def vqe(molecule='H2', depth=6, max_trials=200, shots=1):
if molecule == 'H2':
n_qubits = 2
Z1 = 1
Z2 = 1
min_distance = 0.2
max_distance = 4
elif molecule == 'LiH':
n_qubits = 4
Z1 = 1
Z2 = 3
min_distance = 0.5
max_distance = 5
else:
raise QISKitError("Unknown molecule for VQE.")
# Read Hamiltonian
ham_name = os.path.join(os.path.dirname(__file__),
molecule + '/' + molecule + 'Equilibrium.txt')
pauli_list = Hamiltonian_from_file(ham_name)
H = make_Hamiltonian(pauli_list)
# Exact Energy
exact = np.amin(la.eig(H)[0]).real
print('The exact ground state energy is: {}'.format(exact))
# Optimization
device = 'local_qasm_simulator'
qp = QuantumProgram()
if shots != 1:
H = group_paulis(pauli_list)
entangler_map = qp.get_backend_configuration(device)['coupling_map']
if entangler_map == 'all-to-all':
entangler_map = {i: [j for j in range(n_qubits) if j != i] for i in range(n_qubits)}
else:
entangler_map = mapper.coupling_list2dict(entangler_map)
initial_theta = np.random.randn(2 * n_qubits * depth) # initial angles
initial_c = 0.01 # first theta perturbations
target_update = 2 * np.pi * 0.1 # aimed update on first trial
save_step = 20 # print optimization trajectory
cost = partial(cost_function, qp, H, n_qubits, depth, entangler_map, shots, device)
SPSA_params = SPSA_calibration(cost, initial_theta, initial_c, target_update, stat=25)
output = SPSA_optimization(cost, initial_theta, SPSA_params, max_trials, save_step, last_avg=1)
return qp
def transpile_dag(dag,
basis_gates='u1,u2,u3,cx,id',
coupling_map=None,
initial_layout=None,
get_layout=False,
format='dag',
seed_mapper=None,
pass_manager=None):
"""Transform a dag circuit into another dag circuit (transpile), through
consecutive passes on the dag.
Args:
dag (DAGCircuit): dag circuit to transform via transpilation
basis_gates (str): a comma separated string for the target basis gates
coupling_map (list): A graph of coupling::
[
[control0(int), target0(int)],
[control1(int), target1(int)],
]
eg. [[0, 2], [1, 2], [1, 3], [3, 4]}
initial_layout (dict): A mapping of qubit to qubit::
{
("q", start(int)): ("q", final(int)),
...
}
eg.
{
("q", 0): ("q", 0),
("q", 1): ("q", 1),
("q", 2): ("q", 2),
("q", 3): ("q", 3)
}
get_layout (bool): flag for returning the final layout after mapping
format (str): The target format of the compilation:
{'dag', 'json', 'qasm'}
seed_mapper (int): random seed_mapper for the swap mapper
pass_manager (PassManager): pass manager instance for the transpilation process
If None, a default set of passes are run.
Otherwise, the passes defined in it will run.
If contains no passes in it, no dag transformations occur.
Returns:
DAGCircuit: transformed dag
DAGCircuit, dict: transformed dag along with the final layout on backend qubits
Raises:
TranspilerError: if the format is not valid.
"""
# TODO: `basis_gates` will be removed after we have the unroller pass.
# TODO: `coupling_map`, `initial_layout`, `get_layout`, `seed_mapper` removed after mapper pass.
# TODO: move this to the mapper pass
num_qubits = sum([qreg.size for qreg in dag.qregs.values()])
if num_qubits == 1 or coupling_map == "all-to-all":
coupling_map = None
final_layout = None
if pass_manager:
# run the passes specified by the pass manager
# TODO return the property set too. See #1086
dag = pass_manager.run_passes(dag)
else:
# default set of passes
# TODO: move each step here to a pass, and use a default passmanager below
basis = basis_gates.split(',') if basis_gates else []
dag_unroller = _dagunroller.DagUnroller(dag,
_dagbackend.DAGBackend(basis))
dag = dag_unroller.expand_gates()
# if a coupling map is given compile to the map
if coupling_map:
logger.info("pre-mapping properties: %s", dag.property_summary())
# Insert swap gates
coupling = Coupling(coupling_list2dict(coupling_map))
removed_meas = remove_last_measurements(dag)
logger.info("measurements moved: %s", removed_meas)
logger.info("initial layout: %s", initial_layout)
dag, final_layout, last_layout = swap_mapper(dag,
coupling,
initial_layout,
trials=20,
seed=seed_mapper)
logger.info("final layout: %s", final_layout)
# Expand swaps
dag_unroller = _dagunroller.DagUnroller(
dag, _dagbackend.DAGBackend(basis))
dag = dag_unroller.expand_gates()
# Change cx directions
dag = direction_mapper(dag, coupling)
# Simplify cx gates
cx_cancellation(dag)
# Simplify single qubit gates
dag = optimize_1q_gates(dag)
return_last_measurements(dag, removed_meas, last_layout)
logger.info("post-mapping properties: %s", dag.property_summary())
# choose output format
# TODO: do we need all of these formats, or just the dag?
if format == 'dag':
compiled_circuit = dag
elif format == 'json':
# FIXME: JsonBackend is wrongly taking an ordered dict as basis, not list
dag_unroller = _dagunroller.DagUnroller(
dag, _jsonbackend.JsonBackend(dag.basis))
compiled_circuit = dag_unroller.execute()
elif format == 'qasm':
compiled_circuit = dag.qasm()
else:
raise TranspilerError('unrecognized circuit format')
if get_layout:
return compiled_circuit, final_layout
return compiled_circuit
def transpile(dag_circuit,
basis_gates='u1,u2,u3,cx,id',
coupling_map=None,
initial_layout=None,
get_layout=False,
format='dag',
seed=None,
pass_manager=None):
"""Transform a dag circuit into another dag circuit (transpile), through
consecutive passes on the dag.
