New features
* Ray-based distributed trainer is now added to `training.trainer`. It acts as a replacement
for `for` loops and enables the parallelization of running many circuits as well as their
optimizations. To install the extra dependencies: `pip install .[ray]`.
[(194)](https://github.com/XanaduAI/MrMustard/pull/194)
python
from mrmustard.lab import Vacuum, Dgate, Ggate
from mrmustard.physics import fidelity
from mrmustard.training.trainer import map_trainer
def make_circ(x=0.):
return Ggate(num_modes=1, symplectic_trainable=True) >> Dgate(x=x, x_trainable=True, y_trainable=True)
def cost_fn(circ=make_circ(0.1), y_targ=0.):
target = Gaussian(1) >> Dgate(-1.5, y_targ)
s = Vacuum(1) >> circ
return -fidelity(s, target)
Use case 0: Calculate the cost of a randomly initialized circuit 5 times without optimizing it.
results_0 = map_trainer(
cost_fn=cost_fn,
tasks=5,
)
Use case 1: Run circuit optimization 5 times on randomly initialized circuits.
results_1 = map_trainer(
cost_fn=cost_fn,
device_factory=make_circ,
tasks=5,
max_steps=50,
symplectic_lr=0.05,
)
Use case 2: Run circuit optimization 2 times on randomly initialized circuits with custom parameters.
results_2 = map_trainer(
cost_fn=cost_fn,
device_factory=make_circ,
tasks=[
{'x': 0.1, 'euclidean_lr': 0.005, 'max_steps': 50, 'HBAR': 1.},
{'x': -0.7, 'euclidean_lr': 0.1, 'max_steps': 2, 'HBAR': 2.},
],
y_targ=0.35,
symplectic_lr=0.05,
AUTOCUTOFF_MAX_CUTOFF=7,
)
* Sampling for homodyne measurements is now integrated in Mr Mustard: when no measurement outcome
value is specified by the user, a value is sampled from the reduced state probability distribution
and the conditional state on the remaining modes is generated.
[(143)](https://github.com/XanaduAI/MrMustard/pull/143)
python
import numpy as np
from mrmustard.lab import Homodyne, TMSV, SqueezedVacuum
conditional state from measurement
conditional_state = TMSV(r=0.5, phi=np.pi)[0, 1] >> Homodyne(quadrature_angle=np.pi/2)[1]
measurement outcome
measurement_outcome = SqueezedVacuum(r=0.5) >> Homodyne()
* The optimizer `minimize` method now accepts an optional callback function, which will be called
at each step of the optimization and it will be passed the step number, the cost value,
and the value of the trainable parameters. The result is added to the `callback_history`
attribute of the optimizer.
[(175)](https://github.com/XanaduAI/MrMustard/pull/175)
* the Math interface now supports linear system solving via `math.solve`.
[(185)](https://github.com/XanaduAI/MrMustard/pull/185)
* We introduce the tensor wrapper `MMTensor` (available in `math.mmtensor`) that allows for
a very easy handling of tensor contractions. Internally MrMustard performs lots of tensor
contractions and this wrapper allows one to label each index of a tensor and perform
contractions using the `` symbol as if it were a simple matrix multiplication (the indices
with the same name get contracted).
[(185)](https://github.com/XanaduAI/MrMustard/pull/185)<br>
[(195)](https://github.com/XanaduAI/MrMustard/pull/195)
python
from mrmustard.math.mmtensor import MMTensor
define two tensors
A = MMTensor(np.random.rand(2, 3, 4), axis_labels=["foo", "bar", "contract"])
B = MMTensor(np.random.rand(4, 5, 6), axis_labels=["contract", "baz", "qux"])
perform a tensor contraction
C = A B
C.axis_labels ["foo", "bar", "baz", "qux"]
C.shape (2, 3, 5, 6)
C.tensor extract actual result
* MrMustard's settings object (accessible via `from mrmustard import settings`) now supports
`SEED` (an int). This will give reproducible results whenever randomness is involved.
The seed is assigned randomly by default, and it can be reassigned again by setting it to None:
`settings.SEED = None`. If one desires, the seeded random number generator is accessible directly
via `settings.rng` (e.g. `settings.rng.normal()`).
[(183)](https://github.com/XanaduAI/MrMustard/pull/183)
* The `Circuit` class now has an ascii representation, which can be accessed via the repr method.
It looks great in Jupyter notebooks! There is a new option at `settings.CIRCUIT_DECIMALS`
which controls the number of decimals shown in the ascii representation of the gate parameters.
If `None`, only the name of the gate is shown.
[(196)](https://github.com/XanaduAI/MrMustard/pull/196)
* PNR sampling from Gaussian circuits using density matrices can now be performed faster.
When all modes are detected, this is done by replacing `math.hermite_renormalized` by `math.hermite_renormalized_diagonal`. If all but the first mode are detected,
`math.hermite_renormalized_1leftoverMode` can be used.
The complexity of these new methods is equal to performing a pure state simulation.
The methods are differentiable, so that they can be used for defining a cost function.
[(154)](https://github.com/XanaduAI/MrMustard/pull/154)
* MrMustard repo now provides a fully furnished vscode development container and a Dockerfile. To
find out how to use dev containers for development check the documentation
[here](https://code.visualstudio.com/docs/devcontainers/containers).
[(214)](https://github.com/XanaduAI/MrMustard/pull/214)
Breaking changes
Improvements
* The `Dgate` is now implemented directly in MrMustard (instead of on The Walrus) to calculate the
unitary and gradients of the displacement gate in Fock representation, providing better numerical
stability for larger cutoff and displacement values.
