Source code for c3.experiment

"""
Experiment class that models and simulates the whole experiment.

It combines the information about the model of the quantum device, the control stack
and the operations that can be done on the device.

Given this information an experiment run is simulated, returning either processes,
states or populations.
"""

import os
import copy
import pickle
import itertools
import hjson
import numpy as np
import tensorflow as tf
from typing import Dict, List
import time

from c3.c3objs import hjson_encode, hjson_decode
from c3.generator.generator import Generator
from c3.parametermap import ParameterMap
from c3.signal.gates import Instruction
from c3.model import Model
from c3.utils.tf_utils import (
    tf_state_to_dm,
    tf_super,
    tf_vec_to_dm,
)

from c3.libraries.propagation import unitary_provider, state_provider

from c3.utils.qt_utils import perfect_single_q_parametric_gate


[docs]class Experiment: """ It models all of the behaviour of the physical experiment, serving as a host for the individual parts making up the experiment. Parameters ---------- pmap: ParameterMap including model: Model The underlying physical device. generator: Generator The infrastructure for generating and sending control signals to the device. gateset: GateSet A gate level description of the operations implemented by control pulses. """ def __init__(self, pmap: ParameterMap = None, prop_method=None): self.pmap = pmap self.opt_gates = None self.propagators: Dict[str, tf.Tensor] = {} self.partial_propagators: Dict = {} self.created_by = None self.logdir: str = "" self.propagate_batch_size = None self.use_control_fields = True self.overwrite_propagators = True # Keep only currently computed propagators self.compute_propagators_timestamp = 0 self.stop_partial_propagator_gradient = True self.evaluate = self.evaluate_legacy self.set_prop_method(prop_method)
[docs] def set_prop_method(self, prop_method=None) -> None: """ Configure the selected propagation method by either linking the function handle or looking it up in the library. """ if prop_method is None: self.propagation = unitary_provider["pwc"] elif isinstance(prop_method, str): try: self.propagation = unitary_provider[prop_method] except KeyError: self.propagation = state_provider[prop_method] elif callable(prop_method): self.propagation = prop_method
[docs] def enable_qasm(self) -> None: """ Switch the sequencing format to QASM. Will become the default. """ self.evaluate = self.evaluate_qasm
[docs] def set_created_by(self, config): """ Store the config file location used to created this experiment. """ self.created_by = config
[docs] def load_quick_setup(self, filepath: str) -> None: """ Load a quick setup file. Parameters ---------- filepath : str Location of the configuration file """ with open(filepath, "r") as cfg_file: cfg = hjson.loads(cfg_file.read(), object_pairs_hook=hjson_decode) self.quick_setup(cfg)
[docs] def quick_setup(self, cfg) -> None: """ Load a quick setup cfg and create all necessary components. Parameters ---------- cfg : Dict Configuration options """ model = Model() model.read_config(cfg["model"]) gen = Generator() gen.read_config(cfg["generator"]) single_gate_time = cfg["single_qubit_gate_time"] v2hz = cfg["v2hz"] instructions = [] sideband = cfg.pop("sideband", None) for gate_name, props in cfg["single_qubit_gates"].items(): target_qubit = model.subsystems[props["qubits"]] instr = Instruction( name=props["name"], targets=[model.names.index(props["qubits"])], t_start=0.0, t_end=single_gate_time, channels=[target_qubit.drive_line], ) instr.quick_setup( target_qubit.drive_line, target_qubit.params["freq"].get_value() / 2 / np.pi, single_gate_time, v2hz, sideband, ) instructions.append(instr) for gate_name, props in cfg["two_qubit_gates"].items(): qubit_1 = model.subsystems[props["qubit_1"]] qubit_2 = model.subsystems[props["qubit_2"]] instr = Instruction( name=props["name"], targets=[ model.names.index(props["qubit_1"]), model.names.index(props["qubit_2"]), ], t_start=0.0, t_end=props["gate_time"], channels=[qubit_1.drive_line, qubit_2.drive_line], ) instr.quick_setup( qubit_1.drive_line, qubit_1.params["freq"].get_value() / 2 / np.pi, props["gate_time"], v2hz, sideband, ) instr.quick_setup( qubit_2.drive_line, qubit_2.params["freq"].get_value() / 2 / np.pi, props["gate_time"], v2hz, sideband, ) instructions.append(instr) self.pmap = ParameterMap(instructions, generator=gen, model=model)
