def test_09_load_params(self):
"""
control.pulseoptim: Hadamard gate (loading config from file)
compare with result produced by pulseoptim method
"""
H_d = sigmaz()
H_c = sigmax()
U_0 = identity(2)
U_targ = hadamard_transform(1)
cfg = optimconfig.OptimConfig()
cfg.param_fname = "Hadamard_params.ini"
cfg.param_fpath = os.path.join(os.path.dirname(__file__),
cfg.param_fname)
cfg.pulse_type = "ZERO"
loadparams.load_parameters(cfg.param_fpath, config=cfg)
dyn = dynamics.DynamicsUnitary(cfg)
dyn.target = U_targ
dyn.initial = U_0
dyn.drift_dyn_gen = H_d
dyn.ctrl_dyn_gen = [H_c]
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
dyn.init_timeslots()
n_ts = dyn.num_tslots
n_ctrls = dyn.num_ctrls
pgen = pulsegen.create_pulse_gen(pulse_type=cfg.pulse_type, dyn=dyn)
loadparams.load_parameters(cfg.param_fpath, pulsegen=pgen)
tc = termcond.TerminationConditions()
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if cfg.optim_method == 'BFGS':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif cfg.optim_method == 'FMIN_L_BFGS_B':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
elif cfg.optim_method is None:
raise errors.UsageError("Optimisation algorithm must be specified "
"via 'optim_method' parameter")
else:
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
loadparams.load_parameters(cfg.param_fpath, optim=optim)
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
optim.config = cfg
optim.dynamics = dyn
optim.pulse_generator = pgen
optim.termination_conditions = tc
init_amps = np.zeros([n_ts, n_ctrls])
for j in range(n_ctrls):
init_amps[:, j] = pgen.gen_pulse()
dyn.initialize_controls(init_amps)
result = optim.run_optimization()
result2 = cpo.optimize_pulse_unitary(H_d,
list([H_c]),
U_0,
U_targ,
6,
6,
fid_err_targ=1e-10,
init_pulse_type='LIN',
amp_lbound=-1.0,
amp_ubound=1.0,
gen_stats=True)
assert_almost_equal(result.final_amps,
result2.final_amps,
decimal=5,
err_msg="Pulses do not match")
def test_load_params(self):
"""
Optimise pulse for Hadamard gate by loading config from file
compare with result produced by pulseoptim method
"""
H_d = sigmaz()
H_c = sigmax()
U_0 = identity(2)
U_targ = hadamard_transform(1)
cfg = optimconfig.OptimConfig()
cfg.param_fname = "Hadamard_params.ini"
cfg.param_fpath = os.path.join(os.path.dirname(__file__),
cfg.param_fname)
cfg.pulse_type = "ZERO"
loadparams.load_parameters(cfg.param_fpath, config=cfg)
dyn = dynamics.DynamicsUnitary(cfg)
dyn.target = U_targ
dyn.initial = U_0
dyn.drift_dyn_gen = H_d
dyn.ctrl_dyn_gen = [H_c]
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
dyn.init_timeslots()
n_ts = dyn.num_tslots
n_ctrls = dyn.num_ctrls
pgen = pulsegen.create_pulse_gen(pulse_type=cfg.pulse_type, dyn=dyn)
loadparams.load_parameters(cfg.param_fpath, pulsegen=pgen)
tc = termcond.TerminationConditions()
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if cfg.optim_method == 'BFGS':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif cfg.optim_method == 'FMIN_L_BFGS_B':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
elif cfg.optim_method is None:
raise errors.UsageError("Optimisation algorithm must be specified "
"via 'optim_method' parameter")
else:
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
loadparams.load_parameters(cfg.param_fpath, optim=optim)
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
optim.config = cfg
optim.dynamics = dyn
optim.pulse_generator = pgen
optim.termination_conditions = tc
init_amps = np.zeros([n_ts, n_ctrls])
for j in range(n_ctrls):
init_amps[:, j] = pgen.gen_pulse()
dyn.initialize_controls(init_amps)
result = optim.run_optimization()
result2 = cpo.optimize_pulse_unitary(H_d, list([H_c]), U_0, U_targ,
6, 6, fid_err_targ=1e-10,
init_pulse_type='LIN',
amp_lbound=-1.0, amp_ubound=1.0,
gen_stats=True)
assert_almost_equal(result.final_amps, result2.final_amps, decimal=5,
err_msg="Pulses do not match")
def gen_optim_config(param_fname=None, parse_cl_args=True, verbosity=None):
"""
Create the optimiser objects and load the configuration.
