⚠️ WARNING
The ITensorTDVP.jl package is deprecated in favor of the ITensorMPS.jl package. All of the code from ITensorTDVP.jl has been copied into ITensorMPS.jl and will be developed there. If you are using this package, please install ITensorMPS.jl and change using ITensorTDVP to using ITensorMPS in your code.

ITensorTDVP

Installation

To install this package, you can use the following steps:

$ julia

julia> ]

pkg> add ITensorTDVP

However, as noted above we now recommend installing and loading ITensorMPS instead of ITensorTDVP.

News

ITensorTDVP.jl v0.4.1 release notes

New features

• A new (experimental) expand function has been introduced for performing global Krylov expansion based on arXiv:2005.06104, which can help with the accuracy of TDVP evolution in certain cases. See the docstrings of expand for more details:

julia> using ITensorTDVP

julia> ?

help?> expand
# ...

Users are not given many customization options just yet as we gain more experience on the right balance between efficacy of the expansion and performance, and default values and keyword arguments are subject to change as we learn more about how to best use the method.

ITensorTDVP.jl v0.4 release notes

Breaking changes

• When calling tdvp(operator, t, init; kwargs...), t is now interpreted as the total evolution time, while in ITensorTDVP.jl v0.3 and below it was being interpreted as the time step. To upgrade, change code like:

tdvp(operator, -0.1im, init; nsweeps=10, kwargs...)

to:

tdvp(operator, -1.0im, init; nsweeps=10, kwargs...)

to evolve to total time 1.0 over 10 time steps of size 0.1. Also note that ITensorTDVP.jl v0.4 introduces nsteps as an alias for nsweeps in the tdvp function. nsteps is now the preferred syntax for specifying the number of time steps, and nsweeps may be deprecated in tdvp in the future. For example the following is equivalent to the examples above:

tdvp(operator, -1.0im, init; nsteps=10, kwargs...)

. Alternatively, you can specify the time steps instead of the number of steps:

tdvp(operator, -1.0im, init; time_step=-0.1im, kwargs...)

Note that the total time divided by the time step must be an integer.

• In tdvp, a custom local updater/solver must now be passed as a keyword argument updater, as opposed to as the first argument which was the syntax in ITensorTDVP.jl v0.3 and below. So code like:

tdvp(custom_updater, operator, t, init; kwargs...)

must be changed to:

tdvp(operator, t, init; updater=custom_updater, kwargs...)

• The keyword argument psi that was being passed to observers in tdvp, linsolve, etc. which stored the current state has been renamed to state. Change code like:

measure_sz(; psi) = expect(psi, "Sz")
obs = observer("Sz" => measure_sz)
tdvp(operator, t, init; (observer!)=obs, kwargs...)

to:

measure_sz(; state) = expect(state, "Sz")
obs = observer("Sz" => measure_sz)
tdvp(operator, t, init; (observer!)=obs, kwargs...)

• Only the argument ordering tdvp(operator, t, init; kwargs...) is now supported. tdvp(t, operator, init; kwargs...) and tdvp(operator, init, t; kwargs...) have been removed.
• In tdvp, the keyword argument solver_backend has been renamed to updater_backend. Change code like:

tdvp(operator, t, init; solver_backend="applyexp", kwargs...)

to:

tdvp(operator, t, init; updater_backend="applyexp", kwargs...)

• In tdvp and ITensorTDVP.dmrg, keyword arguments passed to the local solver/updater should now be passed in a NamedTuple in the updater_kwargs keyword argument, such as updater_kwargs=(; tol=1e-5, krylovdim=20), instead of as keyword arguments solver_tol, solver_krylovdim, etc. Change code like:

tdvp(operator, t, init; solver_tol=1e-5, solver_krylovdim=20, kwargs...)
ITensorTDVP.dmrg(operator, init; solver_tol=1e-5, solver_krylovdim=20, kwargs...)

to:

tdvp(operator, t, init; updater_kwargs=(; tol=1e-5, krylovdim=20), kwargs...)
ITensorTDVP.dmrg(operator, init; updater_kwargs=(; tol=1e-5, krylovdim=20), kwargs...)

