Pretrain

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Haiqu.pretrain(problem, *, max_time=60, seed=42, initial_parameters=None, name=None, description=None)

• Parameters:
  • problem (VariationalProblem | NonlinearVariationalProblem) — problem instance containing the ansatz circuit and either a single observable (linear) or a loss expression with named observables (nonlinear).
  • max_time (float) — maximal time (in seconds) the pretraining can take. If this time exceeds (not counting initialization and other overheads), then the current best result is returned. Defaults to 1 minute. Current maximal pretraining time is 15 minutes.
  • seed (int *|*None) — a seed for initial random initialization of weights. They are chosen from uniform distribution in the interval [-π,π). Defaults to 42.
  • initial_parameters (list *[*float ] *|*None) — if specified, then these weights are used instead of random ones. Defaults to None.
  • name (str *|*None) — optional name of the job. If not set, then automatic will be generated.
  • description (str *|*None) — optional description of the job. If not set, then automatic will be generated.
• Returns: Job handle to track pretraining progress and retrieve results. : Call job.result() to retrieve the pretrained ansatz parameters as a list[float] (one entry per parameter in the input VariationalProblem.ansatz), suitable for use as parameters in run() or as initial_parameters in variational_optimization(). Run help(job.result) for the full description of result and info contents.
• Return type: PretrainingJobModel

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Examples

>>> from qiskit import QuantumCircuit
>>> from qiskit.circuit.library import efficient_su2
>>> from qiskit.quantum_info import SparsePauliOp
>>> from haiqu.sdk.qml import VariationalProblem
>>> pqc = QuantumCircuit(5)
>>> pqc.compose(efficient_su2(num_qubits=pqc.num_qubits, reps=1), inplace=True)
>>> loss = SparsePauliOp(["ZIIII", "IIZXI"])
>>> problem = VariationalProblem(pqc, loss)
>>> job = haiqu.pretrain(problem, max_time=10)
>>> pretrained_params = job.result()  # accessing the pretrained parameters
>>> haiqu.run([problem.ansatz], observables=[problem.observable], parameters=[pretrained_params],
...           device=haiqu.get_device("aer_simulator")).result()  # checking the result
[[[-2.0]]]  # result may vary. -2 is the optimal loss for two independent pauli strings

>>> from haiqu.sdk.qml import NonlinearVariationalProblem
>>> problem = NonlinearVariationalProblem(
...     pqc, "1 - x/y", {"x": [("ZIIII", 1.0)], "y": [("0IIII", 0.5), ("1IIZI", -0.5)]}
... )
>>> job = haiqu.pretrain(problem, max_time=10)