Returns
-------
OptimConfig
"""
def printv(msg, verb_tresh=1):
if verbosity and verbosity >= verb_tresh:
print(msg)
cfg = optimconfig.OptimConfig()
if parse_cl_args:
parser = argparse.ArgumentParser(
description="Command line argument parser")
parser.add_argument('-p', '--param_file', type=str, default="",
help="Parameters file name")
parser.add_argument('-o', '--output_dir', type=str, default="",
help="output sub directory")
parser.add_argument('-j', '--job_id', type=int, default=0,
help="Job id (from bsub)")
parser.add_argument('-i', '--job_idx', type=int, default=0,
help="Job index (from bsub batch array)")
parser.add_argument('-I', '--idx_opt', type=str, default='',
help="Job index option (what to do with it)")
parser.add_argument('-N', '--num_qubits', type=int, default=0,
help="Number of qubits")
parser.add_argument('-u', '--init_amps', type=str, default=0,
help="File name of initial amplitudes")
parser.add_argument('-m', '--num_tslots', type=int, default=0,
help="Number of timeslots")
parser.add_argument('-T', '--evo_time', type=float, default=0,
help="Total evolution time")
parser.add_argument('--evo_time_npi', type=int, default=0,
help="Total evolution time in mulitples of pi")
parser.add_argument('--evo_time_npi_g0s', type=int, default=0,
help="Total evolution time in mulitples of pi "
"scaled by first coupling constant")
parser.add_argument('-a', '--numer_acc', type=float, default=-1.0,
help="Numerical accuracy")
parser.add_argument('-e', '--fid_err_targ', type=float, default=0.0,
help="Fidelity error target")
parser.add_argument('-n', '--num_cpus', type=int, default=0,
help="Fidelity error target")
parser.add_argument('-M', '--mp_opt', type=str, default='',
help="Multiprocessing option")
parser.add_argument('--mem_opt', type=int, default=0,
help="Memory optimising level")
parser.add_argument('--pulse_scaling', type=int, default=0.0,
help="Initial pulse scaling")
parser.add_argument('--max_wall_time', type=int, default=0.0,
help="Maximum simulation wall time")
cfg.args = vars(parser.parse_args())
else:
cfg.args = None
cfg.log_level = log_level
cfg.use_param_file = True
if parse_cl_args and len(cfg.args['param_file']) > 0:
param_fname = cfg.args['param_file']
cfg.param_fpath = os.path.join(os.getcwd(), param_fname)
if not os.path.isfile(cfg.param_fpath):
raise ValueError("Commandline argument parameter "
"file name '{}' does not exist.".format(param_fname))
else:
printv("Parameters will be read from:\n{}".format(cfg.param_fpath))
elif param_fname is None:
printv("No parameter file. Using defaults in code.")
cfg.use_param_file = False
elif os.path.abspath(param_fname):
cfg.param_fname = os.path.basename(param_fname)
cfg.param_fpath = param_fname
param_fname = cfg.param_fname
else:
cfg.param_fpath = os.path.join(os.getcwd(), param_fname)
if (not os.path.isfile(cfg.param_fpath)):
printv ("Default parameter file {} not present. "
"Using defaults in code.").format(param_fname)
cfg.use_param_file = False
cfg.param_fname = param_fname
# Script operational parameters
cfg.job_id = 0
cfg.job_idx = 0
cfg.verbosity = 0
# Folder where output files will be stored
# ~ can be used for home folder
cfg.output_dir = "~quant_self_optim/output/default"
# Used as part of output file name.
cfg.output_base_name = 'general'
# If True then the physics parameters are included in the output
cfg.report_phys_params = True
# True means print messages saved in output file as well as std_out
cfg.double_print = True
cfg.output_files = True
cfg.plot_result = False
cfg.plot_file_type = 'PNG'
cfg.save_plots = False
cfg.save_results = True
# File extension for output file
cfg.data_file_ext = "txt"
# An extended file extension, which includes job_id and job_idx
# when these are set by the job scheduler
cfg.out_file_ext = "txt"
# Optimizer config
# Multi-var optimisation methdo
cfg.optim_method = 'LBFGSB'
# Pulse generator type
# For other option see qutip.control.pulsegen.create_pulse_gen
# Only the random types make sense with multiple repetitions
cfg.p_type = 'RND'
cfg.check_grad = False
# If true then processing stats will be collected
cfg.gen_stats = True
# stats_type options: standard|local
# local can be used with the pure_Choi_local FidelityComputer
cfg.stats_type = 'standard'
# If True then stats for each pulse optimisation will be reported
cfg.report_stats = True
# If True then the qutip OptimResult object is kept for each pulse
# optimisation. By default they are not, so as to save RAM.
cfg.keep_optim_result = False
# Save pulse amplitdues to file (or not)
cfg.save_initial_amps = False
cfg.save_final_amps = False
# fid_type options pure_Choi_local|pure_Choi_global|unit_global
cfg.fid_type = 'pure_choi_local'
# These will be used as bounds for the PulseGenerator and the Optimizer
cfg.amp_lbound = -np.Inf
cfg.amp_ubound = np.Inf
# Number of repetions for each pulse optimisation
cfg.num_reps = 2
# Number of cpus to utilise
cfg.num_cpus = 1
# Multi-processing options:-
# <blank> - repetitions will be spread over available cpus
# num_tslots - scenarios with different num_tslots spread over cpus
# numer_acc_limit - automatic search numerical accuracy threshold
cfg.mp_opt = ""