[(147)](https://github.com/XanaduAI/MrMustard/pull/147)
[(211)](https://github.com/XanaduAI/MrMustard/pull/211)
* Now the Wigner function is implemented in its own module and uses numba for speed.
[(171)](https://github.com/XanaduAI/MrMustard/pull/171)
python
from mrmustard.utils.wigner import wigner_discretized
W, Q, P = wigner_discretized(dm, q, p) dm is a density matrix
* Calculate marginals independently from the Wigner function thus ensuring that the marginals are
physical even though the Wigner function might not contain all the features of the state
within the defined window. Also, expose some plot parameters and return the figure and axes.
[(179)](https://github.com/XanaduAI/MrMustard/pull/179)
* Allows for full cutoff specification (index-wise rather than mode-wise) for subclasses
of `Transformation`. This allows for a more compact Fock representation where needed.
[(181)](https://github.com/XanaduAI/MrMustard/pull/181)
* The `mrmustard.physics.fock` module now provides convenience functions for applying kraus
operators and choi operators to kets and density matrices.
[(180)](https://github.com/XanaduAI/MrMustard/pull/180)
python
from mrmustard.physics.fock import apply_kraus_to_ket, apply_kraus_to_dm, apply_choi_to_ket, apply_choi_to_dm
ket_out = apply_kraus_to_ket(kraus, ket_in, indices)
dm_out = apply_choi_to_dm(choi, dm_in, indices)
dm_out = apply_kraus_to_dm(kraus, dm_in, indices)
dm_out = apply_choi_to_ket(choi, ket_in, indices)
* Replaced norm with probability in the repr of `State`. This improves consistency over the
old behaviour (norm was the sqrt of prob if the state was pure and prob if the state was mixed).
[(182)](https://github.com/XanaduAI/MrMustard/pull/182)
* Added two new modules (`physics.bargmann` and `physics.husimi`) to host the functions related
to those representations, which have been refactored and moved out of `physics.fock`.
[(185)](https://github.com/XanaduAI/MrMustard/pull/185)
* The internal type system in MrMustard has been beefed up with much clearer types, like ComplexVector,
RealMatrix, etc... as well as a generic type `Batch`, which can be parametrized using the other types,
like `Batch[ComplexTensor]`. This will allow for better type checking and better error messages.
[(199)](https://github.com/XanaduAI/MrMustard/pull/199)
* Added multiple tests and improved the use of Hypothesis.
[(191)](https://github.com/XanaduAI/MrMustard/pull/191)
* The `fock.autocutoff` function now uses the new diagonal methods for calculating a
probability-based cutoff. Use `settings.AUTOCUTOFF_PROBABILITY` to set the probability threshold.
[(203)](https://github.com/XanaduAI/MrMustard/pull/203)
* The unitary group optimization (for the interferometer) and the orthogonal group optimization
(for the real interferometer) have been added. The symplectic matrix that describes an
interferometer belongs to the intersection of the orthogonal group and the symplectic group,
which is a unitary group, so we needed both.
[(208)](https://github.com/XanaduAI/MrMustard/pull/208)
Bug fixes
* The `Dgate` and the `Rgate` now correctly parse the case when a single scalar is intended
as the same parameter of a number of gates in parallel.
[(180)](https://github.com/XanaduAI/MrMustard/pull/180)
* The trace function in the fock module was giving incorrect results when called with certain
choices of modes. This is now fixed.
[(180)](https://github.com/XanaduAI/MrMustard/pull/180)
* The purity function for fock states no longer normalizes the density matrix before computing
the purity.
[(180)](https://github.com/XanaduAI/MrMustard/pull/180)
* The function `dm_to_ket` no longer normalizes the density matrix before diagonalizing it.
[(180)](https://github.com/XanaduAI/MrMustard/pull/180)
* The internal fock representation of states returns the correct cutoffs in all cases
(solves an issue when a pure dm was converted to ket).
[(184)](https://github.com/XanaduAI/MrMustard/pull/184)
* The ray related tests were hanging in github action causing tests to halt and fail.
Now ray is forced to init with 1 cpu when running tests preventing the issue.
[(201)](https://github.com/XanaduAI/MrMustard/pull/201)
* Various minor bug fixes.
[(202)](https://github.com/XanaduAI/MrMustard/pull/202)
* Fixed the issue that the optimization of the interferometer was using orthogonal group
optimization rather than unitary.
[(208)](https://github.com/XanaduAI/MrMustard/pull/208)
* Fixes a slicing issue that arises when we compute the fidelity between gaussian and fock states.
[(210)](https://github.com/XanaduAI/MrMustard/pull/210)
* The sign of parameters in the circuit drawer are now displayed correctly.
[(209)](https://github.com/XanaduAI/MrMustard/pull/209)
* Fixed a bug in the Gaussian state which caused its covariance matrix to be multiplied
by hbar/2 twice. Adds the argument `modes` to `Ggate`.
[(212)](https://github.com/XanaduAI/MrMustard/pull/212)
* Fixes a bug in the cutoffs of the choi operator.
[(216)](https://github.com/XanaduAI/MrMustard/pull/216)
Documentation
Contributors
This release contains contributions from (in alphabetical order):
[Robbe De Prins](https://github.com/rdprins) (rdprins), [Sebastian Duque Mesa](https://github.com/sduquemesa) (sduquemesa),
[Filippo Miatto](https://github.com/ziofil) (ziofil), [Zeyue Niu](https://github.com/zeyueN) (zeyueN),
[Yuan Yao](https://github.com/sylviemonet) (sylviemonet)