tensorflow_quantum/python/layers/high_level/noisy_pqc.py [141:207]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - differentiator=None, initializer=tf.keras.initializers.RandomUniform(0, 2 * np.pi), regularizer=None, constraint=None, **kwargs, ): """Instantiate this layer. Create a layer that will output noisy expectation values of the given operators when fed quantum data to it's input layer. This layer will accept one input tensor representing a quantum data source (these circuits must not contain any symbols) and append the model_circuit to them, execute them and then finally output the expectation values. model_circuit: `cirq.Circuit` containing `sympy.Symbols` that will be used as the model which will be fed quantum data inputs. operators: `cirq.PauliSum` or Python `list` of `cirq.PauliSum` objects used as observables at the end of the model circuit. repetitions: Python `int` indicating how many trajectories to use when estimating expectation values. sample_based: Python `bool` indicating whether to use sampling to estimate expectations or analytic calculations with each trajectory. differentiator: Optional `tfq.differentiator` object to specify how gradients of `model_circuit` should be calculated. initializer: Optional `tf.keras.initializer` object to specify how the symbols in `model_circuit` should be initialized when creating the managed variables. regularizer: Optional `tf.keras.regularizer` object applied to the managed variables parameterizing `model_circuit`. constraint: Optional `tf.keras.constraint` object applied to the managed variables parameterizing `model_circuit`. """ super().__init__(**kwargs) # Ingest model_circuit. if not isinstance(model_circuit, cirq.Circuit): raise TypeError("model_circuit must be a cirq.Circuit object." " Given: {}".format(model_circuit)) self._symbols_list = list( sorted(util.get_circuit_symbols(model_circuit))) self._symbols = tf.constant([str(x) for x in self._symbols_list]) self._model_circuit = util.convert_to_tensor([model_circuit]) if len(self._symbols_list) == 0: raise ValueError("model_circuit has no sympy.Symbols. Please " "provide a circuit that contains symbols so " "that their values can be trained.") # Ingest operators. if isinstance(operators, (cirq.PauliString, cirq.PauliSum)): operators = [operators] if not isinstance(operators, (list, np.ndarray, tuple)): raise TypeError("operators must be a cirq.PauliSum or " "cirq.PauliString, or a list, tuple, " "or np.array containing them. " "Got {}.".format(type(operators))) if not all([ isinstance(op, (cirq.PauliString, cirq.PauliSum)) for op in operators ]): raise TypeError("Each element in operators to measure " "must be a cirq.PauliString" " or cirq.PauliSum") self._operators = util.convert_to_tensor([operators]) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tensorflow_quantum/python/layers/high_level/pqc.py [140:211]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - differentiator=None, initializer=tf.keras.initializers.RandomUniform(0, 2 * np.pi), regularizer=None, constraint=None, **kwargs, ): """Instantiate this layer. Create a layer that will output expectation values of the given operators when fed quantum data to it's input layer. This layer will accept one input tensor representing a quantum data source (these circuits must not contain any symbols) and append the model_circuit to them, execute them and then finally output the expectation values. model_circuit: `cirq.Circuit` containing `sympy.Symbols` that will be used as the model which will be fed quantum data inputs. operators: `cirq.PauliSum` or Python `list` of `cirq.PauliSum` objects used as observables at the end of the model circuit. repetitions: Optional Python `int` indicating how many samples to use when estimating expectation values. If `None` analytic expectation calculation is used. backend: Optional Backend to use to simulate states. Defaults to the noiseless TensorFlow simulator, however users may also specify a preconfigured cirq simulation object to use instead. If a cirq object is given it must inherit either `cirq.sim.simulator.SimulatesExpectationValues` if analytic expectations are desired or `cirq.Sampler` if sampled expectations are desired. differentiator: Optional `tfq.differentiator` object to specify how gradients of `model_circuit` should be calculated. initializer: Optional `tf.keras.initializer` object to specify how the symbols in `model_circuit` should be initialized when creating the managed variables. regularizer: Optional `tf.keras.regularizer` object applied to the managed variables parameterizing `model_circuit`. constraint: Optional `tf.keras.constraint` object applied to the managed variables parameterizing `model_circuit`. """ super().__init__(**kwargs) # Ingest model_circuit. if not isinstance(model_circuit, cirq.Circuit): raise TypeError("model_circuit must be a cirq.Circuit object." " Given: {}".format(model_circuit)) self._symbols_list = list( sorted(util.get_circuit_symbols(model_circuit))) self._symbols = tf.constant([str(x) for x in self._symbols_list]) self._model_circuit = util.convert_to_tensor([model_circuit]) if len(self._symbols_list) == 0: raise ValueError("model_circuit has no sympy.Symbols. Please " "provide a circuit that contains symbols so " "that their values can be trained.") # Ingest operators. if isinstance(operators, (cirq.PauliString, cirq.PauliSum)): operators = [operators] if not isinstance(operators, (list, np.ndarray, tuple)): raise TypeError("operators must be a cirq.PauliSum or " "cirq.PauliString, or a list, tuple, " "or np.array containing them. " "Got {}.".format(type(operators))) if not all([ isinstance(op, (cirq.PauliString, cirq.PauliSum)) for op in operators ]): raise TypeError("Each element in operators to measure " "must be a cirq.PauliString" " or cirq.PauliSum") self._operators = util.convert_to_tensor([operators]) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -