CrosstalkAdaptiveSchedule
qiskit.transpiler.passes.CrosstalkAdaptiveSchedule(*args, **kwargs)
Bases: TransformationPass
Crosstalk mitigation through adaptive instruction scheduling.
CrosstalkAdaptiveSchedule initializer.
Parameters

backend_prop (BackendProperties) – backend properties object

crosstalk_prop (dict (opens in a new tab)) –
crosstalk properties object crosstalk_prop[g1][g2] specifies the conditional error rate of g1 when g1 and g2 are executed simultaneously. g1 should be a twoqubit tuple of the form (x,y) where x and y are physical qubit ids. g2 can be either twoqubit tuple (x,y) or singlequbit tuple (x). We currently ignore crosstalk between pairs of singlequbit gates. Gate pairs which are not specified are assumed to be crosstalk free.
Example:
crosstalk_prop = {(0, 1) : {(2, 3) : 0.2, (2) : 0.15}, (4, 5) : {(2, 3) : 0.1}, (2, 3) : {(0, 1) : 0.05, (4, 5): 0.05}}
The keys of the crosstalk_prop are tuples for ordered tuples for CX gates e.g., (0, 1) corresponding to CX 0, 1 in the hardware. Each key has an associated value dict which specifies the conditional error rates with nearby gates e.g.,
(0, 1) : {(2, 3) : 0.2, (2) : 0.15}
means that CNOT 0, 1 has an error rate of 0.2 when it is executed in parallel with CNOT 2,3 and an error rate of 0.15 when it is executed in parallel with a single qubit gate on qubit 2. 
weight_factor (float (opens in a new tab)) – weight of gate error/crosstalk terms in the objective $weight_factor*fidelities + (1weight_factor)*decoherence errors$. Weight can be varied from 0 to 1, with 0 meaning that only decoherence errors are optimized and 1 meaning that only crosstalk errors are optimized. weight_factor should be tuned per application to get the best results.

measured_qubits (list (opens in a new tab)) – a list of qubits that will be measured in a particular circuit. This arg need not be specified for circuits which already include measure gates. The arg is useful when a subsequent module such as state_tomography_circuits inserts the measure gates. If CrosstalkAdaptiveSchedule is made aware of those measurements, it is included in the optimization.

target (Target) – A target representing the target backend, if both
backend_prop
and this are specified then this argument will take precedence andcoupling_map
will be ignored.
Raises
ImportError (opens in a new tab) – if unable to import z3 solver
Attributes
is_analysis_pass
Check if the pass is an analysis pass.
If the pass is an AnalysisPass, that means that the pass can analyze the DAG and write the results of that analysis in the property set. Modifications on the DAG are not allowed by this kind of pass.
is_transformation_pass
Check if the pass is a transformation pass.
If the pass is a TransformationPass, that means that the pass can manipulate the DAG, but cannot modify the property set (but it can be read).
Methods
assign_gate_id
assign_gate_id(dag)
ID for each gate
basic_bounds
basic_bounds()
Basic variable bounds for optimization
check_dag_dependency
check_dag_dependency(gate1, gate2)
gate2 is a DAG dependent of gate1 if it is a descendant of gate1
check_xtalk_dependency
check_xtalk_dependency(t_1, t_2)
Check if two gates have a crosstalk dependency. We do not consider crosstalk between pairs of single qubit gates.
coherence_constraints
coherence_constraints()
Set decoherence errors based on qubit lifetimes
create_updated_dag
create_updated_dag(layers, barriers)
Given a set of layers and barriers, construct a new dag
create_z3_vars
create_z3_vars()
Setup the variables required for Z3 optimization
cx_tuple
cx_tuple(gate)
Representation for twoqubit gate Note: current implementation assumes that the CX error rates and crosstalk behavior are independent of gate direction
enforce_schedule_on_dag
enforce_schedule_on_dag(input_gate_times)
Z3 outputs start times for each gate. Some gates need to be serialized to implement the Z3 schedule. This function inserts barriers to implement those serializations
execute
execute(passmanager_ir, state, callback=None)
Execute optimization task for input Qiskit IR.
Parameters
 passmanager_ir (Any (opens in a new tab)) – Qiskit IR to optimize.
 state (PassManagerState) – State associated with workflow execution by the pass manager itself.
 callback (Callable (opens in a new tab)  None) – A callback function which is caller per execution of optimization task.
Returns
Optimized Qiskit IR and state of the workflow.
Return type
tuple (opens in a new tab)[Any (opens in a new tab), qiskit.passmanager.compilation_status.PassManagerState]
extract_crosstalk_relevant_sets
extract_crosstalk_relevant_sets()
Extract the set of program gates which potentially have crosstalk noise
extract_dag_overlap_sets
extract_dag_overlap_sets(dag)
Gate A, B are overlapping if A is neither a descendant nor an ancestor of B. Currently overlaps (A,B) are considered when A is a 2q gate and B is either 2q or 1q gate.
extract_solution
extract_solution()
Extract gate start and finish times from Z3 solution
fidelity_constraints
fidelity_constraints()
Set gate fidelity based on gate overlap conditions
filter_candidates
filter_candidates(candidates, layer, layer_id, triplet)
For a gate G and layer L, L is a candidate layer for G if no gate in L has a DAG dependency with G, and if Z3 allows gates in L and G to overlap.
find_layer
find_layer(layers, triplet)
Find the appropriate layer for a gate
gate_tuple
gate_tuple(gate)
Representation for gate
generate_barriers
generate_barriers(layers)
For each gate g, see if a barrier is required to serialize it with some previously processed gate
is_significant_xtalk
is_significant_xtalk(gate1, gate2)
Given two conditional gate error rates check if there is high crosstalk by comparing with independent error rates.
name
name()
Name of the pass.
Return type
objective_function
objective_function()
Objective function is a weighted combination of gate errors and decoherence errors
parse_backend_properties
parse_backend_properties()
This function assumes that gate durations and coherence times are in seconds in backend.properties() This function converts gate durations and coherence times to nanoseconds.
powerset
powerset(iterable)
Finds the set of all subsets of the given iterable This function is used to generate constraints for the Z3 optimization
r2f
r2f(val)
Convert Z3 Real to Python float
reset
reset()
Reset variables
run
run(dag)
Main scheduling function
scheduling_constraints
scheduling_constraints()
DAG scheduling constraints optimization Sets overlap indicator variables
singleq_tuple
singleq_tuple(gate)
Representation for singlequbit gate
solve_optimization
solve_optimization()
Setup and solve a Z3 optimization for finding the best schedule
update_status
update_status(state, run_state)
Update workflow status.
Parameters
 state (PassManagerState) – Pass manager state to update.
 run_state (RunState) – Completion status of current task.
Returns
Updated pass manager state.
Return type