Release notes

Current develop

New features

  • Added a new Abc triple for mapping the quadrature representation into Bargmann representation. (#368)

  • Added sort function to math backends.

Breaking changes


  • Switch from the julia Python package to juliacall for easier installation and usage. (#394)

  • Save pytest timings to an S3 bucket for regression analysis. Also add a script to help visualize the timing results quickly. (#404)

Bug fixes

  • Fix the bug in the order of indices of the triples for DsMap CircuitComponent. (#385)

  • Ensure all symplectic eigenvalues are returned by the symplectic_eigenvals function.

  • Ensured support for TensorFlow 2.16+, which would be chosen when installing with pip. (#406)




Samuele Ferracin Yuan Yao Filippo Miatto Austin Lund Kasper Nielsen Matthew Silverman

Release 0.7.3 (current release)

New features

  • Added a function to_fock to map different representations into Fock representation. (#355)

  • Added a new Abc triple for s-parametrized displacement gate. (#368)

  • Added a function real_gaussian_integral as helper function to map between different representations. (#371)

Breaking changes


Bug fixes




Samuele Ferracin, Yuan Yao Filippo Miatto

Release 0.7.1

New features

  • Added functions to generate the (A, b, c) triples for the Fock-Bargmann representation of several states and gates. (#338)

  • Added support for python 3.11. (#354)

Breaking changes


Bug fixes

  • Fixing a bug in _transform_gaussian in that modifies the input state’s cov and means. (#349)

  • Fixing a bug in general_dyne in physics/ that returns the wrong probability and outcomes with given projection. (#349)




Samuele Ferracin, Yuan Yao Filippo Miatto

Release 0.7.0

New features

  • Added a new interface for backends, as well as a numpy backend (which is now default). Users can run all the functions in the utils, math, physics, and lab with both backends, while training requires using tensorflow. The numpy backend provides significant improvements both in import time and runtime. (#301)

  • Added the classes and methods to create, contract, and draw tensor networks with mrmustard.math. (#284)

  • Added functions in physics.bargmann to join and contract (A,b,c) triples. (#295)

  • Added an Ansatz abstract class and PolyExpAnsatz concrete implementation. This is used in the Bargmann representation. (#295)

  • Added complex_gaussian_integral method. (#295)

  • Added Bargmann representation (parametrized by Abc). Supports all algebraic operations and CV (exact) inner product. (#296)

Breaking changes

  • Removed circular dependencies by:

    • Removing–moved ProgressBar to training and mikkel_plot to lab.

    • Moving circuit_drawer and wigner to physics.

    • Moving xptensor to math. (#289)

  • Created file to host Settings. (#289)

  • Moved,, and to utils. (#289)

  • Removed the Math class. To use the mathematical backend, replace from mrmustard.math import Math ; math = Math() with import mrmustard.math as math in your scripts. (#301)

  • The numpy backend is now default. To switch to the tensorflow backend, add the line math.change_backend("tensorflow") to your scripts. (#301)


  • Calculating Fock representations and their gradients is now more numerically stable (i.e. numerical blowups that result from repeatedly applying the recurrence relation are postponed to higher cutoff values). This holds for both the “vanilla strategy” (#274) and for the “diagonal strategy” and “single leftover mode strategy” (#288). This is done by representing Fock amplitudes with a higher precision than complex128 (countering floating-point errors). We run Julia code via PyJulia (where Numba was used before) to keep the code fast. The precision is controlled by setting settings.PRECISION_BITS_HERMITE_POLY. The default value is 128, which uses the old Numba code. When setting to a higher value, the new Julia code is run.

  • Replaced parameters in training with Constant and Variable classes. (#298)

  • Improved how states, transformations, and detectors deal with parameters by replacing the Parametrized class with ParameterSet. (#298)

  • Includes julia dependencies into the python packaging for downstream installation reproducibility. Removes dependency on tomli to load pyproject.toml for version info, uses importlib.metadata instead. (#303) (#304)

  • Improves the algorithms implemented in vanilla and vanilla_vjp to achieve a speedup. Specifically, the improved algorithms work on flattened arrays (which are reshaped before being returned) as opposed to multi-dimensional array. (#312) (#318)

  • Adds functions hermite_renormalized_batch and hermite_renormalized_diagonal_batch to speed up calculating Hermite polynomials over a batch of B vectors. (#308)

  • Added suite to filter undesired warnings, and used it to filter tensorflow’s ComplexWarnings. (#332)

  • When re-assigning an immutable setting with the same value, no more error is raised. (#316)

Bug fixes

  • Added the missing shape input parameters to all methods U in the file. (#291)

  • Fixed inconsistent use of atol in purity evaluation for Gaussian states. (#294)

  • Fixed the documentations for loss_XYd and amp_XYd functions for Gaussian channels. (#305)

  • Replaced all instances of np.empty with np.zeros to fix instabilities. (#309)

  • Fixing a bug where scipy.linalg.sqrtm returns an unsupported type. (#337)



  • Added tests for calculating Fock amplitudes with a higher precision than complex128.


