Metadata flags

README.md

@@ -1,4 +1,4 @@
-![pylint](https://img.shields.io/badge/PyLint-9.29-yellow?logo=python&logoColor=white)
+![pylint](https://img.shields.io/badge/PyLint-9.21-yellow?logo=python&logoColor=white)
 
 # Quantum Computing Application Specifications
 

scripts/AP-RE.py

@@ -4,6 +4,7 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed, ProcessPoolExecutor
 from qca.utils.chemistry_utils import load_pathway, generate_electronic_hamiltonians, gsee_molecular_hamiltonian
 
+
 @dataclass
 class pathway_info:
     pathway: list[int]
@@ -18,6 +19,13 @@ def grab_arguments() -> Namespace:
         help='Factor to reduce the active space',
         default=10
     )
+    parser.add_argument(
+        '-d', 
+        '--directory', 
+        type=str, 
+        help='Directoty with pathway datafiles.',
+        default='./data/'
+    )
     args = parser.parse_args()
     return args
 
@@ -68,22 +76,23 @@ def grab_molecular_hamiltonians_pool(
     pid = os.getpid()
     args = grab_arguments()
     active_space_reduc = args.active_space_reduction
+    pathway_directory= args.directory
     pathways = [
         pathway_info(
             pathway=[27, 1, 14, 15, 16, 24, 25, 26],
-            fname='water_oxidation_Co2O9H12.xyz'
+            fname=f'{pathway_directory}water_oxidation_Co2O9H12.xyz'
         ),
         pathway_info(
             pathway=[3, 1, 14, 15, 16, 20, 21, 22, 23],
-            fname='water_oxidation_Co2O9H12.xyz'
+            fname=f'{pathway_directory}water_oxidation_Co2O9H12.xyz'
         ),
         pathway_info(
             pathway=[2, 1, 14, 15, 16, 17, 18, 19],
-            fname='water_oxidation_Co2O9H12.xyz'
+            fname='{pathway_directory}water_oxidation_Co2O9H12.xyz'
         ),
         pathway_info(
             pathway=[5, 10, 28, 29, 30, 31, 32, 33],
-            fname='water_oxidation_Co2O9H12.xyz'
+            fname='{pathway_directory}water_oxidation_Co2O9H12.xyz'
         )
     ]
     coords_pathways = [

scripts/DickeModel.py

@@ -8,7 +8,7 @@
 from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
 from networkx import path_graph, set_node_attributes, get_node_attributes, draw, draw_networkx_edge_labels
 from qca.utils.algo_utils import gsee_resource_estimation
-from qca.utils.utils import circuit_estimate, EstimateMetaData
+from qca.utils.utils import circuit_estimate, GSEEMetaData
 from qca.utils.hamiltonian_utils import (generate_two_orbital_nx, nx_to_two_orbital_hamiltonian,
                                          dicke_model_qubit_hamiltonian)
 
@@ -30,6 +30,7 @@ def main(args):
     value = args.value
     repetitions = args.repetitions
     circuit_write = args.circuit_write
+    is_extrapolated = args.extrapolate
 
     ham_dicke = dicke_model_qubit_hamiltonian(n_s = n_s, n_b = n_b, omega_c = omega_c, omega_o = omega_o, lam = lam)
 
@@ -55,28 +56,36 @@ def main(args):
     print('starting')
     value_per_circuit = value/repetitions
     value_per_circuit=6
-    dicke_metadata = EstimateMetaData(
+    #TODO: See if I need to refactor the size string to include the variable names
+    dicke_metadata = GSEEMetaData(
         id=time.time_ns(),
         name=name,
         category='scientific',
         size=f'{n_b} + 1 + {n_s}',
         task='Ground State Energy Estimation',
-        implementations=f'GSEE, evolution_time={t_dicke}, bits_precision={bits_precision_dicke}, trotter_order={trotter_order_dicke}, n_s={n_s}, n_b={n_b}',
         value_per_circuit=value_per_circuit,
-        repetitions_per_application=repetitions
+        repetitions_per_application=repetitions,
+
+
+        evolution_time=t_dicke,
+        trotter_order = trotter_order_dicke,
+        is_extrapolated=is_extrapolated,
+        bits_precision = bits_precision_dicke,
+        nsteps=trotter_steps_dicke,
     )
 
