Load Classical Data With Data Loading

curl --request POST \
  --url 'https://api.example.com/ai/load_classical_data_with_data_loading?HAIQU_API_KEY=' \
  --header 'Content-Type: application/json' \
  --data '
{
  "parameters": {},
  "experiment_id": "",
  "name": "",
  "num_qubits": 123,
  "distribution_name": "<string>"
}
'

{
  "context": "<string>"
}

API Reference

Load Classical Data With Data Loading

Submit a classical-to-quantum data-loading job and return polling context.

Use this tool when the caller needs a generated circuit from classical data before execution or later circuit analysis. The request must use one of the supported dl_type values, and the response schema is a lightweight {"context": ...} wrapper rather than a structured job object.

When to Use:

Call this when classical data must be turned into a circuit before execution or later circuit analysis.
Call this when the user has already chosen a concrete data-loading mode such as DistributionLoading or VectorLoading.

Constraints:

dl_type must be exactly one of DistributionLoading, VectorLoading, BlockVectorLoading, MpsLoading, or EntangledManifoldEmbedding.
MCP-layer validation checks required parameter keys for the selected mode and enforces the BlockVectorLoading exclusivity rule.
The MCP layer does not reject every unknown extra key or fully validate parameter value domains before delegating to the backend.

Notes:

DistributionLoading prepares a probability distribution state.
VectorLoading prepares an arbitrary real or complex vector state.
MpsLoading prepares an arbitrary state from a matrix product state.
BlockVectorLoading prepares a block-wise vector or matrix state.
EntangledManifoldEmbedding encodes real data into a quantum state with controllable entanglement.
After submission, callers should use get_job_results_and_status to retrieve terminal outputs such as the generated circuit ID.

Args: user: Authenticated user resolved from the API key. data: Data-loading payload. dl_type must be exactly one of DistributionLoading, VectorLoading, BlockVectorLoading, MpsLoading, or EntangledManifoldEmbedding. db: Active database session.

Returns: A lightweight context wrapper summarizing the submission and next polling step.

Raises: HTTPException: Raised with 404 if the experiment is unavailable for the user, or 400 if payload validation fails.

POST

load_classical_data_with_data_loading

Load Classical Data With Data Loading

curl --request POST \
  --url 'https://api.example.com/ai/load_classical_data_with_data_loading?HAIQU_API_KEY=' \
  --header 'Content-Type: application/json' \
  --data '
{
  "parameters": {},
  "experiment_id": "",
  "name": "",
  "num_qubits": 123,
  "distribution_name": "<string>"
}
'

{
  "context": "<string>"
}

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. distribution_name: Distribution identifier required for DistributionLoading.

parameters

Parameters · object

required

Mode-specific parameters. For DistributionLoading: distribution_name, num_qubits, interval_start, interval_end, loc, scale, num_layers, truncation_cutoff (+ optional scipy distribution shape args). For VectorLoading: data, num_layers, truncation_cutoff, fine_tuning_iterations. For BlockVectorLoading: data, (num_blocks or target_num_qubits), num_layers, truncation_cutoff, fine_tuning_iterations. For EntangledManifoldEmbedding: data, density, real, periodicity, num_layers, truncation_cutoff, fine_tuning_iterations. For MpsLoading: mps, shape, num_layers, truncation_cutoff, fine_tuning_iterations.

dl_type

enum<string>

required

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

Available options:

DistributionLoading,

VectorLoading,

BlockVectorLoading,

EntangledManifoldEmbedding,

MpsLoading

experiment_id

string | null

default:""

name

string | null

default:""

num_qubits

integer | null

Optional qubit count. Required for DistributionLoading; optional for VectorLoading and EntangledManifoldEmbedding; ignored for BlockVectorLoading.

distribution_name

string | null

Required for DistributionLoading (e.g. norm, uniform, beta).

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

Compress Circuit With State Compression List Codes

⌘I