Eli Bourassa, Robbe De Prins, Samuele Ferracin, Jan Provaznik, Yuan Yao Filippo Miatto

Release 0.6.1-post1


  • Relaxes dependency versions in pyproject.toml. More specifically, this is to unpin scipy. (#300)


Filippo Miatto, Samuele Ferracin, Yuan Yao, Zeyue Niu

Release 0.6.0

New features

  • Added a new method to discretize Wigner functions that revolves Clenshaw summations. This method is expected to be fast and reliable for systems with high number of excitations, for which the pre-existing iterative method is known to be unstable. Users can select their preferred methods by setting the value of Settings.DISCRETIZATION_METHOD to either interactive (default) or clenshaw. (#280)

  • Added the PhaseNoise(phase_stdev) gate (non-Gaussian). Output is a mixed state in Fock representation. It is not based on a choi operator, but on a nonlinear transformation of the density matrix. (#275)

Breaking changes

  • The value of hbar can no longer be specified outside of Settings. All the classes and methods that allowed specifying its value as an input now retrieve it directly from Settings. (#273)

  • Certain attributes of Settings can no longer be changed after their value is queried for the first time. (#273)


  • Calculating Fock representations using the “vanilla strategy” is now more numerically stable (i.e. numerical blowups that result from repeatedly applying the recurrence relation are now postponed to higher cutoff values). This is done by representing Fock amplitudes with a higher precision than complex128 (which counters the accumulation of floating-point errors). We run Julia code via PyJulia (where Numba was used before) to keep the code fast. (#274)

  • Tensorflow bumped to v2.14 with poetry installation working out of the box on Linux and Mac. (#281)

  • Incorporated Tensor into Transformation in order to deal with modes more robustly. (#287)

  • Created the classes Unitary and Channel to simplify the logic in Transformation. (#287)

Bug fixes

  • Fixed a bug about the variable names in functions (apply_kraus_to_ket, apply_kraus_to_dm, apply_choi_to_ket, apply_choi_to_dm). (#271)

  • Fixed a bug that was leading to an error when computing the Choi representation of a unitary transformation. (#283)



Filippo Miatto, Yuan Yao, Robbe De Prins, Samuele Ferracin Zeyue Niu

Release 0.5.0

New features

  • Optimization callback functionalities has been improved. A dedicated Callback class is added which is able to access the optimizer, the cost function, the parameters as well as gradients, during the optimization. In addition, multiple callbacks can be specified. This opens up the endless possiblities of customizing the the optimization progress with schedulers, trackers, heuristics, tricks, etc. (#219)

  • Tensorboard-based optimization tracking is added as a builtin Callback class: TensorboardCallback. It can automatically track costs as well as all trainable parameters during optimization in realtime. Tensorboard can be most conveniently viewed from VScode. (#219)

    import numpy as np
    from import Optimizer, TensorboardCallback
    def cost_fn():
    def as_dB(cost):
        delta = np.sqrt(np.log(1 / (abs(cost) ** 2)) / (2 * np.pi))
        cost_dB = -10 * np.log10(delta**2)
        return cost_dB
    tb_cb = TensorboardCallback(cost_converter=as_dB, track_grads=True)
    opt = Optimizer(euclidean_lr = 0.001);
    opt.minimize(cost_fn, max_steps=200, by_optimizing=[...], callbacks=tb_cb)
    # Logs will be stored in `tb_cb.logdir` which defaults to `./tb_logdir/...` but can be customized.
    # VScode can be used to open the Tensorboard frontend for live monitoring.
    # Or, in command line: `tensorboard --logdir={tb_cb.logdir}` and open link in browser.
  • Gaussian states support a bargmann method for returning the bargmann representation. (#235)

  • The ket method of State now supports new keyword arguments max_prob and max_photons. Use them to speed-up the filling of a ket array up to a certain probability or total photon number. (#235)

    from mrmustard.lab import Gaussian
    # Fills the ket array up to 99% probability or up to the |0,3>, |1,2>, |2,1>, |3,0> subspace, whichever is reached first.
    # The array has the autocutoff shape, unless the cutoffs are specified explicitly.
    ket = Gaussian(2).ket(max_prob=0.99, max_photons=3)
  • Gaussian transformations support a bargmann method for returning the bargmann representation. (#239)

  • BSGate.U now supports method=’vanilla’ (default) and ‘schwinger’ (slower, but stable to any cutoff) (#248)