[docs] def read_config(self, filepath: str) -> None: """ Load a file and parse it to create a Model object. Parameters ---------- filepath : str Location of the configuration file """ with open(filepath, "r") as cfg_file: cfg = hjson.loads(cfg_file.read(), object_pairs_hook=hjson_decode) self.from_dict(cfg)
[docs] def from_dict(self, cfg: Dict) -> None: """ Load experiment from dictionary """ model = Model() model.fromdict(cfg["model"]) generator = Generator() generator.fromdict(cfg["generator"]) pmap = ParameterMap(model=model, generator=generator) pmap.fromdict(cfg["instructions"]) if "options" in cfg: for k, v in cfg["options"].items(): self.__dict__[k] = v self.pmap = pmap
[docs] def write_config(self, filepath: str) -> None: """ Write dictionary to a HJSON file. """ with open(filepath, "w") as cfg_file: hjson.dump(self.asdict(), cfg_file, default=hjson_encode)
[docs] def asdict(self) -> Dict: """ Return a dictionary compatible with config files. """ exp_dict: Dict[str, Dict] = {} exp_dict["instructions"] = {} for name, instr in self.pmap.instructions.items(): exp_dict["instructions"][name] = instr.asdict() exp_dict["model"] = self.pmap.model.asdict() exp_dict["generator"] = self.pmap.generator.asdict() exp_dict["options"] = { "propagate_batch_size": self.propagate_batch_size, "use_control_fields": self.use_control_fields, "overwrite_propagators": self.overwrite_propagators, "stop_partial_propagator_gradient": self.stop_partial_propagator_gradient, } return exp_dict
[docs] def __str__(self) -> str: return hjson.dumps(self.asdict(), default=hjson_encode)
[docs] def evaluate_legacy(self, sequences, psi_init: tf.Tensor = None): """ Compute the population values for a given sequence of operations. Parameters ---------- sequences: str list A list of control pulses/gates to perform on the device. psi_init: tf.Tensor A tensor containing the initial statevector Returns ------- list A list of populations """ model = self.pmap.model if psi_init is None: psi_init = model.tasks["init_ground"].initialise( model.drift_ham, model.lindbladian ) self.psi_init = psi_init populations = [] for sequence in sequences: psi_t = copy.deepcopy(self.psi_init) for gate in sequence: psi_t = tf.matmul(self.propagators[gate], psi_t) pops = self.populations(psi_t, model.lindbladian) populations.append(pops) return populations
[docs] def evaluate_qasm(self, sequences, psi_init: tf.Tensor = None): """ Compute the population values for a given sequence (in QASM format) of operations. Parameters ---------- sequences: dict list A list of control pulses/gates to perform on the device in QASM format. psi_init: tf.Tensor A tensor containing the initial statevector Returns ------- list A list of populations """ model = self.pmap.model if psi_init is None: if "init_ground" in model.tasks: psi_init = model.tasks["init_ground"].initialise( model.drift_ham, model.lindbladian ) else: psi_init = model.get_ground_state() self.psi_init = psi_init populations = [] for sequence in sequences: psi_t = copy.deepcopy(self.psi_init) for gate in sequence: psi_t = tf.matmul(self.lookup_gate(**gate), psi_t) pops = self.populations(psi_t, model.lindbladian) populations.append(pops) return populations