# Maximum number of scenarios when mp_opt=numer_acc_limit
cfg.max_mp_scens = np.inf
# True implies that some external job scheduler is organising some
# multi-processing using the job_idx to set parameter values
# hence a fuller name will be in the output file.
cfg.ext_mp = False
# Number of threads given to each process
# Effective only with large systems, > 6 qubits, and only really useful
# on a large cluster
cfg.num_threads = 1
if cfg.use_param_file:
# load the config parameters
# note these will overide those above if present in the file
printv("Loading config parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, config=cfg)
if verbosity is None:
verbosity = cfg.verbosity
cfg.plot_file_ext = cfg.plot_file_type.lower()
# override with command line params (if any)
if parse_cl_args:
if cfg.args['num_qubits'] > 0:
cfg.num_qubits = cfg.args['num_qubits']
printv("Using num_qubits={} from command line".format(
cfg.num_qubits))
if len(cfg.args['output_dir']) > 0:
cfg.output_dir = cfg.args['output_dir']
printv("Using output_dir '{}' from command line".format(
cfg.output_dir))
if cfg.args['num_cpus'] > 0:
cfg.num_cpus = cfg.args['num_cpus']
printv("Using num_cpus={} from command line".format(cfg.num_cpus))
if len(cfg.args['mp_opt']) > 0:
cfg.mp_opt = cfg.args['mp_opt']
printv("Using mp_opt={} from command line".format(cfg.mp_opt))
if cfg.args['job_id'] > 0:
cfg.job_id = cfg.args['job_id']
if cfg.job_id:
printv("Processing job ID: {}".format(cfg.job_id))
if cfg.args['job_idx'] > 0:
cfg.job_idx = cfg.args['job_idx']
if cfg.job_idx:
printv("Processing job array index: {}".format(cfg.job_idx))
cfg.out_file_ext = "{}.{}.{}".format(cfg.job_id, cfg.job_idx,
cfg.data_file_ext)
cfg.plot_file_ext = "{}.{}.{}".format(cfg.job_id, cfg.job_idx,
cfg.plot_file_type)
else:
cfg.out_file_ext = "{}.{}".format(cfg.job_id, cfg.data_file_ext)
cfg.plot_file_ext = "{}.{}".format(cfg.job_id, cfg.plot_file_type)
logger.setLevel(cfg.log_level)
return cfg
def gen_optim_objects(cfg, verbosity=None):
"""
Generate the optimiser objects based on the configuration
Returns
-------
Optimizer
"""
if verbosity is None:
verbosity = cfg.verbosity
def printv(msg, verb_tresh=1):
if verbosity and verbosity >= verb_tresh:
print(msg)
dyn = dynamics.DynamicsUnitary(cfg)
dyn.dense_oper = True
dyn.memory_optimization = 1
# Number of timeslots for the pulse discretisation
dyn.num_tslots = 12
# For a range of evo_time based on job_idx (cfg.idx_opt='num_tslots')
# For a specific list of num_tslots values based on job_idx
dyn.num_tslots_list = []
# Or generate a range
dyn.st_num_tslots = 10
dyn.d_num_tslots = 5
dyn.num_num_tslots = 10
# Time allowed for the gate to evolve
dyn.evo_time = 10.0
# For a range of evo_time based on job_idx (cfg.idx_opt='evo_time'):
# For a specific list of evo_time values based on job_idx
dyn.evo_time_list = [7, 10, 12, 20]
# Or generate a range
dyn.st_evo_time = 0.2
dyn.d_evo_time = 0.2
dyn.num_evo_time = 50
# Any number of qubits >= 3 can choosen.
# However larger numbers take a lot of processing
dyn.num_qubits = 3
# Only CNOT implemented for target_type
dyn.target_type = 'CNOT'
# interact options: Ising|Heisenberg| or combinations of xyz
dyn.interact = 'Ising'
# topology options: chain|star|full|ring
dyn.topology = 'chain'
# ctrls_type options: combinations of xyz
dyn.ctrls_type = 'XY'
# If True then same coupling constant will be used for
# all interactions between qubit pairs
dyn.iso_coup = True
# Coupling strength for each interaction
# If a single float, then this will be used for all interactions
# (single float list will be converted to float)
# Otherwise
# If iso_coup=True then should be one list item per iteracting pair
# If iso_coup=False then should be blocks of list items per iteracting pair
# e.g. for 4 qubit Heisenberg chain:
# [g_0x, g_1x, g_2x, g_0y, g_1y, g_2y, g_0z, g_1z, g_2z]
# Note that the number of interacting pairs is n-1 for chain and star,
# n for ring, and n*(n-1)/2 for fully connected
dyn.coup_const = [1.0]
# Used to change the order of the qubits in the Hilbert space
# The two qubit gate will always be between 0 and 1,
# but the couplings will be permuted based on hspace_order
dyn.hspace_order = []
# When True the hspace_order will be automatically generated
# based on the other attributes
dyn.auto_hspace = False
# Separation of the first and second qubits in the coupling Hilbert space
# 0 implies adjacent, -1 implies random
dyn.hspace_01_sep = 0
# Index of the first qubit in the coupling Hilbert space
# -1 implies random
dyn.hspace_0_idx = 0
# dynamical decoup attribs
# These are the decoup amplitudes in the three axes
dyn.decoup_x = 0.0
dyn.decoup_y = 0.0
dyn.decoup_z = 0.0
