Experimental API

In this page we present some experimental interfaces of NetKet. Those are not guaranteed to be API-stable, and might change without notice (other than the changelog) among minor releases. The netket.experimental module mirrors the standard netket module structure, and we suggest to use it as follows:

import netket as nk
import netket.experimental as nkx

Drivers

Currently NetKet offers an experimental driver implementing Stochastic Reconfiguration with the kernel trick (originally introduced under the name of minSR by Ao Chen and Markus Heyl). This is slightly more limited in features than the standard Stochastic Reconfiguration implementation of netket.drivers.VMC, but can scale to millions of parameters

driver.VMC_SRt

Energy minimization using Variational Monte Carlo (VMC) and the kernel formulation of Stochastic Reconfiguration (SR).

Quantum State Reconstruction

The Quantum State Reconstruction algorithm performs an approximate tomographic reconstruction of measurement data coming from a quantum computer (or similar device) using a Pure or Mixed quantum state.

QSR	Quantum state reconstruction driver minimizing KL divergence.

Samplers

They are experimental, meaning that we could change them at some point, and we actively seeking for feedback and opinions on their usage and APIs.

Parallel tempering samplers

An experimental sampler is MetropolisSamplerPmap, which makes use of jax.pmap() to use different GPUs/CPUs without having to use MPI. It should scale much better over several CPUs, but you have to start jax with a specific environment variable.

sampler.MetropolisSamplerPmap

Metropolis-Hastings sampler for an Hilbert space according to a specific transition rule where chains are split among the available devices (jax.devices()).

Particle-specific samplers

The following samplers are for 2nd-quantisation fermionic hilbert spaces (netket.experimental.hilbert.SpinOrbitalFermions).

sampler.MetropolisParticleExchange

This sampler moves (or hops) a random particle to a different but random empty mode.

And the corresponding rules

sampler.rules.ParticleExchangeRule

Exchange rule for particles on a lattice.

Logging

This module contains experimental loggers that can be used with the optimization drivers.

logging.HDF5Log

HDF5 Logger, that can be passed with keyword argument logger to Monte Carlo drivers in order to serialize the output data of the simulation.

Variational State Interface

vqs.variables_from_file	Loads the variables of a variational state from a .mpack file.
vqs.variables_from_tar	Loads the variables of a variational state from the i-th element of a .tar archive.

Time Evolution Driver

TDVP	Variational time evolution based on the time-dependent variational principle which, when used with Monte Carlo sampling via netket.vqs.MCState, is the time-dependent VMC (t-VMC) method.
driver.TDVPSchmitt	Variational time evolution based on the time-dependent variational principle which, when used with Monte Carlo sampling via netket.vqs.MCState, is the time-dependent VMC (t-VMC) method.

ODE Integrators

Concrete solvers

This is a collection of ODE solvers that can be used with the TDVP driver above.

dynamics.Euler	The canonical first-order forward Euler method.
dynamics.Heun	The second order Heun's method.
dynamics.Midpoint	The second order midpoint method.
dynamics.RK12	The second order Heun's method.
dynamics.RK23	2nd order adaptive solver with 3rd order error control, using the Bogacki–Shampine coefficients
dynamics.RK4	The canonical Runge-Kutta Order 4 method.
dynamics.RK45	Dormand-Prince's 5/4 Runge-Kutta method.

The corresponding integrator is then automatically constructed within the TDVP driver.

Abstract classes

Those are the abstract classes you can inherit from to implement your own solver

dynamics.AbstractSolver	Abstract base class for ODE solvers.
dynamics.AbstractSolverState	Base class holding the state of a solver.

Fermions

This modules contains hilbert space and operator implementations of fermions in second quantization. It is experimental until it has been thoroughly tested by the community, meaning feedback is welcome.

hilbert.SpinOrbitalFermions

Hilbert space for 2nd quantization fermions with spin s distributed among n_orbital orbitals.

operator.FermionOperator2nd	A fermionic operator in \(2^{nd}\) quantization, using Numba for indexing.
operator.fermion.create	Builds the fermion creation operator \(\hat{a}^\dagger\) acting on the site-th of the Hilbert space hilbert.
operator.fermion.destroy	Builds the fermion destruction operator \(\hat{a}\) acting on the site-th of the Hilbert space hilbert.
operator.fermion.number	Builds the number operator \(\hat{a}^\dagger\hat{a}\) acting on the site-th of the Hilbert space hilbert.
operator.from_pyscf_molecule	Construct a netket operator encoding the electronic hamiltonian of a pyscf molecule in a chosen orbital basis.
operator.pyscf.TV_from_pyscf_molecule	Computes the nuclear repulsion energy \(E_{nuc}\), and the T and V tensors encoding the 1-body and 2-body terms in the electronic hamiltonian of a pyscf molecule using the specified molecular orbitals.

Observables

This module contains various observables that can be computed starting from various variational states.

observable.Renyi2EntanglementEntropy	Rényi2 bipartite entanglement entropy of a state \(| \Psi \rangle\) between partitions A and B.
observable.VarianceObservable	Observable computing the variance of a quantum operator \(O\).