Breaking Changes

  • The previous callback argument to Optimizer.minimize is now callbacks since we can now pass multiple callbacks to it. (#219)

  • The opt_history attribute of Optimizer does not have the placeholder at the beginning anymore. (#235)


  • The math module now has a submodule lattice for constructing recurrence relation strategies in the Fock lattice. There are a few predefined strategies in mrmustard.math.lattice.strategies. (#235)

  • Gradients in the Fock lattice are now computed using the vector-jacobian product. This saves a lot of memory and speeds up the optimization process by roughly 4x. (#235)

  • Tests of the compact_fock module now use hypothesis. (#235)

  • Faster implementation of the fock representation of BSgate, Sgate and SqueezedVacuum, ranging from 5x to 50x. (#239)

  • More robust implementation of cutoffs for States. (#239)

  • Dependencies and versioning are now managed using Poetry. (#257)

Bug fixes

  • Fixed a bug that would make two progress bars appear during an optimization (#235)

  • The displacement of the dual of an operation had the wrong sign (#239)

  • When projecting a Gaussian state onto a Fock state, the upper limit of the autocutoff now respect the Fock projection. (#246)

  • Fixed a bug for the algorithms that allow faster PNR sampling from Gaussian circuits using density matrices. When the cutoff of the first detector is equal to 1, the resulting density matrix is now correct.



Filippo Miatto, Zeyue Niu, Robbe De Prins, Gabriele Gullì, Richard A. Wolf

Release 0.4.1

New features

Breaking changes


  • Fixed flaky optimization tests and removed tf dependency. (#224) (#233)

Bug fixes

  • Unpins package versions in that got mistakenly pinned in 0.4.0. (#223)

  • fixing a bug with the Dgate optimization (#232)



Filippo Miatto, Sebastian Duque Mesa

Release 0.4.0

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)

    from mrmustard.lab import Vacuum, Dgate, Ggate
    from mrmustard.physics import fidelity
    from 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(
    # Use case 1: Run circuit optimization 5 times on randomly initialized circuits.
    results_1 = map_trainer(
    # Use case 2: Run circuit optimization 2 times on randomly initialized circuits with custom parameters.
    results_2 = map_trainer(
            {'x': 0.1, 'euclidean_lr': 0.005, 'max_steps': 50, 'HBAR': 1.},
            {'x': -0.7, 'euclidean_lr': 0.1, 'max_steps': 2, 'HBAR': 2.},
  • 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)

    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)

  • the Math interface now supports linear system solving via math.solve. (#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)

    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)

  • 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)

  • 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)

  • 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. (#214)

Breaking changes


  • 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) (#211)

  • Now the Wigner function is implemented in its own module and uses numba for speed. (#171)

    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)

  • 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)

  • The mrmustard.physics.fock module now provides convenience functions for applying kraus operators and choi operators to kets and density matrices. (#180)

    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)

  • 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)

  • 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)

  • Added multiple tests and improved the use of Hypothesis. (#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)

  • 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)

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)

  • The trace function in the fock module was giving incorrect results when called with certain choices of modes. This is now fixed. (#180)

  • The purity function for fock states no longer normalizes the density matrix before computing the purity. (#180)

  • The function dm_to_ket no longer normalizes the density matrix before diagonalizing it. (#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)

  • 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)

  • Various minor bug fixes. (#202)

  • Fixed the issue that the optimization of the interferometer was using orthogonal group optimization rather than unitary. (#208)

  • Fixes a slicing issue that arises when we compute the fidelity between gaussian and fock states. (#210)

  • The sign of parameters in the circuit drawer are now displayed correctly. (#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)

  • Fixes a bug in the cutoffs of the choi operator. (#216)



This release contains contributions from (in alphabetical order): Robbe De Prins, Sebastian Duque Mesa, Filippo Miatto, Zeyue Niu, Yuan Yao

Release 0.3.0

New features

  • Can switch progress bar on and off (default is on) from the settings via settings.PROGRESSBAR = True/False. (#128)

  • States in Gaussian and Fock representation now can be concatenated.

    from mrmustard.lab.states import Gaussian, Fock
    from mrmustard.lab.gates import Attenuator
    # concatenate pure states
    fock_state = Fock(4)
    gaussian_state = Gaussian(1)
    pure_state = fock_state & gaussian_state
    # also can concatenate mixed states
    mixed1 = fock_state >> Attenuator(0.8)
    mixed2 = gaussian_state >> Attenuator(0.5)
    mixed_state = mixed1 & mixed2


  • Parameter passthrough allows one to use custom variables and/or functions as parameters. For example we can use parameters of other gates:

    from mrmustard.lab.gates import Sgate, BSgate
    BS = BSgate(theta=np.pi/4, theta_trainable=True)[0,1]
    S0 = Sgate(r=BS.theta)[0]
    S1 = Sgate(r=-BS.theta)[1]
    circ = S0 >> S1 >> BS