• In linsolve, the keyword argument solver_kwargs has been renamed to updater_kwargs.
• In ITensorTDVP.dmrg, dmrg_x, and linsolve, the keyword argument step_observer! has been renamed to sweep_observer!. Either name is allowed in tdvp but step_observer! is preferred and the name sweep_observer! may be deprecated in tdvp in future versions.
• Support for ITensors.AbstractObserver-based observers has been removed, use Observers.observer instead.
• In contract(operator::MPO, state::MPS; alg="fit", kwargs...), and apply(operator::MPO, state::MPS; alg="fit", kwargs...), the keyword argument for specifying an initial guess for the result is now called init instead of init_mps. Additionally, in contract, init should have primed site indices, or more generally should have site indices which are those that are not shared by the input operator and state. In apply, init should have site indices matching those of the input state.
• In custom local updaters/solvers, the keyword arguments time_step, current_time, and outputlevel are now being passed as a NamedTuple in a new keyword argument internal_kwargs. Change local updaters/solvers from:

function custom_updater(operator, init; time_step, current_time, outputlevel, kwargs...)
  ### Updater implementation.
end

to:

function custom_updater(operator, init; internal_kwargs, kwargs...)
  # List whichever keyword arguments of `internal_kwargs` are needed
  # on the left hand side.
  (; time_step) = internal_kwargs
  ### Updater implementation.
end

New features

• nsteps is now an alias for the nsweeps keyword argument in tdvp and is the preferred syntax for setting the number of time steps of TDVP. nsweeps may be deprecated as a keyword argument of tdvp in the future.
• TimeDependentSum now accepts coefficients and terms that are Tuples, along with the previous interface which accepted Vectors.
• Custom local updaters/solvers can be passed as a keyword argument updater to ITensorTDVP.dmrg, dmrg_x, and linsolve, which is consistent with the new syntax for tdvp.

ITensorTDVP.jl v0.3 Release Notes

Breaking changes

• ITensorTDVP.dmrg and ITensorTDVP.dmrg_x now output a tuple containing the eigenvalue and eigenvector, while before they just output the eigenvector. You should update code like this:

psi = dmrg_x(H, psi0; nsweeps=10, maxdim=100, cutoff=1e-6)
psi = ITensorTDVP.dmrg(H, psi0; nsweeps=10, maxdim=100, cutoff=1e-6)

to:

energy, psi = dmrg_x(H, psi0; nsweeps=10, maxdim=100, cutoff=1e-6)
energy, psi = ITensorTDVP.dmrg(H, psi0; nsweeps=10, maxdim=100, cutoff=1e-6)

ITensorTDVP.jl v0.2 Release Notes

Breaking changes

• ITensorTDVP.jl v0.2.0-v0.2.4: The applyexp Krylov exponentiation solver backend was removed, and solver_backend="applyexp" option for tdvp now just calls exponentiate from KrylovKit.jl. applyexp is in many ways the same as exponentiate bit exponentiate has more advanced features like restarts. In these versions, solver_backend="applyexp" prints a warning to that effect. As of ITensorTDVP.jl v0.2.5, we have brought back the applyexp backend because we received reports that it performed better in certain cases. We plan to investigate that issue and make sure exponentiate works as well as applyexp in those cases so that we can go back to just having a single exponentiate backend.

Bug fixes

• svd_alg now doesn't specify a default value, so the default value is set by the svd function in ITensors/NDTensors. This fixes an issue using ITensorTDVP.jl and GPU backends, where the default value being set in ITensorTDVP.jl wasn't compatible with the options available in some GPU backends like CUDA.
• More generally, keyword arguments are handled better throughout the package, so default values are handled more systematically and keyword arguments are listed or forwarded more explicitly, so it should catch more mistakes like passing an incorrect keyword argument name.