Compact fock #154
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Compact fock #154
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… of a gaussian object and its gradients
Code now works without displacement and without numba (in the diagonal case)
…ment. leftover mode case works with numba, without strides, without displacement
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Additional details and impacted files@@ Coverage Diff @@
## develop #154 +/- ##
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- Coverage 82.24% 75.57% -6.67%
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Files 35 42 +7
Lines 3171 3816 +645
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+ Hits 2608 2884 +276
- Misses 563 932 +369
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…sed cropped version of A and B
… now for the algorithm where we use complete pivot diagonals (not using bridge pivots)
…one pass for amplitudes and one pass for gradients. Diagonal case; not yet leftover mode case.
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This is a valuable PR! It be nice to reactivate its development. Apart from syncing it with the latest version of MM, what else is missing? |
ziofil
reviewed
Feb 28, 2023
ziofil
reviewed
Feb 28, 2023
ziofil
reviewed
Feb 28, 2023
ziofil
reviewed
Feb 28, 2023
ziofil
reviewed
Feb 28, 2023
…nto compactFock
**Context:** autocutoffs are a bit flimsy, it would be nice to have a norm-based autocutoff **Description of the Change:** Uses Robbe's new function to improve autocutoff: each mode now gets a cutoff such that if it were the marginal it would capture `settings.AUTOCUTOFF_PROBABILITY` probability (default 0.999). **Benefits:** Much more sensible autocutoffs **Possible Drawbacks:** Some users may need to update their code **Related GitHub Issues:** perhaps [#118](#118)
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Mar 8, 2023
# 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)](#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)](#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)](#175) * the Math interface now supports linear system solving via `math.solve`. [(#185)](#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)](#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)](#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)](#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)](#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)](#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)](#147) [(#211)](#211) * Now the Wigner function is implemented in its own module and uses numba for speed. [(#171)](#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)](#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)](#181) * The `mrmustard.physics.fock` module now provides convenience functions for applying kraus operators and choi operators to kets and density matrices. [(#180)](#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)](#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)](#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)](#199) * Added multiple tests and improved the use of Hypothesis. [(#191)](#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)](#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)](#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)](#180) * The trace function in the fock module was giving incorrect results when called with certain choices of modes. This is now fixed. [(#180)](#180) * The purity function for fock states no longer normalizes the density matrix before computing the purity. [(#180)](#180) * The function `dm_to_ket` no longer normalizes the density matrix before diagonalizing it. [(#180)](#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)](#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)](#201) * Various minor bug fixes. [(#202)](#202) * Fixed the issue that the optimization of the interferometer was using orthogonal group optimization rather than unitary. [(#208)](#208) * Fixes a slicing issue that arises when we compute the fidelity between gaussian and fock states. [(#210)](#210) * The sign of parameters in the circuit drawer are now displayed correctly. [(#209)](#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)](#212) * Fixes a bug in the cutoffs of the choi operator. [(#216)](#216) ### Documentation ### Contributors This release contains contributions from (in alphabetical order): [Robbe De Prins](https://github.com/rdprins), [Sebastian Duque Mesa](https://github.com/sduquemesa), [Filippo Miatto](https://github.com/ziofil), [Zeyue Niu](https://github.com/zeyueN), [Yuan Yao](https://github.com/sylviemonet) --------- Co-authored-by: Sebastián Duque Mesa <675763+sduquemesa@users.noreply.github.com> Co-authored-by: JacobHast <jacobhastrup@gmail.com> Co-authored-by: elib20 <53090166+elib20@users.noreply.github.com> Co-authored-by: Luke Helt <31250931+heltluke@users.noreply.github.com> Co-authored-by: zeyueN <48225584+zeyueN@users.noreply.github.com> Co-authored-by: Robbe De Prins <52749580+rdprins@users.noreply.github.com> Co-authored-by: Robbe De Prins (UGent-imec) <Robbe.DePrins@UGent.be> Co-authored-by: Yuan <16817699+sylviemonet@users.noreply.github.com>
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Context: Make PNR sampling faster for Gaussian circuits when using density matrices. This is done by applying the recurrence relation in a selective manner such that useless (off-diagonal) amplitudes from the Fock representation are not calculated. When all modes are detected,
math.hermite_renormalizedcan be replaced bymath.hermite_renormalized_diagonal. In case all but the first mode are detected,math.hermite_renormalized_1leftoverModecan be used. The complexity of these new methods is equal to performing a pure state simulation. The methods are differentiable, such that they can be used for defining costfunctions.Description of the Change: Adds the function
math.hermite_renormalized_diagonalandmath.hermite_renormalized_1leftoverMode.Benefits: Faster simulation and optimization of Gaussian circuits with PNR detectors when using density matrices.