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Haiqu.vector_loading(data, num_qubits=None, num_layers=2, truncation_cutoff=1e-06, fine_tuning_iterations=20, name=None)

Generate a quantum circuit that prepares an arbitrary real or complex vector. Given a vector of data, this method creates a Data Loading job that runs in the Haiqu cloud. The result of this job is a circuit which can be used to supply the vector to a quantum algorithm for processing. The cost and time of this job can be estimated with vector_loading_estimates(). The complexity and quality of the generated circuit can be controlled by the num_layers, truncation_cutoff, and fine_tuning_iterations parameters. If len(data) < 2**num_qubits, the vector will be padded with zeros.
  • Parameters:
    • data (Sequence *[*Number ]) — The vector with data to encode (length of data is from 1 to 2**20 values).
    • num_qubits (int | None) — (int | None): The number of qubits in the generated circuit (from 1 to 20 qubits). If None (default), it is set automatically from the size of the data.
    • num_layers (int) — The number of layers in the generated circuit (from 1 to 15 layers). More layers can improve the quality of the output vector at the cost of a deeper circuit. Defaults to 2.
    • truncation_cutoff (Real) — The entanglement cutoff for later layers. Increasing this threshold may result in a smaller (but more approximate) circuit. Defaults to 1e-6.
    • fine_tuning_iterations (int) — The maximum number of fine-tuning iterations to perform after each layer is added. Increasing this limit may improve the quality of the circuit by using more classical resources. Defaults to 20, maximal is 200.
    • name (str | None) — The name for the job and the produced circuit. If None (default), a name will be automatically generated.
  • Returns: The Data Loading job that will generate the circuit for the data vector. : Call job.result() to retrieve a Qiskit-compatible gate (HaiquCircuitGate) that prepares the input data vector. job.quality is the achieved state fidelity vs. the ideal target vector; job.info exposes loader metadata (fidelity). Run help(job.result) for the full description of result and info contents.
  • Return type: DataLoadingJobModel

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Examples

>>> bell_state = [1, 0, 0, 1]  # normalization is not required
>>> job = haiqu.vector_loading(data=bell_state, name="Bell state Vector Loading")
>>> vl_gate = job.result()  # vl_gate is a Qiskit-compatible gate
>>> fidelity = job.quality
>>> print(f"Bell state was loaded with fidelity {fidelity:.6f}")
Bell state was loaded with fidelity 1.000000
>>> print(f"Vector loading required {job.num_qubits} qubits")
Vector loading required 2 qubits
>>> circuit = qiskit.QuantumCircuit(job.num_qubits)
>>> circuit.append(vl_gate, range(job.num_qubits))
>>> circuit.draw()
     ┌────────────────────────────────────────────────────────────┐
q_0: ┤0                                                           ├
     │  Haiqucircuit(circ-12345678-1234-5678-1234-567812345678,2) │
q_1: ┤1                                                           ├
     └────────────────────────────────────────────────────────────┘

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Haiqu.vector_loading_estimates(data, num_qubits=None, num_layers=2, truncation_cutoff=1e-06, fine_tuning_iterations=20, name=None)

Estimate the cost and time of a Data Loading job created by vector_loading(). The parameters are the same as for vector_loading(). Once you discover values that result in acceptable cost and time estimates, you can remove _estimates from the end of the method name and call vector_loading().
  • Parameters:
    • data (Sequence *[*Number ]) — The vector with data to encode (length of data is from 1 to 2**20 values).
    • num_qubits (int | None) — (int | None): The number of qubits in the generated circuit (from 1 to 20 qubits). If None (default), it is set automatically from the size of the data.
    • num_layers (int) — The number of layers in the generated circuit (from 1 to 15 layers). More layers can improve the quality of the output vector at the cost of a deeper circuit. Defaults to 2.
    • truncation_cutoff (Real) — The entanglement cutoff for later layers. Increasing this threshold may result in a smaller (but more approximate) circuit. Defaults to 1e-6.
    • fine_tuning_iterations (int) — The maximum number of fine-tuning iterations to perform after each layer is added. Increasing this limit may improve the quality of the circuit by using more classical resources. Defaults to 20, maximal is 200.
    • name (str | None) — The name for the job and the produced circuit. If None (default), a name will be automatically generated.
  • Returns: The estimated time (in seconds) and cost (in Haiqu Credits).
  • Return type: DataLoadingEstimatesModel

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Examples

>>> est = haiqu.vector_loading_estimates(
...     num_qubits=10,
...     num_layers=5,
>>> )
>>> est
DataLoadingEstimatesModel(estimated_time=221.07399999999998, estimated_cost=0.06308276)
>>> est.draw()  # in Jupyter notebook