infidelity_stf

Graph.infidelity_stf(sample_times, hamiltonian, target, noise_operators=None, *, name=None)

Create the total infidelity of a given system with a sampleable Hamiltonian.

See infidelity_pwc for information about the total infidelity created by this function.

Parameters

• sample_times (np.ndarray ( 1D , real )) – The times at which the Hamiltonian and noise operators (if present) should be sampled for the integration. Must start with 0, be ordered, and contain at least one element.
• hamiltonian (Stf) – The control Hamiltonian $H_{\mathrm c}(t)$
• target (Target) – The object describing the target gate $U_\mathrm{target}$ and (optionally) the filter function projector $P$
• noise_operators (list [ np.ndarray or Tensor or Stf ] or None , optional) – The perturbative noise operators $\{N_j(t)\}$
• name (str or None , optional) – The name of the node.

Returns

The total infidelity (operational infidelity plus filter function values) of the given system, with respect to the given target gate, at the last time in sample_times. If you provide a batch of Hamiltonians or noise operators, the function returns a batch of infidelities containing one infidelity for each Hamiltonian and list of noise operators in the input batches.

Return type

Tensor

SEE ALSO

Graph.infidelity_pwc : Corresponding operation for Pwc Hamiltonians.

Graph.target : Define the target operation of the time evolution.

Graph.time_evolution_operators_stf : Unitary time evolution operators for quantum systems with Stf Hamiltonians.

Examples

Calculate the infidelity of the Pauli $X$

>>> sigma_x = np.array([[0, 1], [1, 0]])
>>> hamiltonian = graph.constant_stf_operator(np.pi * sigma_x / 2)
>>> target = graph.target(sigma_x)
>>> infidelity = graph.infidelity_stf(
...     sample_times=np.linspace(0, 0.5, 100),
...     hamiltonian=hamiltonian,
...     target=target,
...     name="infidelity",
... )
>>> result = bo.execute_graph(graph=graph, output_node_names="infidelity")
>>> result["output"]["infidelity"]["value"]
0.5000000000260991