# # Copyright 2021 Keysight Technologies Inc. # """ Writing Custom Noise Sources ============================ """ #%% # This example will demonstrate how to write a custom noise source for the simulator. # See also :doc:`this guide<../../guides/run/simulator>` for more general information. import numpy as np import trueq as tq import trueq.simulation as tqs #%% # A custom noise source can be implemented by subclassing # :py:class:`~trueq.simulation.noise_source.NoiseSource` and overriding its # :py:meth:`~trueq.simulation.noise_source.NoiseSource.apply` method. # # The :py:meth:`~trueq.simulation.noise_source.NoiseSource.apply` method will be passed # three arguments: ``cycle_wrapper``, ``state``, and ``circuit_cache``. The job of apply # is to mutate ``state`` based on the contents of ``cycle_wrapper``. The ``state`` # refers to "the state of simulation" and can either be a # :py:class:`~trueq.math.tensor.StateTensor` (which can store either a pure state or a # mixed state), or a :py:class:`~trueq.math.tensor.OperatorTensor` (which can store # either a unitary or a superoperator). Each of these four possibilities share the # method :py:meth:`~trueq.math.tensor.Tensor.apply_matrix` which can always accept a # unitary or superoperator as input, and for example upgrades pure states to mixed # states when necessary, and so the distinction between these various types of states # becomes irrelevant to the implementation of a noise source. # # Note that ``cycle_wrapper`` is not a :py:class:`~trueq.Cycle` instance as one # might expect, but instead a thin wrapper called # :py:class:`~trueq.simulation.match.CycleWrapper` which in turn wraps each cycle # operation as :py:class:`~trueq.simulation.match.OpWrapper`\. # # These wrapper types are required to store metadata on each operation (also discussed # further below). However, noise sources will rarely need to deal with these wrapper # objects directly. This is because noise sources (by strongly encouraged convention, # though not by contract) own a :py:class:`~trueq.simulation.match.Match` instance which # takes ownership of the mechanics of these wrappers. This is seen in the example below, # where the :py:class:`~trueq.simulation.match.Match.iter_gates` method is used to yield # gate objects with their corresponding gate labels. # predefine noise matrices to be applied to qubits following ideal gates # note that rowstack_subsys is the superoperator convention required by the simulator p = 0.01 s = tq.math.Superop.from_kraus([np.sqrt(1 - p) * np.eye(2), np.sqrt(p) * tq.Gate.x.mat]) s1 = s.rowstack_subsys p = 0.02 s = tq.math.Superop.from_kraus( [np.sqrt(1 - p) * np.eye(4), np.sqrt(p) * tq.math.random_unitary(4)] ) s2 = s.rowstack_subsys class ExampleNoise(tqs.NoiseSource): def __init__(self, match=None): # this simulator hardcodes the subsystem dimension to 2 super().__init__(dim=2, match=match) def make_circuit_cache(self, circuit): # this return will be made available to apply() as circuit_cache for every # cycle in the circuit return set(circuit.labels) def apply(self, cycle_wrappers, state, circuit_cache): # in this method we need to mutate state by inspecting the latest cycle_wrapper used_labels = set() # loop through the gates in the cycle and mutate the state # note that we must set noise_only=False since we are simulating each gate for labels, gate in self.match.iter_gates(cycle_wrappers, noise_only=False): used_labels.update(labels) # first do ideal simulation of this gate state.apply_matrix(labels, gate.mat) # now add some noise to each qubit the gate acts on if len(labels) == 1: state.apply_matrix(labels, s1) elif len(labels) == 2: state.apply_matrix(labels, s2) # apply a 20 degree Z rotation to every qubit without a gate in this cycle for label in circuit_cache.difference(used_labels): state.apply_matrix((label,), tq.Gate.rp("Z", 20)) # %% # Now we can instantiate a simulator that uses this noise source and do any simulator # calculation. Here, we use the simulator to predict the output of a hypothetical # KNR experiment. sim = tq.Simulator().append_noise_source(ExampleNoise()) cycle = {(0, 1): tq.Gate.cnot, 2: tq.Gate.x} fit = sim.predict_knr(cycle, twirl=tq.Twirl("P", range(5))) fit.plot.knr_heatmap() # %% # Caching # ------- # # There are two distinct caches available to a noise source. The first is a private # noise source attribute that is called ``_cache`` (by convention, not by contract) in # built-in noise models which persists throughout the lifetime of the noise source. For # example, a gate-dependent noise model may consider caching gate noise if it is # expensive to recompute everytime the same gate is encountered. # # The second cache comes as the third argument to the apply # method. This cache is instantiated by # :py:meth:`~trueq.simulation.noise_source.NoiseSource.make_circuit_cache` just before a # new circuit is simulated and is presented as the third argument to the apply method # for every cycle within that circuit. It typically stores information about the # circuit that is not available in every cycle, such as all of the labels the circuit # acts on, or which measurements it performs at the end. # %% # Wrappers and metadata # --------------------- # # The variable ``cycle_wrapper`` in the example above has type # :py:class:`~trueq.simulation.match.CycleWrapper` which is a thin wrapper for a cycle # and also wraps every cycle operation with # :py:class:`~trueq.simulation.match.OpWrapper`\. During simulation, everytime a new # cycle is encountered it is wrapped once, and the same wrapped cycle instance is passed # to all noise sources. The cycle wrapper exists to enable efficient looping logic used # by :py:class:`~trueq.simulation.match.Match` and to persist operation wrappers for # multiple noise sources\. The operation wrapper exists to store # two important pieces of information: # # 1. Whether some noise source has previously simulated the same operation of the # cycle. This is stored as a boolean called ``has_been_simulated`` and is set to # true whenever a match method is called with the argument ``noise_only=False``, as # it is in the above example. # 2. Whether no noise sources (except the ideal final simulation if relevant) should # touch the operation again. This is stored as a boolean called ``no_more_noise`` # and is set to true whenever it is yielded by a match method whose ``exclusive`` # value is true.