Change Log

NetKet 3.0b3 (unreleased)

GitHub commits.

New features

  • The submodule provides utilities for geometrical and permutation groups. Lattice (and its specialisations like Grid) use these to automatically construct the space groups of lattices, as well as their character tables for generating wave functions with broken symmetry. #724

  • Autoregressive neural networks, sampler, and masked linear layers have been added to models, sampler and nn #705.

Breaking Changes

  • The graph.Grid class has been removed. graph.Grid will now return an instance of graph.Lattice supporting the same API but with new functionalities related to spatial symmetries. The color_edges optional keyword argument has been removed without deprecation. #724

  • MCState.n_discard has been renamed MCState.n_discard_per_chain and the old binding has been deprecated #739.

Bug Fixes

  • nn.to_array and MCState.to_array, if normalize=False, do not subtract the logarithm of the maximum value from the state #705.

NetKet 3.0b2 (published on 31 May 2021)

GitHub commits.

New features

  • Group Equivariant Neural Networks have been added to models #620

  • Permutation invariant RBM and Permutation invariant dense layer have been added to models and nn.linear #573

  • Add the property acceptance to MetropolisSampler’s SamplerState, computing the MPI-enabled acceptance ratio. #592.

  • Add StateLog, a new logger that stores the parameters of the model during the optimization in a folder or in a tar file. #645

  • A warning is now issued if NetKet detects to be running under mpirun but MPI dependencies are not installed #631

  • operator.LocalOperators now do not return a zero matrix element on the diagonal if the whole diagonal is zero. #623.

  • logger.JSONLog now automatically flushes at every iteration if it does not consume significant CPU cycles. #599

  • The interface of Stochastic Reconfiguration has been overhauled and made more modular. You can now specify the solver you wish to use, NetKet provides some dense solvers out of the box, and there are 3 different ways to compute the Quantum Geometric Tensor. Read the documentation to learn more about it. #674

  • Unless you specify the QGT implementation you wish to use with SR, we use an automatic heuristic based on your model and the solver to pick one. This might affect SR performance. #674

Breaking Changes

  • For all samplers, n_chains now sets the total number of chains across all MPI ranks. This is a breaking change compared to the old API, where n_chains would set the number of chains on a single MPI rank. It is still possible to set the number of chains per MPI rank by specifying n_chains_per_rank instead of n_chains. This change, while breaking allows us to be consistent with the interface of variational.MCState, where n_samples is the total number of samples across MPI nodes.

  • MetropolisSampler.reset_chain has been renamed to MetropolisSampler.reset_chains. Likewise in the constructor of all samplers.

  • Briefly during development releases MetropolisSamplerState.acceptance_ratio returned the percentage (not ratio) of acceptance. acceptance_ratio is now deprecated in favour of the correct acceptance.

  • models.Jastrow now internally symmetrizes the matrix before computing its value #644

  • MCState.evaluate has been renamed to MCState.log_value #632

  • nk.optimizer.SR no longer accepts keyword argument relative to the sparse solver. Those should be passed inside the closure or functools.partial passed as solver argument.

  • and have been deprecated and will soon be removed.

  • Parts of the Lattice API have been overhauled, with deprecations of several methods in favor of a consistent usage of Lattice.position for real-space location of sites and Lattice.basis_coords for location of sites in terms of basis vectors. Lattice.sites has been added, which provides a sequence of LatticeSite objects combining all site properties. Furthermore, Lattice now provides lookup of sites from their position via id_from_position using a hashing scheme that works across periodic boundaries. #703 #715

  • nk.variational has been renamed to nk.vqs and will be removed in a future release.

