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 (list or tuple or 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 \(H_{\mathrm c}(t)\). You can provide either a single Hamiltonian or a batch of them.
target (Target) – The object describing the target gate \(U_\mathrm{target}\) and (optionally) the filter function projector \(P\). If you provide a batch of Hamiltonians, the function uses the same target for all the elements in the batch.
noise_operators (list[np.ndarray or Tensor or Stf], optional) – The perturbative noise operators \(\{N_j(t)\}\). The operators in the list can either be single operators or batches of them. If any of the noise operators or the Hamiltonian are batches, the batch shapes must all be broadcastable. You can omit this list if there are no noises.
name (str, 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
See also
infidelity_pwc
Corresponding operation for Pwc Hamiltonians.
target
Define the target operation of the time evolution.
time_evolution_operators_stf
Unitary time evolution operators for quantum systems with Stf Hamiltonians.
Examples
Calculate the infidelity of the Pauli \(X\) gate for a noiseless qubit.
>>> 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 = qctrl.functions.calculate_graph(graph=graph, output_node_names=["infidelity"]) >>> result.output["infidelity"]["value"] 0.5000000000260991
See more examples in the How to perform model-based optimization with user-defined basis functions user guide.