Variational
Variational optimization endpoint. Creates a variational job.
Args: user (User): User authenticated with api key. data (VariationalProblemSubmitModel): Variational job arguments. db: Database.
Returns: VariationalJobModel: The new job object.
Authorizations
Body
Submit model for variational problem execution.
Configuration options for the NFT (Nakanishi-Fujii-Todo) optimizer.
The NFT algorithm is a gradient-free optimizer designed for variational quantum algorithms. For detailed information about the algorithm, see the paper: https://arxiv.org/abs/1903.12166
Preconditions: NFT requires the following conditions on the parameterized quantum circuit:
1. Parameters must be independent: each parameter must appear in exactly one
gate (no reusing the same parameter across multiple gates).
2. Parameterized gates must be rotations of the form ``R_j(θ_j) = exp(-i*θ_j*A_j/2)``
where ``A_j² = I`` (e.g., RX, RY, RZ gates satisfy this).
3. The cost function must be a sum of expectation values of Hermitian operators:
``L(θ) = Σ_k w_k ⟨ψ_k|U†(θ) H_k U(θ)|ψ_k⟩``.
Scaling: NFT updates one parameter at a time. Each full sweep through N parameters requires ≥2N function evaluations (depending on reset_interval).
Args: randomized_order: If True, shuffles the order of parameters to update each lap (full sweep through all parameters). Default: False. reset_interval: How often to reset the recycled loss value. Set to 0 to disable resets. Default: 32. maxfev: Maximum number of function evaluations (circuit executions). Optimization stops when this limit is reached. Default: 1024. maxiter: Maximum number of iterations (parameter updates). Default: 500. eps: Small epsilon value to avoid division by zero in the analytic solution. Default: 1e-32.
Notes: Stopping criterion: Optimization stops when either maxfev or maxiter is reached, whichever comes first.
Function evaluations per iteration: Each iteration uses 2-3 function
evaluations. The very first iteration and the first iteration of each
reset interval use 3 evaluations. Subsequent iterations reuse the
previous optimal value, requiring only 2 evaluations.
Example: >>> from haiqu.sdk.qml import NFTOptimizerOptions >>> optimizer = NFTOptimizerOptions(maxfev=2048, maxiter=100)
Response
Successful Response
Job returned for variational problem execution.
Class for job status.
Submitted, Initializing, Queued, Validating, Running, Cancelled, Done, Error Class for job types.
User local job, Analytics, Device specific analytics, Data Loading, Run, State Compression, Transpilation, Variational, Pretraining, SKQD