Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.haiqu.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
Haiqu.observable_backpropagation(circuit, observables, max_qwc_groups=50, max_error_total=0.05, max_error_per_slice=0.005, log=True)

Optimize the observables for a circuit with observable backpropagation. This method wraps the Qiskit Operator Backpropagation (OBP) functionality to preprocess your circuits and observables for efficient execution.
  • Parameters:
    • circuit (QuantumCircuit | CircuitModel) — The quantum circuit to optimize.
    • observables (SparsePauliOp | list *(*SparsePauliOp )) — The observable(s) to optimize. Can be a single SparsePauliOp or list of SparsePauliOp. The order of Pauli terms follows the Qiskit reversed-order convention.
    • max_qwc_groups (int) — Maximum number of qubit-wise commuting groups to create. Defaults to 50. Treat with caution as increasing this value may lead to SIGNIFICANTLY higher computational costs!
    • max_error_total (float) — Maximum error allowed for the entire circuit. Defaults to 0.05.
    • max_error_per_slice (float) — Maximum error allowed per slice of the circuit. Defaults to 0.005.
    • log (bool) — If True (default), logs the reduced circuits to the Haiqu cloud.
  • Returns: A list of reduced circuits and a list of backpropagated observables.
  • Return type: tuple[list, list]

​
Examples

>>> from qiskit import QuantumCircuit
>>> from qiskit.quantum_info import SparsePauliOp
>>> qc = QuantumCircuit(2)
>>> qc.h(0)
>>> qc.cx(0, 1)
>>> obs = [SparsePauliOp("ZZ"), SparsePauliOp("XX")]
>>> optimized_circuits, optimized_obs = haiqu.observable_backpropagation(circuit=qc, observables=obs, log=False)
>>> [len(qc) for qc in optimized_circuits]
[0, 0]
>>> optimized_obs
[SparsePauliOp(['ZI'],
               coeffs=[1.+0.j]),
 SparsePauliOp(['IZ'],
               coeffs=[1.+0.j])]