state_preparation_evolutionary Namespace Reference

Variables
	backend = Aer.get_backend('unitary_simulator')
	Qiskit backend for simulator. More...
dictionary	config
	state preparation ##################################### More...
	cStatePrep = N_Qubit_State_Preparation_adaptive( State, config=config )
	decomp_error = cStatePrep.Optimization_Problem( params_BFGS )
	job = execute(qc, backend)
	job execution and getting the result as an object More...
int	levels = 1
	parameter_num = cStatePrep.get_Parameter_Num()
	parameters = np.random.randn( parameter_num )
	params_AGENTS = cStatePrep.get_Optimized_Parameters()
	params_BFGS = cStatePrep.get_Optimized_Parameters()
	qc = QuantumCircuit.from_qasm_file( '../../benchmarks/IBM/alu-v4_37.qasm')
	quantum_circuit = cStatePrep.get_Qiskit_Circuit()
	Qiskit quantum circuit. More...
	result = job.result()
	the result of the Qiskit job More...
	State = np.ascontiguousarray( Umtx[:,0].copy() )
	Umtx = result.get_unitary(qc)
	the unitary matrix from the result object More...

Variable Documentation

backend

state_preparation_evolutionary.backend = Aer.get_backend('unitary_simulator')

Qiskit backend for simulator.

Definition at line 46 of file state_preparation_evolutionary.py.

config

dictionary state_preparation_evolutionary.config

Initial value:

=  {      'agent_lifetime':1000,
                'agent_num': 64,
                'max_inner_iterations_agent': 100000,
                'max_inner_iterations_compression': 1000,
                'max_inner_iterations' : 100,
                'max_inner_iterations_final': 100,          
                'Randomized_Radius': 0.3, 
                'randomized_adaptive_layers': 1,
                'optimization_tolerance_agent': 1e-8,
                'optimization_tolerance': 1e-16,
          'convergence_length': 10}

state preparation #####################################

Definition at line 68 of file state_preparation_evolutionary.py.

cStatePrep

state_preparation_evolutionary.cStatePrep = N_Qubit_State_Preparation_adaptive( State, config=config )

Definition at line 84 of file state_preparation_evolutionary.py.

decomp_error

state_preparation_evolutionary.decomp_error = cStatePrep.Optimization_Problem( params_BFGS )

Definition at line 141 of file state_preparation_evolutionary.py.

job

state_preparation_evolutionary.job = execute(qc, backend)

job execution and getting the result as an object

Definition at line 49 of file state_preparation_evolutionary.py.

levels

int state_preparation_evolutionary.levels = 1

Definition at line 95 of file state_preparation_evolutionary.py.

parameter_num

state_preparation_evolutionary.parameter_num = cStatePrep.get_Parameter_Num()

Definition at line 103 of file state_preparation_evolutionary.py.

parameters

state_preparation_evolutionary.parameters = np.random.randn( parameter_num )

Definition at line 105 of file state_preparation_evolutionary.py.

params_AGENTS

state_preparation_evolutionary.params_AGENTS = cStatePrep.get_Optimized_Parameters()

Definition at line 132 of file state_preparation_evolutionary.py.

params_BFGS

state_preparation_evolutionary.params_BFGS = cStatePrep.get_Optimized_Parameters()

Definition at line 140 of file state_preparation_evolutionary.py.

qc

state_preparation_evolutionary.qc = QuantumCircuit.from_qasm_file( '../../benchmarks/IBM/alu-v4_37.qasm')

Definition at line 37 of file state_preparation_evolutionary.py.

quantum_circuit

state_preparation_evolutionary.quantum_circuit = cStatePrep.get_Qiskit_Circuit()

Qiskit quantum circuit.

Definition at line 171 of file state_preparation_evolutionary.py.

result

state_preparation_evolutionary.result = job.result()

the result of the Qiskit job

Definition at line 51 of file state_preparation_evolutionary.py.

State

state_preparation_evolutionary.State = np.ascontiguousarray( Umtx[:,0].copy() )

Definition at line 59 of file state_preparation_evolutionary.py.

Umtx

state_preparation_evolutionary.Umtx = result.get_unitary(qc)

the unitary matrix from the result object

Definition at line 54 of file state_preparation_evolutionary.py.