# num_decoup_tslots = None means use the specific mask
# num_decoup_tslots = 0 use all
# num_decoup_tslots +ve is the number of tslots from start
# num_decoup_tslots -ve is the number of zeros at the end
dyn.num_decoup_tslots = 0
dyn.decoup_tslots = []
#dyn.decoup_tslot_mask = []
if hasattr(cfg, 'args') and isinstance(cfg.args, argparse.ArgumentParser):
parse_cl_args = True
else:
parse_cl_args = False
if cfg.use_param_file:
# load the dynamics parameters
# note these will overide those above if present in the file
printv("Loading dynamics parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
if parse_cl_args:
if cfg.args['num_qubits'] > 0:
dyn.num_qubits = cfg.args['num_qubits']
printv("num_qubits = {} from command line".format(dyn.num_qubits))
# If job_idx is given, then use it to calculate some other parameter
# (which supersedes all other settings)
if cfg.job_idx and len(cfg.args['idx_opt']) > 0:
cfg.ext_mp = True
opt_str = cfg.args['idx_opt'].lower()
if opt_str == 'evo_time':
printv("basing evo_time on job_idx... ")
num_evo_time = len(dyn.evo_time_list)
if num_evo_time > 0:
printv("...using evo_time_list")
# get the evo time from the list
T_idx = (cfg.job_idx - 1) % num_evo_time
dyn.evo_time = dyn.evo_time_list[T_idx]
else:
printv("...using start and increment")
# calculate the evo_time from job_idx
T_idx = (cfg.job_idx - 1) % dyn.num_evo_time
dyn.evo_time = (dyn.st_evo_time
+ dyn.d_evo_time*float(T_idx))
printv("evo_time={} for job idx {}".format(dyn.evo_time,
cfg.job_idx))
elif opt_str == 'num_tslots':
printv("basing num_tslots on job_idx... ")
num_nts = len(dyn.num_tslots_list)
if num_nts > 0:
printv("...using num_tslots_list")
nts_idx = (cfg.job_idx - 1) % num_nts
dyn.num_tslots = dyn.num_tslots_list[nts_idx]
else:
printv("...using start and increment")
# calculate the num_tslots from job_idx
nts_idx = (cfg.job_idx - 1) % dyn.num_num_tslots
dyn.num_tslots = (dyn.st_num_tslots
+ dyn.d_num_tslots*nts_idx)
printv("num_tslots={} for job idx {}".format(dyn.num_tslots,
cfg.job_idx))
else:
raise ValueError("No option for idx_opt '{}' "
"in command line".format(opt_str))
if cfg.args['evo_time'] > 0:
dyn.evo_time = cfg.args['evo_time']
printv("Using evo_time={} from command line".format(dyn.evo_time))
if cfg.args['evo_time_npi'] > 0:
dyn.evo_time = np.pi*cfg.args['evo_time_npi']
printv("Using evo_time={} from command line evo_time_npi".format(
dyn.evo_time))
if cfg.args['evo_time_npi_g0s'] > 0:
dyn.evo_time = np.pi*cfg.args['evo_time_npi_g0s']/dyn.coup_const[0]
printv("Using evo_time={} from command line "
"evo_time_npi_g0s".format(dyn.evo_time))
if cfg.args['num_tslots'] > 0:
dyn.num_tslots = cfg.args['num_tslots']
printv("Using num_tslots={} from command line".format(
dyn.num_tslots))
if cfg.args['mem_opt'] > 0:
dyn.memory_optimization = cfg.args['mem_opt']
printv("Using mem_opt={} from command line".format(
dyn.memory_optimization))
printv("evo_time={}".format(dyn.evo_time))
printv("num_tslots={}".format(dyn.num_tslots))
if dyn.num_tslots == 0:
dyn.num_tslots = dyn.num_qubits*4 + 8
printv("num_tslots calculated and set to be {}".format(dyn.num_tslots))
if len(dyn.coup_const) == 1:
dyn.coup_const = dyn.coup_const[0]
dyn.prop_computer = propcomp.PropCompFrechet(dyn)
if dyn.dense_oper:
dyn.oper_dtype = np.ndarray
else:
dyn.oper_dtype = Qobj
# Pulse optimisation termination conditions
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
# Target for the infidelity (1 - gate fidelity)
tc.fid_err_targ = 1e-3
# Sum of the gradients wrt optimisation parameters (timeslot amplitudes)
tc.min_gradient_norm = 1e-30
# Number of iterations of the algorithm
tc.max_iter = 2400
# Computation time (in seconds)
tc.max_wall_time = 120*60.0
# Relative change in fid_err between iterations
tc.accuracy_factor = 1e5
if cfg.use_param_file:
# load the termination condition parameters
# note these will overide those above if present in the file
printv("Loading termination condition parameters from {}".format(
cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if parse_cl_args:
if cfg.args['fid_err_targ'] > 0.0:
tc.fid_err_targ = cfg.args['fid_err_targ']
printv("fid_err_targ = {} from cmd line".format(tc.fid_err_targ))
if cfg.args['max_wall_time'] > 0.0:
tc.max_wall_time = cfg.args['max_wall_time']
printv("max_wall_time = {} from cmd line".format(tc.max_wall_time))
# Fidelity oomputer
ft = cfg.fid_type.lower()
if ft == 'pure_choi_local':
dyn.fid_computer = FidCompPureChoiLocal(dyn)
elif ft == 'pure_choi_global':
dyn.fid_computer = FidCompPureChoiGlobal(dyn)
elif ft == 'unit_global':
dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
dyn.fid_computer.local = False
else:
raise errors.UsageError("Unknown fid type {}".format(cfg.fid_type))
