[[0 0 0 0]\n[0 0 0 0]\n[0 0 0 0]\n[0 0 0 0]]"
    ]
}

The sf.api.Connection class has been replaced with the xcc.Connection class. (#645)

Previously, in Strawberry Fields v0.19.0, an sf.RemoteEngine can be instantiated with a custom Xanadu Cloud connection as follows:

import strawberryfields as sf
import strawberryfields.api

connection = strawberryfields.api.Connection(
  token="Xanadu Cloud API key goes here",
  host="platform.strawberryfields.ai",
  port=443,
  use_ssl=True,
)
engine = sf.RemoteEngine("X8", connection=connection)

In Strawberry Fields v0.20.0, the same result can be achieved using

import strawberryfields as sf
import xcc

connection = xcc.Connection(
  refresh_token="Xanadu Cloud API key goes here",  # See "token" argument above.
  host="platform.strawberryfields.ai",
  port=443,
  tls=True,                                        # See "use_ssl" argument above.
)
engine = sf.RemoteEngine("X8", connection=connection)

The sf.configuration module has been replaced with the xcc.Settings class. (#649)

This means that Xanadu Cloud credentials are now stored in exactly one location, the path to which depends on your operating system:

Windows: C:\Users\%USERNAME%\AppData\Local\Xanadu\xanadu-cloud\.env
MacOS: /home/$USER/Library/Application\ Support/xanadu-cloud/.env
Linux: /home/$USER/.config/xanadu-cloud/.env

The format of the configuration file has also changed to .env and the names of some fields have been updated. For example,

# Strawberry Fields v0.19.0 (config.toml)
[api]
authentication_token = "Xanadu Cloud API key goes here"
hostname = "platform.strawberryfields.ai"
port = 443
use_ssl = true

is equivalent to

# Strawberry Fields v0.20.0 (.env)
XANADU_CLOUD_REFRESH_TOKEN='Xanadu Cloud API key goes here'
XANADU_CLOUD_HOST='platform.strawberryfields.ai'
XANADU_CLOUD_PORT=443
XANADU_CLOUD_TLS=True

Similarly, the names of the configuration environment variables have changed from

# Strawberry Fields v0.19.0
export SF_API_AUTHENTICATION_TOKEN="Xanadu Cloud API key goes here"
export SF_API_HOSTNAME="platform.strawberryfields.ai"
export SF_API_PORT=443
export SF_API_USE_SSL=true

# Strawberry Fields v0.20.0
export XANADU_CLOUD_REFRESH_TOKEN="Xanadu Cloud API key goes here"
export XANADU_CLOUD_HOST="platform.strawberryfields.ai"
export XANADU_CLOUD_PORT=443
export XANADU_CLOUD_TLS=true

Finally, strawberryfields.store_account() has been replaced such that

# Strawberry Fields v0.19.0
import strawberryfields as sf
sf.store_account("Xanadu Cloud API key goes here")

becomes

# Strawberry Fields v0.20.0
import xcc
xcc.Settings(REFRESH_TOKEN="Xanadu Cloud API key goes here").save()

The sf.api.Job class has been replaced with the xcc.Job class. (#650)
A Job object is returned when running jobs asynchronously. In previous versions of Strawberry Fields (v0.19.0 and lower), the Job object can be used as follows:
```
>>> job = engine.run_async(program, shots=1)
>>> job.status
'queued'
>>> job.result
InvalidJobOperationError
>>> job.refresh()
>>> job.status
'complete'
>>> job.result
[[0 1 0 2 1 0 0 0]]
```
In Strawberry Fields v0.20.0, the Job object works slightly differently:
```
>>> job = engine.run_async(program, shots=1)
>>> job.status
'queued'
>>> job.wait()
>>> job.status
'complete'
>>> job.result
{'output': [array([[0 1 0 2 1 0 0 0]])]}
```
The job.wait() method is a blocking method that will wait for the job to finish. Alternatively, job.clear() can be called to clear the cache, allowing job.status to re-fetch the job status.
The sf.api.Result class has been updated to support the Xanadu Cloud Client integration. (#651)
While Result.samples should return the same type and shape as before, the Result.all_samples property has been renamed to Result.samples_dict and returns the samples as a dictionary with corresponding measured modes as keys.
```
>>> res = eng.run(prog, shots=3)
>>> res.samples
array([[1, 0], [0, 1], [1, 1]])
>>> res.samples_dict
{0: [np.array([1, 0, 1])], 1: [np.array([0, 1, 1])]}
```
The samples dictionary is only accessible for simulators.
The sf.api.DeviceSpec class has been updated to support the Xanadu Cloud Client integration. (#644)
It now works as a container for a device specification dictionary. There are no more API connection usages, and DeviceSpec.target is retrieved from the device specification rather than passed at initialization.
The api subpackage has been removed and the contained DeviceSpec and Result classes have been moved to the root strawberryfields folder. (#652)
They can now be imported as follows:
```
import strawberryfields  as sf
# sf.DeviceSpec
# sf.Result
```

Documentation

Strawberry Fields interactive has been removed from the documentation. (#635)

Contributors

This release contains contributions from (in alphabetical order):

Mikhail Andrenkov, Sebastián Duque Mesa, Theodor Isacsson, Josh Izaac, Filippo Miatto, Nicolás Quesada, Antal Száva, Yuan Yao.

StrawberryFields 0.20.0 Release 0.20.0 on Python PyPI

New features since last release

Breaking Changes

Documentation

Contributors

StrawberryFields 0.20.0
Release 0.20.0

on Python PyPI