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POST
Load Classical Data With Data Loading

Authorizations

HAIQU_API_KEY
string
query
required

Body

application/json

Define payload for MCP classical-to-quantum data loading jobs.

MCP-layer validation is limited to mode-specific required-key checks plus the BlockVectorLoading exclusivity rule implemented by :func:haiqu.api.mcp.data_loading.validation.validate_data_loading_payload.

Attributes: experiment_id: Parent experiment identifier for the data-loading job. name: Optional job name. dl_type: Data-loading mode selector. parameters: Mode-specific parameters forwarded to the backend. num_qubits: Optional qubit count. Required for DistributionLoading, MultivariateDistributionLoading and FunctionLoading. distribution_name: Distribution identifier required for DistributionLoading (1D) and MultivariateDistributionLoading (2D distribution or copula).

parameters
Parameters · object
required

Mode-specific parameters. For DistributionLoading (1D scipy.stats distributions): distribution_name, num_qubits, interval_start, interval_end, loc, scale (+ optional scipy distribution shape args). For MultivariateDistributionLoading (2D distributions and copulas): interval (a [start, end] pair shared by both dimensions or a nested [[x_start, x_end], [y_start, y_end]] pair), encoding (probability or amplitude), plus distribution_params for direct 2D distributions, or copula_params, marginal_distribution_names, marginal_distribution_params (two entries each) for copulas. For VectorLoading: data. For BlockVectorLoading: data and exactly one of num_blocks or target_num_qubits. For EntangledManifoldEmbedding: data, density, real, periodicity. For MpsLoading: mps, shape. For FunctionLoading: func (math expression string in the single variable x, e.g. exp(-x**2)), interval_start, interval_end. For FourierLoading: fourier_coefficients (nested list, ascending in frequency along every axis with no fftshift applied) is the only required key. Optional: num_qubits and min_freqs (an integer shared by all dimensions or one entry per dimension) and long_range (default false). Every mode also accepts max_time (a soft limit in seconds) and synthesis_options, which chooses how the circuit is built from the prepared state and defaults to the mode's own settings when omitted. synthesis_options requires a method key, and each method accepts only its own parameters: analytical (cheapest, best on smooth targets) takes num_layers, layer_type (V, linear or reverse_linear), bond_dim_layer, entanglement_cutoff and fine_tuning_iterations; blockwise (slower, usually fewer CX gates) takes the same minus entanglement_cutoff; variational (shallowest, brickwork layers) takes num_layers, layer_type (additionally brickwork), gate_cnots (1, 2 or 3), force_complex, entanglement_cutoff and fine_tuning_iterations. Passing a parameter to a method that does not accept it is rejected.

dl_type
enum<string>
required

Data loading mode. Use one of: DistributionLoading, MultivariateDistributionLoading, VectorLoading, BlockVectorLoading, EntangledManifoldEmbedding, MpsLoading, FunctionLoading, FourierLoading.

Available options:
DistributionLoading,
MultivariateDistributionLoading,
VectorLoading,
BlockVectorLoading,
EntangledManifoldEmbedding,
MpsLoading,
FunctionLoading,
FourierLoading
experiment_id
string | null
default:""
name
string | null
default:""
description
string | null
default:""
Maximum string length: 1024
num_qubits
integer | null

Optional qubit count. Required for DistributionLoading, FunctionLoading and MultivariateDistributionLoading (where it is split between the two dimensions); optional for VectorLoading and EntangledManifoldEmbedding; ignored for BlockVectorLoading. Also ignored for FourierLoading, which takes its per-dimension output width from parameters.num_qubits.

distribution_name
string | null

Required for DistributionLoading (a 1D scipy.stats distribution, e.g. norm, uniform, beta) and for MultivariateDistributionLoading (a 2D distribution or copula: bivariate_student_t, bivariate_gamma, bivariate_von_mises, marshall_olkin_weibull, bivariate_poisson_normal_approx, gaussian_copula, clayton_copula, gumbel_copula, frank_copula).

Response

Successful Response

Represent context payload returned after data-loading submission.

Attributes: context: Submission summary text including the created job ID and next polling step. This response model does not expose structured job_id or output circuit_id fields outside the text.

context
string
required