fid_comp = dyn.fid_computer
# If True and optim dumping set to at least SUMMARY, then the
# global fid will be added to the summary (when using pure_choi_local)
fid_comp.dump_global_choi_fid = False
# This is the maximum precsion that sub-system fidelities are 'measured'
# see choi_closed_fidcomp.my_round for details
# Zero implies full machine precision
fid_comp.numer_acc = 0.0
# These next parameters are used in the automatic search for the numerical
# accuracy threshold
# If numer_acc_exact==false, then numer_acc, st_numer_acc, end_numer_acc
# will be treated as proportion of fid_err_targ (during config)
fid_comp.numer_acc_exact = False
fid_comp.st_numer_acc = 0.01
fid_comp.end_numer_acc = 0.2
# These proportions are used to determine the boundaries for the search
# They are proportions of the number number of successful repeats
# for the scenario.
fid_comp.success_prop_uthresh = 0.95
fid_comp.success_prop_lthresh = 0.01
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
printv("Loading fidcomp parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, obj=dyn.fid_computer,
section='fidcomp')
if parse_cl_args:
if cfg.args['numer_acc'] >= 0.0:
fid_comp.numer_acc = cfg.args['numer_acc']
printv("numer_acc = {} from cmd line".format(fid_comp.numer_acc))
if not fid_comp.numer_acc_exact:
fid_comp.numer_acc = round_sigfigs(
fid_comp.numer_acc*tc.fid_err_targ, 6)
fid_comp.st_numer_acc = round_sigfigs(
fid_comp.st_numer_acc*tc.fid_err_targ, 6)
fid_comp.end_numer_acc = round_sigfigs(
fid_comp.end_numer_acc*tc.fid_err_targ, 6)
# Pulse generator
p_gen = pulsegen.create_pulse_gen(pulse_type=cfg.p_type, dyn=dyn)
p_gen.all_ctrls_in_one = False
p_gen.lbound = cfg.amp_lbound
p_gen.ubound = cfg.amp_ubound
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
printv("Loading pulsegen parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, pulsegen=p_gen)
if not isinstance(p_gen, pulsegen.PulseGenCrab):
if not (np.isinf(cfg.amp_lbound) or np.isinf(cfg.amp_ubound)):
p_gen.scaling = cfg.amp_ubound - cfg.amp_lbound
p_gen.offset = (cfg.amp_ubound + cfg.amp_lbound) / 2.0
if parse_cl_args:
if cfg.args['pulse_scaling'] > 0.0:
p_gen.scaling = cfg.args['pulse_scaling']
printv("p_gen.scaling = {} from cmd line".format(p_gen.scaling))
# Create the Optimiser instance
if cfg.optim_method is None:
raise errors.UsageError("Optimisation method must be specified "
"via 'optim_method' parameter")
om = cfg.optim_method.lower()
if om == 'bfgs':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif om == 'lbfgsb':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
else:
# Assume that the optim_method is valid
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
if cfg.verbosity > 1:
printv("Created optimiser of type {}".format(type(optim)), 2)
optim.config = cfg
optim.dynamics = dyn
optim.termination_conditions = tc
optim.pulse_generator = p_gen
optim.method_approach = 'DEF'
optim.dumping = 'SUMMARY'
if cfg.use_param_file:
# load the optimiser parameters
# note these will overide those above if present in the file
printv("Loading optimiser parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, optim=optim)
optim.termination_conditions = tc
if cfg.gen_stats:
# Create a stats object
# Note that stats object is optional
# if the Dynamics and Optimizer stats attribute is not set
# then no stats will be collected, which could improve performance
stats_type = 'standard'
try:
stats_type = cfg.stats_type.lower()
except:
pass
if stats_type == 'local':
sts = qsostats.StatsFidCompLocal()
else:
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
return optim
def gen_config(param_fname='unctrlsympl_params.ini'):
"""
Generate the configuration for the oscillator
In general values from the parameter file overwrite those hardcoded
and these in turn are overwritten by cmdline args
Returns
-------
Optimizer
"""
parser = argparse.ArgumentParser(
description="Command line argument parser")
parser.add_argument('-p',
'--param_file',
type=str,
default="",
help="Parameters file name")
parser.add_argument('-o',
'--output_dir',
type=str,
default="",
help="output sub directory")
parser.add_argument('-j',
'--job_id',
type=int,
default=0,
help="Job id (from bsub)")
parser.add_argument('-i',
'--job_idx',
type=int,
default=0,
help="Job index (from bsub batch array)")
parser.add_argument('-I',
'--idx_opt',
type=str,
default='',
help="Job index option (what to do with it)")
#parser.add_argument('-S', '--job_size', type=int, default=0,
# help="Number of jobs in array")
parser.add_argument('-N',
'--num_qubits',
type=int,
default=0,
help="Number of qubits")
parser.add_argument('-u',
'--init_amps',
type=str,
default=0,
help="File name of initial amplitudes")
parser.add_argument('-m',
'--num_tslots',
type=int,
default=0,
help="Number of timeslots")
parser.add_argument('-T',
'--evo_time',
type=float,
default=0,
help="Total evolution time")
parser.add_argument('--evo_time_npi',
type=int,
default=0,
help="Total evolution time in mulitples of pi")
parser.add_argument('--evo_time_npi_g0s',
type=int,
default=0,
help="Total evolution time in mulitples of pi "
"scaled by first coupling constant")
parser.add_argument('-a',
'--numer_acc',
type=float,
default=-1.0,
help="Numerical accuracy")
parser.add_argument('-e',
'--fid_err_targ',
type=float,
default=0.0,
help="Fidelity error target")
parser.add_argument('-n',
'--num_cpus',
type=int,
default=0,
help="Fidelity error target")
parser.add_argument('-M',
'--mp_opt',
type=str,
default='',
help="Multiprocessing option")
parser.add_argument('--mem_opt',
type=int,
default=0,
help="Memory optimising level")
parser.add_argument('--pulse_scaling',
type=int,
default=0.0,
help="Initial pulse scaling")
parser.add_argument('--max_wall_time',
type=int,
default=0.0,
help="Maximum simulation wall time")
