64 update docs (#65)

* update paper link

* update paper link

README.md

@@ -5,7 +5,7 @@
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.05364/status.svg)](https://doi.org/10.21105/joss.05364)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8086188.svg)](https://doi.org/10.5281/zenodo.8086188)
 
-This package implements the [Quantum Approximate Optimization Algorithm](https://arxiv.org/abs/1411.4028) and the [Mean-Field Approximate Optimization Algorithm](https://arxiv.org/abs/2303.00329).
+This package implements the [Quantum Approximate Optimization Algorithm](https://arxiv.org/abs/1411.4028) and the [Mean-Field Approximate Optimization Algorithm](https://link.aps.org/doi/10.1103/PRXQuantum.4.030335).
 
 ## Installation
 

docs/src/examples/mean_field.md

@@ -95,56 +95,4 @@ E = expectation(S, mf_problem.local_fields, mf_problem.couplings)
 and the solution of the algorithm can be retrieved by calling
 ```julia
 sol = mean_field_solution(S)
-```
-
-## Equations of Motion via ODE Solver
-
-There is also the option to solve the full mean-field equations of motion (s. e.g. equation (9) [in this paper](https://arxiv.org/pdf/2403.11548)) via `OrdinaryDiffEq.jl`. For a simple linear annealing schedule, we can do this by calling
-```
-T_final = p * τ
-schedule_function = t -> t/T_final
-sol = evolve(mf_problem.local_fields, mf_problem.couplings, T_final, schedule_function)
-```
-
-### Tensor Problem Definition
-
-Instead of being restricted to simple QUBO problems defined by `local_fields` ``h_i`` and `couplings` ``J_{ij}`` as introduced above, we also want to be able to solve problems defined in terms of arbitrary tensors, e.g.
-```math
-\begin{align}
-    H(t) = (1 - s(t)) \sum_{i=1}^N n_i^x(t) + s(t)\sum_{i=1}^N \bigg[ J_i + \sum_{j>i} \big[ J_{ij} + \sum_{k>j} J_{ijk}n_k^z(t) + \cdots \big]  n_j^z(t) \bigg] n_i^z(t).
-\end{align}
-```
-To define the tensors for the standard QUBO set-up discussed here, we build the dictionaries
-```julia
-xtensor = Dict([(i, ) => 1.0 for i in 1:mf_problem.num_qubits])
-```
-for the local transverse-field driver and
-```julia
-ztensor = Dict()
-for (i, h_i) in enumerate(mf_problem.local_fields)
-    if h_i != 0.0
-        ztensor[(i,)] = h_i
-    end
-end
-
-for i in 1:mf_problem.num_qubits
-    for j in i+1:mf_problem.num_qubits
-        if mf_problem.couplings[i, j] != 0.0
-            ztensor[(i, j)] = mf_problem.couplings[i, j]
-        end
-    end
-end
-```
-for the problem Hamiltonian. We then feed these to
-```julia
-tensor_problem = TensorProblem(mf_problem.num_qubits, xtensor, ztensor)
-```
-and finally call
-```julia
-sol = QAOA.evolve(tensor_problem, T_final, t -> 1 - schedule_function(t), schedule_function)
-```
-which uses a generalized two-component schedule (for the driver and problem parts of the total Hamiltonian, respectively).
-
-
-!!! note
-    `TensorProblem` can also deal with arbitrary higher-order tensors for the driver Hamiltonian!
+```

docs/src/index.md

@@ -1,6 +1,6 @@
 # Welcome!
 
-`QAOA.jl` is a lightweight implementation of the [Quantum Approximate Optimization Algorithm (QAOA)](https://arxiv.org/abs/1411.4028) based on [`Yao.jl`](https://github.com/QuantumBFS/Yao.jl). Furthermore, it implements the [mean-field Approximate Optimization Algorithm](https://arxiv.org/abs/2303.00329), which is a useful tool to simulate quantum annealing and the QAOA in the mean-field approximation.
+`QAOA.jl` is a lightweight implementation of the [Quantum Approximate Optimization Algorithm (QAOA)](https://arxiv.org/abs/1411.4028) based on [`Yao.jl`](https://github.com/QuantumBFS/Yao.jl). Furthermore, it implements the [Mean-Field Approximate Optimization Algorithm](https://link.aps.org/doi/10.1103/PRXQuantum.4.030335), which is a useful tool to simulate quantum annealing and the QAOA in the mean-field approximation.
 
 ## Installation
 
@@ -40,8 +40,6 @@ optimize_parameters(problem::Problem, beta_and_gamma::Vector{Float64}; niter::In
 ```@docs
 evolve!(S::Vector{<:Vector{<:Real}}, h::Vector{<:Real}, J::Matrix{<:Real}, β::Vector{<:Real}, γ::Vector{<:Real})
 evolve!(S::Vector{<:Vector{<:Vector{<:Real}}}, h::Vector{<:Real}, J::Matrix{<:Real}, β::Vector{<:Real}, γ::Vector{<:Real})
-evolve(h::Vector{<:Real}, J::Matrix{<:Real}, T_final::Float64, schedule::Function; rtol=1e-4, atol=1e-6)
-evolve(tensor_problem::TensorProblem, T_final::Float64, schedule_x::Function, schedule_z::Function; rtol=1e-4, atol=1e-6)
 expectation(S::Vector{<:Vector{<:Real}}, h::Vector{<:Real}, J::Matrix{<:Real})
 mean_field_solution(problem::Problem, β::Vector{<:Real}, γ::Vector{<:Real})
 mean_field_solution(S::Vector{<:Vector{<:Real}})