     print('Estimating Dicke')
     t0 = time.perf_counter()
     estimate = gsee_resource_estimation(
         outdir=directory,
-        numsteps=trotter_steps_dicke,
+        nsteps=trotter_steps_dicke,
         gsee_args=args_dicke,
         init_state=init_state_dicke,
         precision_order=1, #actual precision bits accounted as scaling factors in the resource estimate
         phase_offset=dicke_phase_offset,
         bits_precision=bits_precision_dicke,
         circuit_name=name,
+        is_extrapolated = is_extrapolated,
         metadata = dicke_metadata,
         write_circuits=circuit_write
     )
@@ -105,6 +114,7 @@ def parse_arguments():
     parser.add_argument('-v', '--value', type=float, default=0, help='value of the total application')
     parser.add_argument('-r', '--repetitions', type=int, default=1, help='repetitions needed to achieve value of computatation (not runs of this script)')
     parser.add_argument('-c', '--circuit_write', default=False, action='store_true')
+    parser.add_argument('-x', '--extrapolate', default=False, action='store_true')
     return parser
 
 if __name__ == "__main__":

scripts/HTSC-one-band-RE.py

@@ -9,7 +9,7 @@
 from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
 from networkx import get_node_attributes, draw, draw_networkx_edge_labels
 from qca.utils.algo_utils import gsee_resource_estimation
-from qca.utils.utils import circuit_estimate, EstimateMetaData
+from qca.utils.utils import circuit_estimate, GSEEMetaData
 from qca.utils.hamiltonian_utils import generate_two_orbital_nx, nx_to_two_orbital_hamiltonian
 
 def main(args):
@@ -25,25 +25,23 @@ def main(args):
     value = args.value
     repetitions = args.repetitions
     circuit_write = args.circuit_write
+    is_extrapolated= args.extrapolate
 
     ham = of.fermi_hubbard(lattice_size, lattice_size, tunneling=tunneling, coulomb=coulomb, periodic=False) #returns an aperiodic fermionic hamiltonian
 
-    trotter_order = 2
-    trotter_steps = 1 #Using one trotter step for a strict lower bound with this method
-
     #this scales the circuit depth proportional to 2 ^ bits_precision
     bits_precision = estimate_bits_precision(error_precision)
 
     E_min = -len(ham.terms) * max(abs(tunneling), abs(coulomb))
     E_max = 0
     omega = E_max-E_min
-    t = 2*np.pi/omega
-    phase_offset = E_max*t
+    evolution_time = 2*np.pi/omega
+    phase_offset = E_max*evolution_time
 
     gsee_args = {
         'trotterize' : True,
         'mol_ham'    : ham,
-        'ev_time'    : t,
+        'ev_time'    : evolution_time,
         'trot_ord'   : trotter_order,
         'trot_num'   : 1 #handling adjustment in resource estimate to save time - scales circuit depth linearly.
     }
@@ -52,29 +50,35 @@ def main(args):
     init_state = [0] * lattice_size * lattice_size * 2 #TODO: use Fock state from Hartree-Fock as initial state
 
     print('starting')
-    metadata = EstimateMetaData(
+    metadata = GSEEMetaData(
         id=time.time_ns(),
         name=name,
         category='scientific',
         size=f'{lattice_size}x{lattice_size}',
         task='Ground State Energy Estimation',
         value_per_circuit=value,
         repetitions_per_application=repetitions,
-        implementations=f'GSEE, evolution_time={t}, bits_precision={bits_precision}, trotter_order={trotter_order}',
+
+        evolution_time=evolution_time,
+        trotter_order=trotter_order,
+        is_extrapolated=is_extrapolated,
+        bits_precision=bits_precision,
+        nsteps=trotter_steps,
     )
 