args = vars(parser.parse_args())
cfg = optimconfig.OptimConfig()
cfg.log_level = log_level
cfg.use_param_file = True
if len(args['param_file']) > 0:
param_fname = args['param_file']
cfg.param_fpath = os.path.join(os.getcwd(), param_fname)
if not os.path.isfile(cfg.param_fpath):
raise ValueError(
"Commandline argument parameter "
"file name '{}' does not exist.".format(param_fname))
else:
print("Parameters will be read from:\n{}".format(cfg.param_fpath))
elif os.path.abspath(param_fname):
cfg.param_fname = os.path.basename(param_fname)
cfg.param_fpath = param_fname
param_fname = cfg.param_fname
else:
cfg.param_fpath = os.path.join(os.getcwd(), param_fname)
if (not os.path.isfile(cfg.param_fpath)):
print("Default parameter file {} not present. "
"Using defaults in code.").format(param_fname)
cfg.use_param_file = False
cfg.param_fname = param_fname
# Script operational parameters
cfg.job_id = 0
cfg.job_idx = 0
cfg.verbosity = 0
cfg.output_dir = None
cfg.output_base_name = 'general'
cfg.double_print = True
cfg.output_files = True
cfg.plot_result = False
cfg.plot_file_type = 'PNG'
cfg.save_plots = False
cfg.save_results = True
cfg.data_file_ext = "txt"
cfg.out_file_ext = "txt"
# Optimizer config
cfg.optim_method = 'LBFGSB'
cfg.p_type = 'RND'
cfg.check_grad = False
cfg.gen_stats = True
cfg.stats_type = 'standard'
cfg.report_stats = True
cfg.save_initial_amps = False
cfg.save_final_amps = False
cfg.fid_type = 'pure_choi_local'
cfg.amp_lbound = -np.Inf
cfg.amp_ubound = np.Inf
cfg.num_reps = 2
cfg.num_cpus = 1
cfg.mp_opt = ""
cfg.max_mp_scens = np.inf
cfg.ext_mp = False
cfg.num_threads = 1
if cfg.use_param_file:
# load the config parameters
# note these will overide those above if present in the file
print("Loading config parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, config=cfg)
# override with command line params (if any)
if args['num_qubits'] > 0:
cfg.num_qubits = args['num_qubits']
print("Using num_qubits={} from command line".format(cfg.num_qubits))
if len(args['output_dir']) > 0:
cfg.output_dir = args['output_dir']
print("Using output_dir '{}' from command line".format(cfg.output_dir))
if args['num_cpus'] > 0:
cfg.num_cpus = args['num_cpus']
print("Using num_cpus={} from command line".format(cfg.num_cpus))
if len(args['mp_opt']) > 0:
cfg.mp_opt = args['mp_opt']
print("Using mp_opt={} from command line".format(cfg.mp_opt))
cfg.plot_file_ext = cfg.plot_file_type.lower()
if args['job_id'] > 0:
cfg.job_id = args['job_id']
if cfg.job_id:
print("Processing job ID: {}".format(cfg.job_id))
if args['job_idx'] > 0:
cfg.job_idx = args['job_idx']
if cfg.job_idx:
print("Processing job array index: {}".format(cfg.job_idx))
cfg.out_file_ext = "{}.{}.{}".format(cfg.job_id, cfg.job_idx,
cfg.data_file_ext)
cfg.plot_file_ext = "{}.{}.{}".format(cfg.job_id, cfg.job_idx,
cfg.plot_file_type)
else:
cfg.out_file_ext = "{}.{}".format(cfg.job_id, cfg.data_file_ext)
cfg.plot_file_ext = "{}.{}".format(cfg.job_id, cfg.plot_file_type)
logger.setLevel(cfg.log_level)
if not cfg.output_dir:
cfg.output_dir = cfg.target_id_text
dyn = dynamics.DynamicsUnitary(cfg)
dyn.dense_oper = True
dyn.num_tslots_list = []
dyn.st_num_tslots = 10
dyn.d_num_tslots = 5
dyn.num_num_tslots = 10
dyn.evo_time = 10.0
# For a specific list of evo_time values based on job_idx
dyn.evo_time_list = [7, 10, 12, 20]
# For a range of evo_time based on job_idx (idx_opt='evo_time')
# Start evo_time
dyn.st_evo_time = 0.2
# Evo time step size
dyn.d_evo_time = 0.2
# Number of evo_time
dyn.num_evo_time = 50
dyn.num_qubits = 6
dyn.target_type = 'CNOT'
dyn.interact = 'Ising'
dyn.topology = 'chain'
dyn.ctrls_type = 'XY'
dyn.coup_const = [1.0]
# Used to change the order of the qubits in the Hilbert space
# so that the two qubit gate will always be between 0 and 1,
# but the couplings will be permuted based on hspace_order
dyn.hspace_order = []
# When True the hspace_order will be automatically generated
# based on the other attributes
dyn.auto_hspace = False
# Separation of the first and second qubits in the coupling Hilbert space
# 0 implies adjacent, -1 implies random
dyn.hspace_01_sep = 0
# Index of the first qubit in the coupling Hilbert space
# -1 implies random
dyn.hspace_0_idx = 0
# If True then same coupling constant will be used for
# all interactions between qubit pairs
dyn.iso_coup = True
# dynamical decoup attribs
# These are the decoup amplitudes in the three axes
dyn.decoup_x = 0.0
dyn.decoup_y = 0.0
dyn.decoup_z = 0.0
# num_decoup_tslots = None means use the specific mask
# num_decoup_tslots = 0 use all
# num_decoup_tslots +ve is the number of tslots from start
# num_decoup_tslots -ve is the number of zeros at the end
dyn.num_decoup_tslots = 0
dyn.decoup_tslots = []
#dyn.decoup_tslot_mask = []
if cfg.use_param_file:
# load the dynamics parameters
# note these will overide those above if present in the file
print("Loading dynamics parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
if args['num_qubits'] > 0:
dyn.num_qubits = args['num_qubits']
print("num_qubits = {} from command line".format(dyn.num_qubits))
# If job_idx is given, then use it to calculate some other parameter
# (which supersedes all other settings)
if cfg.job_idx and len(args['idx_opt']) > 0:
cfg.ext_mp = True
opt_str = args['idx_opt'].lower()
if opt_str == 'evo_time':
print("basing evo_time on job_idx... ")
num_evo_time = len(dyn.evo_time_list)
if num_evo_time > 0:
print("...using evo_time_list")
# get the evo time from the list
T_idx = (cfg.job_idx - 1) % num_evo_time
dyn.evo_time = dyn.evo_time_list[T_idx]
else:
print("...using start and increment")
# calculate the evo_time from job_idx
T_idx = (cfg.job_idx - 1) % dyn.num_evo_time
dyn.evo_time = dyn.st_evo_time + dyn.d_evo_time * float(T_idx)
print("evo_time={} for job idx {}".format(dyn.evo_time,
cfg.job_idx))