[docs] def lookup_gate(self, name, qubits, params=None) -> tf.constant: """ Returns a fixed operation or a parametric virtual Z gate. To be extended to general parametric gates. """ if name == "VZ": gate = tf.constant(self.get_VZ(qubits, params)) else: gate = self.propagators[name + str(qubits)] return gate
[docs] def get_VZ(self, target, params): """ Returns the appropriate Z-rotation. """ dims = self.pmap.model.dims return perfect_single_q_parametric_gate("Z", target[0], params[0], dims)
[docs] def process(self, populations, labels=None): """ Apply a readout procedure to a population vector. Very specialized at the moment. Parameters ---------- populations: list List of populations from evaluating. labels: list List of state labels specifying a subspace. Returns ------- list A list of processed populations. """ model = self.pmap.model populations_final = [] populations_no_rescale = [] for pops in populations: # TODO: Loop over all model.tasks in a general fashion # TODO: Selecting states by label in the case of computational space if "conf_matrix" in model.tasks: pops = model.tasks["conf_matrix"].confuse(pops) if labels is not None: pops_select = 0 for label in labels: pops_select += pops[model.comp_state_labels.index(label)] pops = pops_select else: pops = tf.reshape(pops, [pops.shape[0]]) else: if labels is not None: pops_select = 0 for label in labels: try: pops_select += pops[model.state_labels.index(label)] except ValueError: raise Exception( f"C3:ERROR:State {label} not defined. Available are:\n" f"{model.state_labels}" ) pops = pops_select else: pops = tf.reshape(pops, [pops.shape[0]]) if "meas_rescale" in model.tasks: populations_no_rescale.append(pops) pops = model.tasks["meas_rescale"].rescale(pops) populations_final.append(pops) return populations_final, populations_no_rescale
[docs] def get_perfect_gates(self, gate_keys: list = None) -> Dict[str, np.ndarray]: """Return a perfect gateset for the gate_keys. Parameters ---------- gate_keys: list (Optional) List of gates to evaluate. Returns ------- Dict[str, np.array] A dictionary of gate names and np.array representation of the corresponding unitary Raises ------ Exception Raise general exception for undefined gate """ instructions = self.pmap.instructions gates = {} dims = self.pmap.model.dims if gate_keys is None: gate_keys = instructions.keys() # type: ignore for gate in gate_keys: gates[gate] = instructions[gate].get_ideal_gate(dims) # TODO parametric gates return gates
[docs] def compute_states(self) -> Dict[Instruction, List[tf.Tensor]]: """Employ a state solver to compute the trajectory of the system. Returns ------- List[tf.tensor] List of states of the system from simulation. """ model = self.pmap.model generator = self.pmap.generator instructions = self.pmap.instructions states = {} gate_ids = self.opt_gates if gate_ids is None: gate_ids = instructions.keys() for gate in gate_ids: try: instr = instructions[gate] except KeyError: raise Exception( f"C3:Error: Gate '{gate}' is not defined." f" Available gates are:\n {list(instructions.keys())}." ) signal = generator.generate_signals(instr) result = self.propagation(model, signal) states[instr] = result["states"] self.states = states return result
[docs] def compute_propagators(self): """ Compute the unitary representation of operations. If no operations are specified in self.opt_gates the complete gateset is computed. Returns ------- dict A dictionary of gate names and their unitary representation. """ model = self.pmap.model generator = self.pmap.generator instructions = self.pmap.instructions propagators = {} partial_propagators = {} gate_ids = self.opt_gates if gate_ids is None: gate_ids = instructions.keys() for gate in gate_ids: try: instr = instructions[gate] except KeyError: raise Exception( f"C3:Error: Gate '{gate}' is not defined." f" Available gates are:\n {list(instructions.keys())}." ) model.controllability = self.use_control_fields result = self.propagation(model, generator, instr) U = result["U"] dUs = result["dUs"] self.ts = result["ts"] if model.use_FR: # TODO change LO freq to at the level of a line freqs = {} framechanges = {} for line, ctrls in instr.comps.items(): # TODO calculate properly the average frequency that each qubit sees offset = 0.0 for ctrl in ctrls.values(): if "freq_offset" in ctrl.params.keys(): if ctrl.params["amp"] != 0.0: offset = ctrl.params["freq_offset"].get_value() freqs[line] = tf.cast( ctrls["carrier"].params["freq"].get_value() + offset, tf.complex128, ) framechanges[line] = tf.cast( ctrls["carrier"].params["framechange"].get_value(), tf.complex128, ) t_final = tf.constant(instr.t_end - instr.t_start, dtype=tf.complex128) FR = model.get_Frame_Rotation(t_final, freqs, framechanges) if model.lindbladian: SFR = tf_super(FR) U = tf.matmul(SFR, U) self.FR = SFR else: U = tf.matmul(FR, U) self.FR = FR if model.dephasing_strength != 0.0: if not model.lindbladian: raise ValueError("Dephasing can only be added when lindblad is on.") else: amps = {} for line, ctrls in instr.comps.items(): amp, sum = generator.devices["awg"].get_average_amp() amps[line] = tf.cast(amp, tf.complex128) t_final = tf.constant( instr.t_end - instr.t_start, dtype=tf.complex128 ) dephasing_channel = model.get_dephasing_channel(t_final, amps) U = tf.matmul(dephasing_channel, U) propagators[gate] = U partial_propagators[gate] = dUs # TODO we might want to move storing of the propagators to the instruction object if self.overwrite_propagators: self.propagators = propagators self.partial_propagators = partial_propagators else: self.propagators.update(propagators) self.partial_propagators.update(partial_propagators) self.compute_propagators_timestamp = time.time() return propagators
[docs] def set_opt_gates(self, gates): """ Specify a selection of gates to be computed. Parameters ---------- gates: Identifiers of the gates of interest. Can contain duplicates. """ if type(gates) is str: gates = [gates] self.opt_gates = gates
[docs] def set_opt_gates_seq(self, seqs): """ Specify a selection of gates to be computed. Parameters ---------- seqs: Identifiers of the sequences of interest. Can contain duplicates. """ self.opt_gates = list(set(itertools.chain.from_iterable(seqs)))
[docs] def set_enable_store_unitaries(self, flag, logdir, exist_ok=False): """ Saving of unitary propagators. Parameters ---------- flag: boolean Enable or disable saving. logdir: str File path location for the resulting unitaries. """ self.enable_store_unitaries = flag self.logdir = logdir if self.enable_store_unitaries: os.makedirs(self.logdir + "unitaries/", exist_ok=exist_ok) self.store_unitaries_counter = 0
[docs] def store_Udict(self, goal): """ Save unitary as text and pickle. Parameter --------- goal: tf.float64 Value of the goal function, if used. """ folder = ( self.logdir + "unitaries/eval_" + str(self.store_unitaries_counter) + "_" + str(goal) + "/" ) if not os.path.exists(folder): os.mkdir(folder) with open(folder + "Us.pickle", "wb+") as file: pickle.dump(self.propagators, file) for key, value in self.propagators.items(): # Windows is not able to parse ":" as file path np.savetxt(folder + key.replace(":", ".") + ".txt", value)
[docs] def populations(self, state, lindbladian): """ Compute populations from a state or density vector. Parameters ---------- state: tf.Tensor State or densitiy vector. lindbladian: boolean Specify if conversion to density matrix is needed. Returns ------- tf.Tensor Vector of populations. """ if lindbladian: rho = tf_vec_to_dm(state) pops = tf.math.real(tf.linalg.diag_part(rho)) return tf.reshape(pops, shape=[pops.shape[0], 1]) else: return tf.abs(state) ** 2
[docs] def expect_oper(self, state, lindbladian, oper): if lindbladian: rho = tf_vec_to_dm(state) else: rho = tf_state_to_dm(state) trace = np.trace(np.matmul(rho, oper)) return [[np.real(trace)]] # ,[np.imag(trace)]]