     print('Estimating one_band')
     t0 = time.perf_counter()
     estimate = gsee_resource_estimation(
             outdir=directory,
-            numsteps=trotter_steps,
+            nsteps=trotter_steps,
             gsee_args=gsee_args,
             init_state=init_state,
             precision_order=1,
             phase_offset=phase_offset,
             bits_precision=bits_precision,
             circuit_name=name,
             metadata=metadata,
+            is_extrapolated=is_extrapolated,
             write_circuits=args.circuit_write)
     t1 = time.perf_counter()
     print(f'Time to estimate one_band: {t1-t0}')
@@ -96,6 +100,7 @@ def parse_arguments():
     parser.add_argument('-v', '--value', type=float, default=0, help='value of the total application')
     parser.add_argument('-r', '--repetitions', type=int, default=1, help='repetitions needed to achieve value of computatation (not runs of this script)')
     parser.add_argument('-c', '--circuit_write', default=False, action='store_true')
+    parser.add_argument('-x', '--extrapolate', default=False, action='store_true')
     return parser
 
 if __name__ == "__main__":

scripts/HTSC-three-band-RE.py

@@ -9,7 +9,7 @@
 from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
 from networkx import get_node_attributes, draw, draw_networkx_edge_labels
 from qca.utils.algo_utils import gsee_resource_estimation
-from qca.utils.utils import circuit_estimate, EstimateMetaData
+from qca.utils.utils import circuit_estimate, GSEEMetaData
 from qca.utils.hamiltonian_utils import generate_three_orbital_nx, nx_to_three_orbital_hamiltonian
 
 ## Three band
@@ -35,6 +35,7 @@ def main(args):
     repetitions = args.repetitions
     directory = args.directory
     name = args.name
+    is_extrapolated = args.extrapolate
 
     bits_precision = estimate_bits_precision(args.error_precision)
     g = generate_three_orbital_nx(lattice_size,lattice_size)
@@ -45,44 +46,50 @@ def main(args):
     E_min = -len(ham.terms) * max(abs(t1), abs(t2), abs(t3), abs(t4), abs(mu))
     E_max = 0
     omega = E_max-E_min
-    t = 2*np.pi/omega
-    phase_offset = E_max*t
+    evolution_time = 2*np.pi/omega
+    phase_offset = E_max*evolution_time
 
     init_state = [0] * n_qubits
 
     gsee_args = {
         'trotterize' : True,
         'mol_ham'    : ham,
-        'ev_time'    : t,
+        'ev_time'    : evolution_time,
         'trot_ord'   : trotter_order,
         'trot_num'   : 1 #Accounted for in a scaling argument later
     }
 
 
     print('starting')
 
-    metadata = EstimateMetaData(
+    metadata = GSEEMetaData(
         id=time.time_ns(),
         name=name,
         category='scientific',
         size=f'{lattice_size}x{lattice_size}',
         task='Ground State Energy Estimation',
         value_per_circuit=value,
         repetitions_per_application=repetitions,
-        implementations=f'GSEE, evolution_time={t}, bits_precision={bits_precision}, trotter_order={trotter_order}',
+
+        evolution_time=evolution_time,
+        trotter_order=trotter_order,
+        is_extrapolated=is_extrapolated,
+        bits_precision=bits_precision,
+        nsteps=trotter_steps,
     )
 