elif opt_str == 'num_tslots':
print("basing num_tslots on job_idx... ")
num_nts = len(dyn.num_tslots_list)
if num_nts > 0:
print("...using num_tslots_list")
nts_idx = (cfg.job_idx - 1) % num_nts
dyn.num_tslots = dyn.num_tslots_list[nts_idx]
else:
print("...using start and increment")
# calculate the num_tslots from job_idx
nts_idx = (cfg.job_idx - 1) % dyn.num_num_tslots
dyn.num_tslots = dyn.st_num_tslots + dyn.d_num_tslots * nts_idx
print("num_tslots={} for job idx {}".format(
dyn.num_tslots, cfg.job_idx))
else:
raise ValueError("No option for idx_opt '{}' "
"in command line".format(opt_str))
if args['evo_time'] > 0:
dyn.evo_time = args['evo_time']
print("Using evo_time={} from command line".format(dyn.evo_time))
if args['evo_time_npi'] > 0:
dyn.evo_time = np.pi * args['evo_time_npi']
print("Using evo_time={} from command line evo_time_npi".format(
dyn.evo_time))
if args['evo_time_npi_g0s'] > 0:
dyn.evo_time = np.pi * args['evo_time_npi_g0s'] / dyn.coup_const[0]
print("Using evo_time={} from command line evo_time_npi_g0s".format(
dyn.evo_time))
if args['num_tslots'] > 0:
dyn.num_tslots = args['num_tslots']
print("Using num_tslots={} from command line".format(dyn.num_tslots))
if args['mem_opt'] > 0:
dyn.memory_optimization = args['mem_opt']
print("Using mem_opt={} from command line".format(
dyn.memory_optimization))
print("evo_time={}".format(dyn.evo_time))
print("num_tslots={}".format(dyn.num_tslots))
if dyn.num_tslots == 0:
dyn.num_tslots = dyn.num_qubits * 4 + 8
print("num_tslots calculated and set to be {}".format(dyn.num_tslots))
if len(dyn.coup_const) == 1:
dyn.coup_const = dyn.coup_const[0]
dyn.prop_computer = propcomp.PropCompFrechet(dyn)
if dyn.dense_oper:
dyn.oper_dtype = np.ndarray
else:
dyn.oper_dtype = Qobj
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
tc.fid_err_targ = 1e-3
tc.min_gradient_norm = 1e-30
tc.max_iter = 2400
tc.max_wall_time = 120 * 60.0
tc.accuracy_factor = 1e5
if cfg.use_param_file:
# load the termination condition parameters
# note these will overide those above if present in the file
print("Loading termination condition parameters from {}".format(
cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if args['fid_err_targ'] > 0.0:
tc.fid_err_targ = args['fid_err_targ']
print("fid_err_targ = {} from command line".format(tc.fid_err_targ))
if args['max_wall_time'] > 0.0:
tc.max_wall_time = args['max_wall_time']
print("max_wall_time = {} from command line".format(tc.max_wall_time))
# Fidelity oomputer
ft = cfg.fid_type.lower()
if ft == 'pure_choi_local':
dyn.fid_computer = FidCompPureChoiLocal(dyn)
elif ft == 'pure_choi_global':
dyn.fid_computer = FidCompPureChoiGlobal(dyn)
elif ft == 'unit_global':
dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
dyn.fid_computer.local = False
else:
raise errors.UsageError("Unknown fid type {}".format(cfg.fid_type))
fid_comp = dyn.fid_computer
fid_comp.numer_acc = 0.0
fid_comp.numer_acc_exact = False
fid_comp.st_numer_acc = 0.01
fid_comp.end_numer_acc = 0.2
fid_comp.success_prop_uthresh = 0.95
fid_comp.success_prop_lthresh = 0.01
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
print("Loading fidcomp parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath,
obj=dyn.fid_computer,
section='fidcomp')
if args['numer_acc'] >= 0.0:
fid_comp.numer_acc = args['numer_acc']
print("numer_acc = {} from command line".format(fid_comp.numer_acc))
if not fid_comp.numer_acc_exact:
fid_comp.numer_acc = round_sigfigs(
fid_comp.numer_acc * tc.fid_err_targ, 6)
fid_comp.st_numer_acc = round_sigfigs(
fid_comp.st_numer_acc * tc.fid_err_targ, 6)
fid_comp.end_numer_acc = round_sigfigs(
fid_comp.end_numer_acc * tc.fid_err_targ, 6)
# Pulse generator
p_gen = pulsegen.create_pulse_gen(pulse_type=cfg.p_type, dyn=dyn)
p_gen.all_ctrls_in_one = False
p_gen.lbound = cfg.amp_lbound
p_gen.ubound = cfg.amp_ubound
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
print("Loading pulsegen parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, pulsegen=p_gen)
if not isinstance(p_gen, pulsegen.PulseGenCrab):
if not (np.isinf(cfg.amp_lbound) or np.isinf(cfg.amp_ubound)):
p_gen.scaling = cfg.amp_ubound - cfg.amp_lbound
p_gen.offset = (cfg.amp_ubound + cfg.amp_lbound) / 2.0
if args['pulse_scaling'] > 0.0:
p_gen.scaling = args['pulse_scaling']
print("p_gen.scaling = {} from command line".format(p_gen.scaling))
# Create the Optimiser instance
if cfg.optim_method is None:
raise errors.UsageError("Optimisation method must be specified "
"via 'optim_method' parameter")
om = cfg.optim_method.lower()
if om == 'bfgs':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif om == 'lbfgsb':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
else:
# Assume that the optim_method is valid
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
if cfg.verbosity > 1:
print("Created optimiser of type {}".format(type(optim)))
optim.config = cfg
optim.dynamics = dyn
optim.termination_conditions = tc
optim.pulse_generator = p_gen
optim.method_approach = 'DEF'
optim.dumping = 'SUMMARY'
if cfg.use_param_file:
# load the optimiser parameters
# note these will overide those above if present in the file
print("Loading optimiser parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, optim=optim)
optim.termination_conditions = tc
if cfg.gen_stats:
# Create a stats object
# Note that stats object is optional
# if the Dynamics and Optimizer stats attribute is not set
# then no stats will be collected, which could improve performance
stats_type = 'standard'
try:
stats_type = cfg.stats_type.lower()
except:
pass
if stats_type == 'local':
sts = qsostats.StatsFidCompLocal()
else:
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
# ****************************************************************
# Define the physics of the problem
print("Configuring drift...")