    Another possibility is with functions:

    def my_r(x):
        return x**2
    x = math.new_variable(0.5, bounds = (None, None), name="x")
    def cost_fn():
      # note that my_r needs to be in the cost function
      # in order to track the gradient
      S = Sgate(r=my_r(x), theta_trainable=True)[0,1]
      return # some function of S
    opt.Optimize(cost_fn, by_optimizing=[x])


  • Adds the new trainable gate RealInterferometer: an interferometer that doesn’t mix the q and p quadratures. (#132)

  • Now marginals can be iterated over:

    for mode in state:


Breaking changes

  • The Parametrized and Training classes have been refactored: now trainable tensors are wrapped in an instance of the Parameter class. To define a set of parameters do

    from import Parametrized
    params = Parametrized(
        magnitude=10, magnitude_trainable=False, magnitude_bounds=None,
        angle=0.1, angle_trainable=True, angle_bounds=(-0.1,0.1)

    which will automatically define the properties magnitude and angle on the params object. To access the backend tensor defining the values of such parameters use the value property


    Gates will automatically be an instance of the Parametrized class, for example

    from mrmustard.lab import BSgate
    bs = BSgate(theta = 0.3, phi = 0.0, theta_trainable: True)
    # access params

    (#133), patch (#144) and (#158).


  • The Parametrized and Training classes have been refactored. The new training module has been added and with it the new Parameter class: now trainable tensors are being wrapped in an instance of Parameter. (#133), patch (#144)

  • The string representations of the Circuit and Transformation objects have been improved: the Circuit.__repr__ method now produces a string that can be used to generate a circuit in an identical state (same gates and parameters), the Transformation.__str__ and objects inheriting from it now prints the name, memory location of the object as well as the modes of the circuit in which the transformation is acting on. The _markdown_repr_ has been implemented and on a jupyter notebook produces a table with valuable information of the Transformation objects. (#141)

  • Add the argument ‘modes’ to the Interferometer operation to indicate which modes the Interferometer is applied to. (#121)

Bug fixes

  • Fixed a bug in the State.ket() method. An attribute was called with a typo in its name. (#135)

  • The math.dagger function applying the hermitian conjugate to an operator was incorrectly transposing the indices of the input tensor. Now math.dagger appropriately calculates the Hermitian conjugate of an operator. (#156)

  • The application of a Choi operator to a density matrix was resulting in a transposed dm. Now the order of the indices in the application of a choi operator to dm and ket is correct. (#188)


  • The centralized Xanadu Sphinx Theme is now used to style the Sphinx documentation. (#126)

  • The documentation now contains the section. The optimization examples on the README and Basic API Reference section have been updated to use the latest API. (#133)


This release contains contributions from (in alphabetical order):

Mikhail Andrenkov, Sebastian Duque Mesa, Filippo Miatto, Yuan Yao

Release 0.2.0

New features since last release

  • Fidelity can now be calculated between two mixed states. (#115)

  • A configurable logger module is added. (#107)

    from mrmustard.logger import create_logger
    logger = create_logger(__name__)
    logger.warning("Warning message")


  • The tensorflow and torch backend adhere to MathInterface. (#103)

Bug fixes

  • Setting the modes on which detectors and state acts using modes kwarg or __getitem__ give consistent results. (#114)

  • Lists are used instead of generators for indices in fidelity calculations. (#117)

  • A raised KeyboardInterrupt while on a optimization loop now stops the execution of the program. #105



This release contains contributions from (in alphabetical order):

Sebastián Duque, Theodor Isacsson, Filippo Miatto

Release 0.1.1

New features since last release

  • physics.normalize and physics.norm are now available. (#97)

  • State now has a norm property. (#97)

  • Can now override autocutoff in State by setting the cutoffs argument. (#97)

Improvements since last release

  • Renamed amplification argument of Amplifier to gain. (#97)

  • Improved __repr__ for State. (#97)

  • Added numba section in about(). (#97)

Bug fixes

  • Renamed “pytorch” to “torch” in mrmustard.__init__() so that torch can be imported correctly. (#97)

  • Fixed typos in State.primal(), State.__rmul__(). (#97)

  • Fixed a multimode bug in PNRDetector.__init__(). (#97)

  • Fixed a bug in normalization of Fock. (#97)

  • Fixed a bug in (#97)


This release contains contributions from (in alphabetical order):

Sebastián Duque, Filippo Miatto

Release 0.1.0

New features since last release

  • This is the initial public release.


This release contains contributions from (in alphabetical order):

Sebastián Duque, Zhi Han, Theodor Isacsson, Josh Izaac, Filippo Miatto, Nicolas Quesada