def _setup_target_models(self):
self.available_lattices_by_name = {
k: topology_predefined.__getattribute__(k)
for k in self.lattice_names
}
self.available_lattices = [
topology_predefined.__getattribute__(k) for k in self.lattice_names
]
if self.shared_true_parameters:
lattice_idx = -1
else:
lattice_idx = self.qmla_id % len(self.available_lattices)
self.log_print(
["true model set by lattice idx = {}".format(lattice_idx)])
# Rerunning subset with more resources
# lattice_idx = self.qmla_id % len(self.rerun_lattices)
self.true_lattice_name = self.lattice_names[lattice_idx]
# self.true_lattice_name = '_4_site_lattice_fully_connected'
self.true_lattice = self.available_lattices_by_name[
self.true_lattice_name]
self.true_model = self.model_from_lattice(self.true_lattice)
self.max_num_models_by_shape = {
1: 2,
2: 2,
3: 2,
4: 2,
5: 2,
6: 2,
"other": 0
}
self.num_processes_to_parallelise_over = len(self.available_lattices)
def __init__(self, exploration_rules, **kwargs):
self.base_terms = ["z"]
self.transverse_field = "x"
super().__init__(exploration_rules=exploration_rules, **kwargs)
# self.latex_string_map_subroutine = qmla.shared_functionality.latex_model_names.lattice_pauli_likewise_concise
self.true_lattice_name = "_3_site_chain_fully_connected"
self.true_lattice = topologies.__getattribute__(self.true_lattice_name)
self.true_model = self.model_from_lattice(self.true_lattice)
def setup_models(self):
self.available_lattices_by_name = {
k : topology_predefined.__getattribute__(k)
for k in self.lattice_names
}
self.available_lattices = [
topology_predefined.__getattribute__(k)
for k in self.lattice_names
]
if self._shared_true_parameters:
lattice_idx = -1
else:
lattice_idx = self.qmla_id % len(self.available_lattices)
self.true_lattice_name = self.lattice_names[ lattice_idx ]
self.true_lattice = self.available_lattices_by_name[self.true_lattice_name]
self.true_model = self.model_from_lattice(self.true_lattice)
# self.log_print(["QMLA {} using lattice {} (lattice idx {}) has model {}".format(self.qmla_id, self.true_lattice_name, lattice_idx, self.true_model)])
self.max_num_qubits = 8
self.max_num_probe_qubits = 8
self.qhl_models = [
self.model_from_lattice(l)
for l in self.available_lattices
]
self.quantisation = 'first' # 'second
if self.quantisation == 'first':
# probe transformer between formalisms - not used currently
self.probe_transformer = qmla.shared_functionality.probe_transformer.ProbeTransformation()
self.system_probes_generation_subroutine = qmla.shared_functionality.probe_set_generation.separable_probe_dict
elif self.quantisation == 'second':
# Default for FH
self.probe_transformer = qmla.shared_functionality.probe_transformer.ProbeTransformation()
self.system_probes_generation_subroutine = qmla.shared_functionality.probe_set_generation.separable_fermi_hubbard_half_filled
# TODO plot probe dict with meaningful probes wrt FH model
self.plot_probes_generation_subroutine = qmla.shared_functionality.probe_set_generation.plus_probes_dict
self.num_sites_true = construct_models.get_num_qubits(self.true_model)
self.num_qubits_true = 2*self.num_sites_true # FH uses 2 qubits per sites (up and down spin)
self.num_probes = 25
self.max_time_to_consider = 25
self.max_num_qubits = 8
self.max_num_probe_qubits = self.max_num_qubits
self.latex_string_map_subroutine = qmla.shared_functionality.latex_model_names.lattice_set_fermi_hubbard
# self.timing_insurance_factor = 0.8
self.min_param = 0.25
self.max_param = 0.75
self.true_model_terms_params = {
# 3 sites
# 'FH-hopping-sum_down_1h2_2h3_d3' : 0.25,
# 'FH-hopping-sum_up_1h2_2h3_d3' : 0.75,
# 'FH-onsite-sum_1_2_3_d3': 0.55,
# 2 sites
'FH-hopping-sum_down_1h2_d2' : 0.25,
'FH-hopping-sum_up_1h2_d2' : 0.75,
'FH-onsite-sum_1_2_d2': 0.55,
}
def __init__(self, exploration_rules, true_model=None, **kwargs):
if true_model is not None:
self.true_model = true_model
print("[LatticeSet] got true model {}".format(self.true_model))
super().__init__(exploration_rules=exploration_rules,
true_model=true_model,
**kwargs)
self._shared_true_parameters = False
self.tree_completed_initially = True # fixed lattice set considered
# self.base_terms = ['x', 'z']
# self.transverse_field = None
self.latex_string_map_subroutine = qmla.shared_functionality.latex_model_names.lattice_set_grouped_pauli
self.initial_models = None # so that QMLA will call generate_models first
# self.true_model = self.model_from_lattice(self.available_lattices[0])
self.max_time_to_consider = 45
self.check_champion_reducibility = False
self.fraction_own_experiments_for_bf = 0.25
self.fraction_opponents_experiments_for_bf = self.fraction_own_experiments_for_bf
self.fraction_particles_for_bf = 0.25
self.lattice_names = [
'_2_site_chain', '_3_site_chain', '_4_site_chain', '_5_site_chain',
'_6_site_chain', '_3_site_lattice_fully_connected',
'_4_site_lattice_fully_connected',
'_5_site_lattice_fully_connected', '_4_site_square', '_6_site_grid'
]
# self.rerun_lattices = [
# '_5_site_lattice_fully_connected',
# '_6_site_grid'
# ]
# self.lattice_names = list(sorted(self.available_lattices_by_name.keys()))
self.available_lattices_by_name = {
k: topology_predefined.__getattribute__(k)
for k in self.lattice_names
}
self.available_lattices = [
topology_predefined.__getattribute__(k) for k in self.lattice_names
]
# self.true_lattice = topology_predefined._4_site_square
# randomly select a true model from the available lattices
if self._shared_true_parameters:
lattice_idx = -1
else:
lattice_idx = self.qmla_id % len(self.available_lattices)
# Rerunning subset with more resources
# lattice_idx = self.qmla_id % len(self.rerun_lattices)
# self.true_lattice_name = self.lattice_names[ lattice_idx ]
self.true_lattice_name = '_4_site_lattice_fully_connected'
self.true_lattice = self.available_lattices_by_name[
self.true_lattice_name]
self.true_model = self.model_from_lattice(self.true_lattice)
self.max_num_models_by_shape = {
1: 2,
2: 2,
3: 2,
4: 2,
5: 2,
6: 2,
'other': 0
}
self.num_processes_to_parallelise_over = len(self.available_lattices)