#The 4 below are all Qobj
# Sx = sigmax()
# Sy = sigmay()
# Sz = sigmaz()
# Si = identity(2)
nq = dyn.num_qubits
# ***** Drift *****
print("... for {} qubits".format(nq))
H_d = qso.get_drift(dyn)
print("Drift dims {}".format(H_d.dims))
# Normalise based on ising chain
H_d_ising_chain = qso.get_drift(dyn,
topology='chain',
interact='ising',
coup_const=1.0)
norm_fact = H_d_ising_chain.norm() / H_d.norm()
print("Normalising drift with factor {}".format(norm_fact))
H_d = H_d * norm_fact
# **** Controls ****
H_c, Sx_cidx, Sy_cidx, Sz_cidx = qso.get_ctrls(dyn)
n_ctrls = len(H_c)
#t0 evo
U_0 = qso.get_initial_op(dyn)
#*** Target ****
U_targ, U_local_targs = qso.get_target(dyn)
sub_dims = []
for k in range(len(U_local_targs)):
sub_dims.append(U_local_targs[k].dims[0][0])
#Enforcing all dimensions are correct for the qBranch code
#Not all of these are needed, but this is safer
for k in range(len(H_c)):
H_c[k].dims = [sub_dims, sub_dims]
H_d.dims = [sub_dims, sub_dims]
U_0.dims = [sub_dims, sub_dims]
U_targ.dims = [sub_dims, sub_dims]
dyn.drift_dyn_gen = H_d
dyn.ctrl_dyn_gen = H_c
dyn.initial = U_0
dyn.target = U_targ
# These are the indexes to the controls on the three axes
dyn.Sx_cidx = Sx_cidx
dyn.Sy_cidx = Sy_cidx
dyn.Sz_cidx = Sz_cidx
fid_comp.U_local_targs = U_local_targs
fid_comp.sub_dims = sub_dims
fid_comp.num_sub_sys = len(U_local_targs)
print("Num acc: {}".format(fid_comp.numer_acc))
return optim
# Create the OptimConfig object
cfg = optimconfig.OptimConfig()
cfg.param_fname = "coup_osc_param.ini"
cfg.param_fpath = os.path.join(os.getcwd(), cfg.param_fname)
cfg.log_level = log_level
# Initial pulse type
# pulse type alternatives:
# RNDWAVES|RNDFOURIER|RNDWALK1|RNDWALK2|RND|ZERO|LIN|SINE|SQUARE|SAW|TRIANGLE|
cfg.pulse_type = 'RNDWAVES'
cfg.amp_lbound = -5.0
cfg.amp_ubound = 5.0
# load the config parameters
# note these will overide those above if present in the file
print("Loading config parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, config=cfg)
# Update the log level, as this may have been changed in the config
logger.setLevel(cfg.log_level)
# Create the dynamics object
dyn = dynamics.DynamicsSymplectic(cfg)
#dyn.prop_computer = propcomp.PropCompApproxGrad(dyn)
#dyn.fid_computer = fidcomp.FidCompTraceDiffApprox(dyn)
#dyn.fid_computer.epsilon = 0.1
#dyn.oper_dtype = Qobj
dyn.num_tslots = 200
dyn.evo_time = 10.0
# Physical parameters
dyn.coupling1 = 0.3
# Create the OptimConfig object
cfg = optimconfig.OptimConfig()
cfg.param_fname = "coup_osc_param.ini"
cfg.param_fpath = os.path.join(os.getcwd(), cfg.param_fname)
cfg.log_level = log_level
# Initial pulse type
# pulse type alternatives:
# RNDWAVES|RNDFOURIER|RNDWALK1|RNDWALK2|RND|ZERO|LIN|SINE|SQUARE|SAW|TRIANGLE|
cfg.pulse_type = 'RNDWAVES'
cfg.amp_lbound = -5.0
cfg.amp_ubound = 5.0
# load the config parameters
# note these will overide those above if present in the file
print("Loading config parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, config=cfg)
# Update the log level, as this may have been changed in the config
logger.setLevel(cfg.log_level)
# Create the dynamics object
dyn = dynamics.DynamicsSymplectic(cfg)
#dyn.prop_computer = propcomp.PropCompApproxGrad(dyn)
#dyn.fid_computer = fidcomp.FidCompTraceDiffApprox(dyn)
#dyn.fid_computer.epsilon = 0.1
#dyn.oper_dtype = Qobj
dyn.num_tslots = 200
dyn.evo_time = 10.0
# Physical parameters
dyn.coupling1 = 0.3