     print('Estimating Circuit Resources')
     t0 = time.perf_counter()
     estimate = gsee_resource_estimation(
             outdir=directory,
-            numsteps=trotter_steps,
+            nsteps=trotter_steps,
             gsee_args=gsee_args,
             init_state=init_state,
             precision_order=1,
             phase_offset=phase_offset,
             bits_precision=bits_precision,
             circuit_name=name,
+            is_extrapolated = is_extrapolated,
             metadata=metadata,
             write_circuits=args.circuit_write)
     t1 = time.perf_counter()
@@ -114,6 +121,7 @@ def parse_arguments():
     parser.add_argument('-v', '--value', type=float, default=0, help='value of the total application')
     parser.add_argument('-r', '--repetitions', type=int, default=1, help='repetitions needed to achieve value of computatation (not runs of this script)')
     parser.add_argument('-c', '--circuit_write', default=False, action='store_true')
+    parser.add_argument('-x', '--extrapolate', default=False, action='store_true')
     return parser
 
 if __name__ == "__main__":

scripts/HTSC-two-band-RE.py

@@ -9,7 +9,7 @@
 from pyLIQTR.PhaseEstimation.pe import PhaseEstimation
 from networkx import get_node_attributes, draw, draw_networkx_edge_labels
 from qca.utils.algo_utils import gsee_resource_estimation
-from qca.utils.utils import circuit_estimate, EstimateMetaData
+from qca.utils.utils import circuit_estimate, GSEEMetaData
 from qca.utils.hamiltonian_utils import generate_two_orbital_nx, nx_to_two_orbital_hamiltonian
 
 ## Two band
@@ -30,6 +30,7 @@ def main(args):
     repetitions = args.repetitions
     directory = args.directory
     name = args.name
+    is_extrapolated=args.extrapolate
 
     bits_precision = estimate_bits_precision(args.error_precision)
     g = generate_two_orbital_nx(lattice_size,lattice_size)
@@ -40,44 +41,50 @@ def main(args):
     E_min = -len(ham.terms) * max(abs(t1), abs(t2), abs(t3), abs(t4), abs(mu))
     E_max = 0
     omega = E_max-E_min
-    t = 2*np.pi/omega
-    phase_offset = E_max*t
+    evolution_time = 2*np.pi/omega
+    phase_offset = E_max*evolution_time
 
     init_state = [0] * n_qubits
 
     gsee_args = {
         'trotterize' : True,
         'mol_ham'    : ham,
-        'ev_time'    : t,
+        'ev_time'    : evolution_time,
         'trot_ord'   : trotter_order,
         'trot_num'   : 1 #Accounted for in a scaling argument later
     }
 
 
     print('starting')
 
-    metadata = EstimateMetaData(
+    metadata = GSEEMetaData(
         id=time.time_ns(),
         name=name,
         category='scientific',
         size=f'{lattice_size}x{lattice_size}',
         task='Ground State Energy Estimation',
         value_per_circuit=value,
         repetitions_per_application=repetitions,
-        implementations=f'GSEE, evolution_time={t}, bits_precision={bits_precision}, trotter_order={trotter_order}',
+
+        evolution_time=evolution_time,
+        trotter_order=trotter_order,
+        is_extrapolated=is_extrapolated,
+        bits_precision=bits_precision,
+        nsteps=trotter_steps,
     )
 
     print('Estimating Circuit Resources')
     t0 = time.perf_counter()
     estimate = gsee_resource_estimation(
             outdir=directory,
-            numsteps=trotter_steps,
+            nsteps=trotter_steps,
             gsee_args=gsee_args,
             init_state=init_state,
             precision_order=1,
             phase_offset=phase_offset,
             bits_precision=bits_precision,
             circuit_name=name,
+            is_extrapolated=is_extrapolated,
             metadata=metadata,
             write_circuits=args.circuit_write)
     t1 = time.perf_counter()
@@ -104,6 +111,7 @@ def parse_arguments():
     parser.add_argument('-v', '--value', type=float, default=0, help='value of the total application')
     parser.add_argument('-r', '--repetitions', type=int, default=1, help='repetitions needed to achieve value of computatation (not runs of this script)')
     parser.add_argument('-c', '--circuit_write', default=False, action='store_true')
+    parser.add_argument('-x', '--extrapolate', default=False, action='store_true')
     return parser
 
 if __name__ == "__main__":