Args:
dag_circuit (DAGCircuit): dag circuit to transform via transpilation
basis_gates (str): a comma seperated string for the target basis gates
coupling_map (list): A graph of coupling::
[
[control0(int), target0(int)],
[control1(int), target1(int)],
]
eg. [[0, 2], [1, 2], [1, 3], [3, 4]}
initial_layout (dict): A mapping of qubit to qubit::
{
("q", start(int)): ("q", final(int)),
...
}
eg.
{
("q", 0): ("q", 0),
("q", 1): ("q", 1),
("q", 2): ("q", 2),
("q", 3): ("q", 3)
}
get_layout (bool): flag for returning the layout
format (str): The target format of the compilation:
{'dag', 'json', 'qasm'}
seed (int): random seed for simulators
pass_manager (PassManager): pass manager instance for the tranpilation process
If None, a default set of passes are run.
Otherwise, the passes defined in it will run.
If contains no passes in it, no dag transformations occur.
Returns:
object: If get_layout == False, the compiled circuit in the specified
format. If get_layout == True, a tuple is returned, with the
second element being the layout.
Raises:
TranspilerError: if the format is not valid.
"""
final_layout = None
if pass_manager:
# run the passes specified by the pass manager
for pass_ in pass_manager.passes():
pass_.run(dag_circuit)
else:
# default set of passes
# TODO: move each step here to a pass, and use a default passmanager below
basis = basis_gates.split(',') if basis_gates else []
dag_unroller = DagUnroller(dag_circuit, DAGBackend(basis))
dag_circuit = dag_unroller.expand_gates()
# if a coupling map is given compile to the map
if coupling_map:
logger.info("pre-mapping properties: %s",
dag_circuit.property_summary())
# Insert swap gates
coupling = Coupling(coupling_list2dict(coupling_map))
logger.info("initial layout: %s", initial_layout)
dag_circuit, final_layout = swap_mapper(dag_circuit,
coupling,
initial_layout,
trials=20,
seed=seed)
logger.info("final layout: %s", final_layout)
# Expand swaps
dag_unroller = DagUnroller(dag_circuit, DAGBackend(basis))
dag_circuit = dag_unroller.expand_gates()
# Change cx directions
dag_circuit = direction_mapper(dag_circuit, coupling)
# Simplify cx gates
cx_cancellation(dag_circuit)
# Simplify single qubit gates
dag_circuit = optimize_1q_gates(dag_circuit)
logger.info("post-mapping properties: %s",
dag_circuit.property_summary())
# choose output format
# TODO: do we need all of these formats, or just the dag?
if format == 'dag':
compiled_circuit = dag_circuit
elif format == 'json':
# FIXME: JsonBackend is wrongly taking an ordered dict as basis, not list
dag_unroller = DagUnroller(dag_circuit, JsonBackend(dag_circuit.basis))
compiled_circuit = dag_unroller.execute()
elif format == 'qasm':
compiled_circuit = dag_circuit.qasm()
else:
raise TranspilerError('unrecognized circuit format')
if get_layout:
return compiled_circuit, final_layout
return compiled_circuit
def vqe(molecule='H2', depth=6, max_trials=200, shots=1):
if molecule == 'H2':
n_qubits = 2
Z1 = 1
Z2 = 1
min_distance = 0.2
max_distance = 4
elif molecule == 'LiH':
n_qubits = 4
Z1 = 1
Z2 = 3
min_distance = 0.5
max_distance = 5
else:
raise QISKitError("Unknown molecule for VQE.")
# Read Hamiltonian
ham_name = os.path.join(os.path.dirname(__file__),
molecule + '/' + molecule + 'Equilibrium.txt')
pauli_list = Hamiltonian_from_file(ham_name)
H = make_Hamiltonian(pauli_list)
# Exact Energy
exact = np.amin(la.eig(H)[0]).real
print('The exact ground state energy is: {}'.format(exact))
# Optimization
device = 'local_qasm_simulator'
if shots == 1:
device = 'local_statevector_simulator'
if 'statevector' not in device:
H = group_paulis(pauli_list)
entangler_map = get_backend(device).configuration()['coupling_map']
if entangler_map == 'all-to-all':
entangler_map = {
i: [j for j in range(n_qubits) if j != i]
for i in range(n_qubits)
}
else:
entangler_map = mapper.coupling_list2dict(entangler_map)
initial_theta = np.random.randn(2 * n_qubits * depth) # initial angles
initial_c = 0.01 # first theta perturbations
target_update = 2 * np.pi * 0.1 # aimed update on first trial
save_step = 20 # print optimization trajectory
cost = partial(cost_function, H, n_qubits, depth, entangler_map, shots,
device)
SPSA_params, circuits_cal = SPSA_calibration(cost,
initial_theta,
initial_c,
target_update,
stat=25)
output, circuits_opt = SPSA_optimization(cost,
initial_theta,
SPSA_params,
max_trials,
save_step,
last_avg=1)
return circuits_cal + circuits_opt