Bug Fixes

  • Fix operator.BoseHubbard usage under jax Hamiltonian Sampling #662

  • Fix SROnTheFly for R->C models with non homogeneous parameters #661

  • Fix MPI Compilation deadlock when computing expectation values #655

  • Fix bug preventing the creation of a hilbert.Spin Hilbert space with odd sites and even S. #641

  • Fix bug #635 preventing the usage of NumpyMetropolisSampler with MCState.expect #635

  • Fix bug #635 where the graph.Lattice was not correctly computing neighbours because of floating point issues. #633

  • Fix bug the Y Pauli matrix, which was stored as its conjugate. #618 #617 #615

NetKet 3.0b1 (published beta release)

GitHub commits.

API Changes

  • Hilbert space constructors do not store the lattice graph anymore. As a consequence, the constructor does not accept the graph anymore.

  • Special Hamiltonians defined on a lattice, such as operator.BoseHubbard, operator.Ising and operator.Heisenberg, now require the graph to be passed explicitly through a graph keyword argument.

  • operator.LocalOperator now default to real-valued matrix elements, except if you construct them with a complex-valued matrix. This is also valid for operators such as :func:operator.spin.sigmax and similars.

  • When performing algebraic operations *, -, + on pairs of operator.LocalOperator, the dtype of the result iscomputed using standard numpy promotion logic.

    • Doing an operation in-place +=, -=, *= on a real-valued operator will now fail if the other is complex. While this might seem annoying, it’s useful to ensure that smaller types such as float32 or complex64 are preserved if the user desires to do so.

  • AbstractMachine has been removed. It’s functionality is now split among the model itself, which is defined by the user and variational.MCState for pure states or variational.MCMixedState for mixed states.

    • The model, in general is composed by two functions, or an object with two functions: an init(rng, sample_val) function, accepting a jax.random.PRNGKey object and an input, returning the parameters and the state of the model for that particular sample shape, and a apply(params, samples, **kwargs) function, evaluating the model for the given parameters and inputs.

    • Some models (previously machines) such as the RBM (Restricted Boltzmann Machine) Machine, NDM (Neural Density Matrix) or MPS (Matrix Product State ansatz) are available in Pre-built models.

    • Machines, now called models, should be written using Flax or another jax framework.

    • Serialization and deserialization functionality has now been moved to variational.MCState, which support the standard Flax interface through MsgPack. See Flax docs for more information

    • AbstractMachine.init_random_parameters functionality has now been absorbed into netket.vqs.VariationalState.init_parameters(), which however has a different syntax.

  • Samplers now require the Hilbert space upon which they sample to be passed in to the constructor. Also note that several keyword arguments of the samplers have changed, and new one are available.

  • It’s now possible to change Samplers dtype, which controls the type of the output. By default they use double-precision samples (np.float64). Be wary of type promotion issues with your models.

  • Samplers no longer take a machine as an argument.

  • Samplers are now immutable (frozen) dataclasses (defined through flax.struct.dataclass) that only hold the sampling parameters. As a consequence it is no longer possible to change their settings such as n_chains or n_sweeps without creating a new sampler. If you wish to update only one parameter, it is possible to construct the new sampler with the updated value by using the sampler.replace(parameter=new_value) function.

  • Samplers are no longer stateful objects. Instead, they can construct an immutable state object sampler.init_state, which can be passed to sampling functions such as sampler.sample, which now return also the updated state. However, unless you have particular use-cases we advise you use the variational state MCState instead.

  • The Optimizer module has been overhauled, and now only re-exports flax optim module. We advise not to use netket’s optimizer but instead to use optax .

  • The SR object now is only a set of options used to compute the SR matrix. The SR matrix, now called quantum_geometric_tensor can be obtained by calling variational.MCState.quantum_geometric_tensor(). Depending on the settings, this can be a lazy object.

  • netket.Vmc has been renamed to netkt.VMC

  • netket.models.RBM replaces the old RBM machine, but has real parameters by default.

  • As we rely on Jax, using dtype=float or dtype=complex, which are weak types, will sometimes lead to loss of precision because they might be converted to float32. Use np.float64 or np.complex128 instead if you want double precision when defining your models.