uscqserver / openquantumtools.jl Goto Github PK

The EnsembleProblem returned by a build_ensemble function contains internal variables which are not reset after solve is called. A convenient reset! function may be provided to for those objects to be reused again with solve.

We use Annealing as a holder for an arbitrary open system evolution. The name might be misleading to people outside QA community. May an equivalent object Evolution should be added.

Waiting for the component package OpenQuantumBase to get out of the mandatory waiting period: Issue 37

• Completely Positive, Simple, and Possibly Highly Accurate Approximation of the Redfield Equation

The Schrodinger equation solver does not work well with circuit Hamiltonian. More information is needed at current stage.

The development now happens in Github.

Implement a vectorized adiabatic master equation solver. The vectorization is described in this page: https://en.wikipedia.org/wiki/Vectorization_(mathematics).

Just as a first-case test, we implemented a GPU solver as a separate function,

Ideally, we would integrate better. How we do this is effectively solved by solving the issue raised in OpenQuantumBase.jl: USCqserver/OpenQuantumBase.jl#40 (comment)

I raised here simply because we'd need to make changes here as well after resolving the issue in Base.

Instead of scaling the ODE operators by t_f, add an option to solve the entire equation from (0, t_f)

Better implementation of project_to_lowlevel to take care of the degenerate energy levels. Possible enhancements includes:

• Taking higher order derivatives as input and implement the method described here: https://epubs.siam.org/doi/abs/10.1137/S0895479896304332

This will help in the case where the noise spectrum is expensive to calculate and needs to be interpolated.

Change OSQAT to HOQST in documentation.

Need to change build_onesided_ame to the OpenQuantumBase.onesided_ame_from_interactions.

Calling load_diff_eq() inside a function will not properly load the DifferentialEquations module.

• Add spin-fluctuator bath module
• Add callback functions of spin-fluctuator bath module
• Debug the prototype and write unit test

DiffEqBase is the lowest common denominator for the DiffEq packages, not necessarily the whole SciML ecosystem, and so it has a lot DiffEq dependencies. These are generally not required by downstream packages. If what you're looking for is a way to define problems without having most dependencies, we recommend you use SciMLBase as the dependency since everything like ODEProblem, SteadyStateProblem, etc. is defined there. We basically recommend depending on SciMLBase for problem definitions, and solver packages for specific solvers, but generally most non-SciML packages should not be depending on DiffEqBase directly (given the split of SciMLBase in 2021)

For more details see: https://diffeq.sciml.ai/stable/features/low_dep/ and https://discourse.julialang.org/t/psa-the-right-dependency-to-reduce-from-differentialequations-jl/72757

Let me know if you need any help updating this, though for almost all dependents here it should be a trivial name change as you're actually using pieces from SciMLBase.

HOQST only works with Julia versions >= 1.4

The error message suggests that the integration produces NaN at the beginning of the equation. This error does not seem to be reproducible on my local machine.

Just for aesthetical reason.

This issue is related to the tutorial repo:

• USCqserver/HOQSTTutorials.jl#15

Add optional save_positions argument in Callback types to the trajectory solver interface. The default value is (false, false) to save runtime memory.

Plot recipe for plotting the Hamiltonian.

They user should be able to define a time dependent rates and Lindblad operators.

Right now, only DenseHamiltonian and SparseHamiltonian supports user defined EIGS and eigen_decomp. The can be extended to other AbstractHamiltonian types.

Implement truncated adiabatic master equation.

• Permutation Matrix Representation Quantum Monte Carlo
• Off-Diagonal Expansion Quantum Monte Carlo
• Calculating the divided differences of the exponential function by addition and removal of inputs

For standard Ohmic bath parameter, a discontinuous point in the Lambshift (S(w)) appears at large frequency.

Implement AME for adiabatic frame Hamiltonian

We've added a GPU solver

The informal timing was done with the following test code:
https://github.com/naezzell/accelqat/blob/b617c423daaa4cb0ab2f4c1a4d8f2536fb9f7bb3/cuda/try_gpu_accel.jl#L1-L66
with 2 CPUs and 1 GPU on USC Discovery cluster.

• Basic solver
• Vectorized solver

Current implementation of Davies object does not take care of Sparse Hamiltonian.

Filter keyword arguments before pass to solve function

This issue is used to trigger TagBot; feel free to unsubscribe.

If you haven't already, you should update your TagBot.yml to include issue comment triggers.
Please see this post on Discourse for instructions and more details.

If you'd like for me to do this for you, comment TagBot fix on this issue.
I'll open a PR within a few hours, please be patient!

We need to first have a working implementation of the following two types of Lamb shift calculation:

• USCqserver/OpenQuantumBase.jl#49
• USCqserver/OpenQuantumBase.jl#61

Then we want to investigate another possible implementation of Lamb shift:

• #78

We don't need to optimize the implementation for v0.7

The current test uses non-positive initial state. Changing to a different example with valid initial density matrix should solve the problem.