Multivariate Distribution Loading

​

Haiqu.multivariate_distribution_loading(num_qubits, distribution_name, interval=(0, 1), encoding=‘probability’, num_layers=1, truncation_cutoff=1e-06, fine_tuning_iterations=20, distribution_params=None, copula_params=None, marginal_distribution_names=None, marginal_distribution_params=None, max_time=900, name=None, job_description=None)

​

NOTE

​

NOTE

• Parameters:
  • num_qubits (int | Sequence *[*int ]) — Either the total number of qubits, split evenly across the dimensions, or a sequence of per-dimension qubit counts such as (3, 1) for an uneven split. A total must therefore be a multiple of the number of dimensions.
  • distribution_name (str) — The name of a multivariate distribution (e.g. bivariate_gamma, bivariate_student_t, bivariate_von_mises, marshall_olkin_weibull, bivariate_poisson_normal_approx) or a copula (gaussian_copula, clayton_copula, gumbel_copula, frank_copula). Names ending in _copula select copula loading and require copula_params with marginal distributions instead of distribution_params. See the multivariate distributions catalog for the full list.
  • interval (Sequence) — The spatial domain. Either a single pair (a, b) shared by all dimensions, or a sequence of per-dimension interval pairs. Each interval width b - a must be at least 1e-08. Defaults to (0, 1).
  • encoding (str) — How the pdf maps to amplitudes. "probability" (default): amplitudes are proportional to sqrt(pdf), so measurement probabilities are proportional to the pdf. "amplitude": amplitudes are proportional to the pdf.
  • num_layers (int) — The number of layers in the generated circuit (from 1 to 100 layers). More layers can improve the quality of the output distribution at the cost of a deeper circuit. Defaults to 1.
  • truncation_cutoff (Real) — The entanglement cutoff for later layers, from 0 to 1. Increasing this threshold may result in a smaller (but more approximate) circuit. Defaults to 1e-6.
  • fine_tuning_iterations (int) — The number of the gradient-based fine-tuning iterations applied after analytical compilation. Defaults to 20, maximal is 500.
  • distribution_params (dict | None) — Keyword arguments of a direct multivariate distribution (e.g. {"nu": 2.0, "rho": 0.5} for bivariate_gamma). Only valid for direct multivariate distributions.
  • copula_params (dict | None) — Keyword arguments of the named copula (e.g. {"rho": 0.5} for gaussian_copula). Only valid for copulas.
  • marginal_distribution_names (Sequence *[*str ] | None) — scipy.stats 1D distribution names used as copula marginals, e.g. ["norm", "gamma"]. Only valid for copulas.
  • marginal_distribution_params (Sequence *[*dict ] | None) — Dictionaries of scipy.stats marginal parameters, in the same order as marginal_distribution_names.
  • max_time (int | float) — Soft time limit for the job (in seconds). The job will first always produce the initial result and then limit the fine-tuning stage by the remaining time left. Defaults to 900 (15 min).
  • name (str | None) — The name for the job and the produced circuit. If None (default), a name will be automatically generated.
  • job_description (str | None) — The description for the job.
• Returns: The Data Loading job that will generate the circuit for the joint PDF. : Call job.result() to retrieve a Qiskit-compatible gate (HaiquCircuitGate) that prepares the requested distribution. job.quality is the circuit-to-MPS compilation fidelity.
• Return type: DataLoadingJobModel

​

Examples

>>> job = haiqu.multivariate_distribution_loading(
...     num_qubits=8,
...     distribution_name="bivariate_gamma",
...     interval=((0, 15), (0, 15)),
...     distribution_params={"nu": 2.0, "rho": 0.5},
... )
>>> dl_gate = job.result()  # dl_gate is a Qiskit-compatible gate

>>> job = haiqu.multivariate_distribution_loading(
...     num_qubits=8,
...     distribution_name="gaussian_copula",
...     interval=((-3, 3), (0, 10)),
...     copula_params={"rho": 0.5},
...     marginal_distribution_names=["norm", "gamma"],
...     marginal_distribution_params=[{"loc": 0, "scale": 1}, {"a": 2.0}],
... )